\n UNEP-WCMC, WorldFish Centre, WRI, TNC (2021). Global distribution of warm-water coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 4.1. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001). Cambridge (UK): UN Environment World Conservation Monitoring Centre. Data DOI: https://doi.org/10.34892/t2wk-5t34."
},
"public-costa-rica-fishing-effort": {
"name": "Costa Rica VMS",
"description": "Vessel monitoring system (VMS) data is provided by the Costa Rican Fisheries and Aquaculture Institute. Data is collected using Ecuador's vessel monitoring system via satellites and is published on a three-day delay containing information on vessels’ location, speed, course, and movement. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques",
"schema": {
"fleet": {
"keyword": "fleet",
"enum": {
"costarica_vms_industrial_longline": "costarica_vms_industrial_longline",
"costarica_vms_atuneros": "costarica_vms_atuneros",
"costarica_vms_sardineros": "costarica_vms_sardineros"
}
},
"speed": "speed",
"bearing": "bearing"
}
},
"public-costa-rica-fishing-vessels": {
"name": "Costa Rica VMS",
"description": "Dataset for VMS Costa Rica (Public)",
"schema": {
"source": "source"
}
},
"public-costa-rica-vessel-identity-vessels": {
"name": "Costa Rica VMS",
"description": "Dataset for VMS Costa Rica (Public)"
},
"public-dgg_sar_caribe_match_2019": {
"name": "SAR with an AIS-matched vessel",
"description": "Vessels identified using synthetic aperture radar (SAR) and matched to a vessel using automatic identification system (AIS) data. Currently available for 2019. Source: Sentinel-1."
},
"public-dgg_sar_caribe_not_match_2019": {
"name": "SAR without an AIS-matched vessel",
"description": "Vessels identified using synthetic aperture radar (SAR) without a known match to any vessel recorded using automatic identification system (AIS) data. Currently available for 2019. Source: Sentinel-1."
},
"public-ecuador-fishing-effort": {
"name": "Ecuador VMS",
"description": "Vessel monitoring system (VMS) data is provided by the National Directorate of Aquatic Spaces of the Ecuadorian Navy. Data is collected using Ecuador's vessel monitoring system via satellites and is published on a seven-day delay containing information on vessels’ identity, location, speed, course, and movement. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques.",
"schema": {
"speed": "speed",
"bearing": "bearing",
"shiptype": {
"keyword": "shiptype",
"enum": {
"fishing": "fishing"
}
}
}
},
"public-ecuador-fishing-vessels": {
"name": "Ecuador VMS (Fishing vessels)",
"description": "Dataset for VMS Ecuador (Public)",
"schema": {
"source": "source"
}
},
"public-ecuador-non-fishing-vessels": {
"name": "Ecuador VMS (Non fishing vessels)",
"description": "Dataset for VMS Ecuador (Public)",
"schema": {
"source": "source"
}
},
"public-ecuador-presence": {
"name": "Ecuador VMS",
"description": "Vessel monitoring system (VMS) data is provided by the National Directorate of Aquatic Spaces of the Ecuadorian Navy. Data is collected using Ecuador's vessel monitoring system via satellites and is published on a seven-day delay containing information on vessels’ identity, location, speed, course, and movement. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques.",
"schema": {
"speed": "speed",
"bearing": "bearing",
"shiptype": {
"keyword": "shiptype",
"enum": {
"fishing": "fishing",
"international traffic": "international traffic",
"national traffic": "national traffic",
"tug": "tug",
"boat": "boat",
"auxiliary": "auxiliary"
}
}
}
},
"public-ecuador-vessel-identity-fishing": {
"name": "Ecuador VMS (Fishing vessels)",
"description": "VMS Ecuador (Fishing vessels)"
},
"public-ecuador-vessel-identity-non-fishing": {
"name": "Ecuador VMS (Non fishing vessels)",
"description": "VMS Ecuador (Non fishing vessels)"
},
"public-eez-areas": {
"name": "EEZs",
"description": "Exclusive economic zones (EEZs) extend up to 200 nautical miles from a country's coast. Flanders Marine Institute (2019). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Source: marineregions.org."
},
"public-eez-areas-12nm": {
"name": "EEZs 12nm",
"description": "Territorial seas are a belt of coastal waters extending at most 12 nautical miles (22.2 km; 13.8 mi) from the baseline (usually the mean low-water mark) of a coastal state.The territorial seas (and EEZs) lines are generally drawn from straight line baselines and these baselines are defined by each coastal state following UNCLOS rules See article 7. Waters inside the baseline are considered \"internal waters\" (See article 8) Citation: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Territorial Seas (12NM), version 4. Available online at https://www.marineregions.org/. https://doi.org/10.14284/633"
},
"public-eez-boundaries": {
"name": "EEZ boundaries",
"description": "EEZs boundaries are shown as solid lines for '200 NM', 'Treaty', 'Median line', 'Joint regime', 'Connection Line', 'Unilateral claim (undisputed)' and dashed lines for 'Joint regime', 'Unsettled', 'Unsettled median line' based on the 'LINE_TYPE' field. Flanders Marine Institute (2019). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Source: marineregions.org. See more detailed metadata information for this layer"
},
"public-eez-land": {
"name": "EEZs",
"description": "Flanders Marine Institute (2019). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Source: marineregions.org"
},
"public-fao": {
"name": "FAO",
"description": "FAO Major Fishing Areas for Statistical Purposes are arbitrary areas, the boundaries of which were determined in consultation with international fishery agencies on various considerations, including (i) the boundary of natural regions and the natural divisions of oceans and seas; (ii) the boundaries of adjacent statistical fisheries bodies already established in inter-governmental conventions and treaties; (iii) existing national practices; (iv) national boundaries; (v) the longitude and latitude grid system; (vi) the distribution of the aquatic fauna; and (vii) the distribution of the resources and the environmental conditions within an area."
},
"public-fao-areas-major-subareas": {
"name": "Fao areas (major & subareas)",
"description": "FAO major fishing areas for statistical purposes are arbitrary areas, the boundaries of which were determined in consultation with international fishery agencies. The major fishing areas, inland and marine, are listed below by two-digit codes and their names. To access maps and description of boundaries of each fishing area click on the relevant item in the list below or in the map showing the 19 major marine fishing areas. Source. See more detailed metadata information for this layer"
},
"public-fao-major": {
"name": "FAO major fishing areas",
"description": "FAO major fishing areas for statistical purposes are arbitrary areas, the boundaries of which were determined in consultation with international fishery agencies. The major fishing areas, inland and marine, are listed below by two-digit codes and their names. To access maps and description of boundaries of each fishing area click on the relevant item in the list below or in the map showing the 19 major marine fishing areas. Source. See more detailed metadata information for this layer",
"schema": {
"FID": "FID",
"F_CODE": "F_CODE"
}
},
"public-fisheries-restricted-areas": {
"name": "Fisheries Restricted Areas",
"description": ""
},
"public-fixed-infrastructure": {
"name": "Fixed infrastructure (SAR, Optical)",
"description": "Overview
\nSatellite synthetic aperture radar (SAR) is a spaceborne radar imaging system that can detect at-sea vessels and infrastructure in any weather conditions. Microwave pulses are transmitted by a satellite-based antenna towards the Earth surface. The microwave energy scattered back to the spacecraft is then measured and integrated to form a “backscatter” image. The SAR image contains rich information about the different objects on the water, such as their size, orientation and texture. SAR imaging systems overcome most weather conditions and illumination levels, including clouds or rain due to the cloud penetrating property of microwaves, and daylight or darkness due to radar being an “active” sensor (it shoots and records back its own energy). SAR gives an advantage over some other “passive” satellite sensors, such as electro-optical imagery, consisting of a satellite-based camera recording the sunlight/infrared radiation reflected from/emitted by objects on the ground. This latter method can be confounded by cloud cover, haze, weather events and seasonal darkness at high latitudes.
\nGlobal Fishing Watch use an algorithm to detect “anomalies” or bright pixels scene in satellite images, and then use a machine learning model to exclude vessels and determine the probability that it’s a known class of infrastructure: wind infrastructure, oil infrastructure, other infrastructure, and noise.
\nUse cases
\n\n - Maritime domain awareness
\n \n - Infrastructure locations can support maritime domain awareness, and understanding of other activities occurring at sea.
\n - Infrastructure data supports assessments of ocean industrialization, facilitating monitoring of areas experiencing build-up or new development
\n
\n - Monitoring vessels
\n \n - Infrastructure locations can be used to analyse the behaviour of vessels associated with infrastructure, including grouping vessels based on their interaction with oil and wind structures.
\n - Interactions between vessels and infrastructure can help quantify the resources required to support offshore industrial activity
\n - The impacts of infrastructure on fishing, including attracting or deterring fishing, can be analyzed.
\n
\n - Marine protected areas (MPAs) and marine spatial planning
\n \n - During the planning stage in the designation of new protected areas, knowing the location of existing infrastructure will be vital to understand which stakeholders shall be included in the consultation process, to understand potential conflicts, and identify easy wins.
\n
\n - Environmental impacts
\n \n - Infrastructure locations can be used to help detect marine pollution events, and to differentiate between types of pollution events (e.g. pollution from vessels versus pollution from platforms)
\n
\n
\nLimitations
\n\n - Sentinel-1 SAR data does not sample most of the open ocean.
\n \n - Sentinel-1 does not sample most of the open ocean. However, the vast majority of industrial activity is close to shore. Also, farther from shore, more fishing vessels use AIS (60-90%), far more than the average for all fishing vessels (about 25%). Thus, for most of the world, our detections data complemented by AIS will capture the vast majority of human activity in the global ocean.
\n
\n - False positives can be produced from noise artifacts.
\n - We do not provide detections of vessels close to shore as it’s difficult to accurately map where the shoreline begins.
\n \n - We do not classify objects within 1 km of shore because of ambiguous coastlines and rocks. Nor do we classify objects in much of the Arctic and Antarctic, where sea ice can create too many false positives; in both regions, however, vessel traffic is either very low (Antarctic) or in countries that have a high adoption of AIS (northern European or northern North American countries). The bulk of industrial activities occurs several kilometers from shore, such as fishing along the continental shelf break, ocean transport over shipping lanes, and offshore development with medium-to-large oil rigs and wind farms. Also, much of the vessel activity within 1 km of shore is by smaller boats such as pleasure crafts.
\n
\n - Vessel detection by SAR imagery is limited primarily by the resolution of the images (~20 m in the case of Sentinel-1 IW GRD products).
\n \n - As a result, we miss most vessels under 15 m in length, although an object smaller than a pixel can still be seen if it is a strong reflector, such as a vessel made of metal rather than wood or fiberglass. Especially for smaller vessels (< 25 m), detection also depends on wind speed and the state of the ocean, as a rougher sea surface will produce higher backscatter, making it difficult to separate a small target from the sea clutter. Conversely, the higher the radar incidence angle, the higher the probability of detection, as less backscatter from the background will be received by the antenna. The vessel orientation relative to the satellite antenna also matters, as a vessel perpendicular to the radar line of sight will have a larger backscatter cross section, increasing the probability of being detected.
\n
\n - Vessel length estimates are limited by the quality of ground truth data
\n \n - Although we selected only high-confidence AIS-SAR matches to construct our training data, we found that some AIS records contained an incorrectly reported length. These errors, however, resulted in only a small fraction of imprecise training labels, and deep learning models can accommodate some noise in the training data.
\n
\n - Not all geographies are covered equally
\n \n - Our fishing classification may be less accurate in certain regions. In areas of high traffic from pleasure crafts and other service boats, such as near cities in some countries and in the fjords of Norway and Iceland, some of these smaller craft might be misclassified as fishing vessels. Conversely, some misclasification of fishing vessels as non-fishing vessels is expected in areas where all activity is not publicly shared. More importantly, however, is that many industrial fishing vessels are between 10 and 20 meters in length, and the accuracy (termed “recall”) of our model falls off quickly within these lengths. As a result, the total number of industrial fishing vessels is likely significantly higher than what we detect. Because our model uses vessel length from SAR, it may be possible to use methods similar to those in Kroodsma et al. (2022) to estimate the number of missing vessels. Future work can address this challenge.
\n - Our data likely underestimates the concentration of fishing in some regions, where we see areas of vessel activity being \"cut off\" by the edge of the Sentinel-1 footprint. We miss very small vessels (e.g., most artisanal fishing) that are less likely to carry AIS devices. Algorithmic improvements can capture the first kilometer from shore, and the inclusion of more SAR satellites in the coming years (two more ESA Sentinel-1 satellites and NASA's NISAR mission) will allow us to apply this method more broadly to build on this map and capture all activity at sea.
\n
\n
\nMethods
\nSAR imagery
\nWe use SAR imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA) [1]. The images are sourced from two satellites (S1A and S1B up until December 2021 when S1B stopped operating, and S1A only from 2022 onward) that orbit 180 degrees out of phase with each other in a polar, sun-synchronous orbit. Each satellite has a repeat-cycle of 12 days, so that together they provide a global mapping of coastal waters around the world approximately every six days for the period that both were operating. The number of images per location, however, varies greatly depending on mission priorities, latitude, and degree of overlap between adjacent satellite passes. Spatial coverage also varies over time [2]. Our data consist of dual-polarization images (VH and VV) from the Interferometric Wide (IW) swath mode, with a resolution of about 20 m.
\n[1]\n \n https://sedas.satapps.org/wp-content/uploads/2015/07/Sentinel-1_User_Handbook.pdf\n \n
\n[2]\n \n https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/observation-scenario\n \n
\nInfrastructure detection by SAR
\nDetecting infrastructure with SAR is based on an algorithm known as Constant False Alarm Rate (CFAR), a threshold algorithm used for anomaly detection in radar imagery. This algorithm is designed to search for pixel values that are unusually bright (the targets) compared to those in the surrounding area (the sea clutter). This method sets a threshold based on the pixel values of the local background (within a window), scanning the whole image pixel-by-pixel. Pixel values above the threshold constitute an anomaly and are likely to be samples from a detection.
\nInfrastructure classification
\nTo classify every detected offshore infrastructure, we use a machine learning model and designed a ConvNet based on the ConvNeXt architecture. A novel aspect of our machine learning classification approach is the combination of SAR imagery from Sentinel-1 with optical imagery from Sentinel-2. From six-month composites of dual-band SAR (VH and VV) and four-band optical (RGB and NIR) images, we extracted small tiles for every detected fixed infrastructure, with the respective objects at the center of the tile. A single model output includes the probabilities for the specified classes: wind infrastructure, oil infrastructure, other infrastructure, and noise.
\nFiltering
\nGlobal Fishing Watch has post-processed the SAR detections to reduce noise (false positives), remove vessels, and exclude areas with sea ice at high latitudes. We used a clustering approach to identify detections across time (within a 50m radius) that were likely the same structure but their coordinates differed slightly, and assigned them the most common predicted label of the cluster. We also filled in gaps for fixed structures that were missing in one timestep but detected in the previous and following timesteps, and dropped detections appearing in a single timestep.
\nResources, code and other notes
\nAll code developed in this study for SAR detection, machine learning models, and analyses is open source and freely available at\n \n https://github.com/GlobalFishingWatch/paper-industrial-activity\n .\n
\nSources data and citations
\nAll vessel and infrastructure data are freely available through the Global Fishing Watch data portal at\n \n https://globalfishingwatch.org\n . All data to reproduce this study can be downloaded from\n \n https://doi.org/10.6084/m9.figshare.24309475 / https://doi.org/10.5281/zenodo.8256932\n \n (statistical analyses and figures) and\n \n https://doi.org/10.6084/m9.figshare.24309469\n \n /\n \n https://doi.org/10.5281/zenodo.8258124\n \n (model training and evaluation).\n
\nLicense
\nNon-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.
",
"schema": {
"label": {
"keyword": "label",
"enum": {
"oil": "Oil",
"wind": "Wind",
"unknown": "Unknown"
}
}
}
},
"public-fixed-infrastructure-filtered-v1.1": {
"name": "Offshore Fixed Infrastructure (SAR, Optical)",
"description": "Overview
Offshore fixed infrastructure is a global dataset that uses AI and machine learning to detect and classify structures throughout the world’s oceans.
Classification labels (oil, wind, and unknown) are provided, as well as confidence levels (high, medium, or low) reflecting our certainty in the assigned label. Detections can be filtered and colored on the map using both label and confidence level.The data is updated on a monthly basis, and new classified detections are added at the beginning of every month. Viewing change using the timebar is simple, and allows anyone to recognize the rapid industrialization of the world’s oceans. For example, you can easily observe the expansion of wind farms in the North and East China Seas, or changes in oil infrastructure in the Gulf of Mexico or Persian Gulf.
By overlaying the existing map layers, you can explore how vessels interact with oil and wind structures, visualise the density of synthetic aperture radar (SAR) and Visible Infrared Imaging Radiometer Suite (VIIRS) vessel detections around infrastructure, or determine which marine protected areas (MPAs) contain wind, oil, or other infrastructure types. These are only examples of the types of questions we can now ask. Offshore fixed infrastructure is a first of its kind dataset that not only brings to light the extensive industrialization of our oceans, but enables users across industries to use this information in research, monitoring and management.
Use cases
- Maritime domain awareness
- Infrastructure locations can support maritime domain awareness, and understanding of other activities occurring at sea.
- Infrastructure data supports assessments of ocean industrialization, facilitating monitoring of areas experiencing build-up or new development
- Monitoring vessels
- Infrastructure locations can be used to analyse the behaviour of vessels associated with infrastructure, including grouping vessels based on their interaction with oil and wind structures.
- Interactions between vessels and infrastructure can help quantify the resources required to support offshore industrial activity
- The impacts of infrastructure on fishing, including attracting or deterring fishing, can be analysed.
- Marine protected areas (MPAs) and marine spatial planning
- During the planning stage in the designation of new protected areas, knowing the location of existing infrastructure will be vital to understand which stakeholders shall be included in the consultation process, to understand potential conflicts, and identify easy wins.
- Environmental impacts
- Infrastructure locations can be used to help detect marine pollution events, and to differentiate between types of pollution events (e.g. pollution from vessels versus pollution from platforms)
Caveats
- Sentinel-1 and Sentinel-2 satellites do not sample most of the open ocean.
- Most industrial activity happens relatively close to shore.
- The extent and frequency of SAR acquisitions is determined by the mission priorities.
- For more info see: https://www.nature.com/articles/s41586-023-06825-8/figures/5
- We do not provide detections of infrastructure within 1 km of shore
- We do not classify objects within 1 km of shore because it is difficult to map where the shoreline begins, and ambiguous coastlines and rocks cause false positives.
- The bulk of industrial activities, including offshore development with medium-to-large oil rigs and wind farms, occur several kilometers from shore.
- False positives can be produced from noise artifacts.
- Rocks, small islands, sea ice, radar ambiguities (radar echoes), and image artifacts can cause false positives
- Detections in some areas including Southern Chile, the Arctic, and the Norwegian Sea have been filtered to remove noise.
- Spatial coverage varies over time, which can produce different detections results year on year - Example
- Infrastructure detentions from 2017-01-01 to near real time are available, and updated on a monthly basis.
- Labels can change over time
- The label assigned to a structure is the greatest predicted label averaged across time. As we get more data, the label may change, and more accurately predict the true infrastructure type.
- Global datasets aren’t perfect
- We’ve done our best to create the most accurate product possible, but there will be infrastructure that isn’t detected, or has been classified incorrectly. This will be most evident when working at the project level.
- We strongly encourage users to provide feedback to the research team so that we may improve future versions of the model. All feedback is greatly appreciated.
Methods
SAR imagery
We use SAR imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA) [1]. The images are sourced from two satellites (S1A and S1B up until December 2021 when S1B stopped operating, and S1A only from 2022 onward) that orbit 180 degrees out of phase with each other in a polar, sun-synchronous orbit. Each satellite has a repeat-cycle of 12 days, so that together they provide a global mapping of coastal waters around the world approximately every six days for the period that both were operating. The number of images per location, however, varies greatly depending on mission priorities, latitude, and degree of overlap between adjacent satellite passes. Spatial coverage also varies over time [2]. Our data consist of dual-polarization images (VH and VV) from the Interferometric Wide (IW) swath mode, with a resolution of about 20 m.
[1] https://sedas.satapps.org/wp-content/uploads/2015/07/Sentinel-1_User_Handbook.pdf
[2] https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/observation-scenario
Infrastructure detection by SAR
Detecting infrastructure with SAR is based on the widely used Constant False Alarm Rate (CFAR) algorithm, an anomaly detection method conceived for detecting ships in synthetic aperture radar images, that has been modified to remove non-stationary objects. This algorithm is designed to search for pixel values that are unusually bright (the targets) compared to those in the surrounding area (the sea clutter). This method sets a threshold based on the pixel values of the local background (within a window), scanning the whole image pixel-by-pixel. Pixel values above the threshold constitute an anomaly and are likely to be samples from a target.
Infrastructure classification
To classify every detected offshore infrastructure, we used deep learning and designed a ConvNet based on the ConvNeXt architecture. A novel aspect of our deep learning classification approach is the combination of SAR imagery from Sentinel-1 with optical imagery from Sentinel-2. From six-month composites of dual-band SAR (VH and VV) and four-band optical (RGB and NIR) images, we extracted small tiles for every detected fixed infrastructure, with the respective objects at the center of the tile. A single model output includes the probabilities for the specified classes: wind, oil, unknown, lake maracaibo, and noise.
Filtering
GFW post-processed the classified SAR detections to reduce noise (false positives), remove vessels, exclude areas with sea ice at high latitudes, and incorporate expert feedback. We used a clustering approach to identify detections across time (within a 50 m radius) that were likely the same structure but their coordinates differed slightly, and assigned them the greatest average predicted label of the cluster. We also filled in gaps for fixed structures that were missing in one timestep but detected in the previous and following timesteps, and dropped detections appearing in a single timestep. Finally, the dataset underwent extensive manual review and editing by researchers and industry experts in order to refine the final product, and provide the most accurate dataset possible.
Data field descriptions
Each detection has a unique individual identifier (detection_id). A six-month image composite is used in the classification, therefore the detection_date represents the middle of the six month period. This helps to remove non-stationary objects (i.e. vessels), and avoid confusion in the model if a structure is being built, or there isn’t adequate imagery available. structure_id allows you to track a structure through time. There are therefore many detection_id (one for each month the structure is detected) for each structure_id. Labels of wind and oil represent any wind or oil related structure respectively. Unknown represents a structure that is not oil or wind related, such as bridges or navigational buoys.
Label confidence levels of ‘High’. ‘Medium’ and ‘Low’ are assigned to each structure, and are conditional on where the detections fell in relation to the boundaries of manually developed wind and oil polygons, and whether the label has changed from the previous month. The label_confidence field can be used to filter analysis.
Resources, code and other notes
Two repos are used in the automation process, both of which are private, and should not be shared publicly.
Detection and classification: https://github.com/GlobalFishingWatch/sentinel-1-ee/tree/master
Clustering and reclassification: https://github.com/GlobalFishingWatch/infrastructure-post-processing
All code developed for the paper, Paolo, F.S., Kroodsma, D., Raynor, J. et al. Satellite mapping reveals extensive industrial activity at sea. Nature 625, 85–91 (2024). https://doi.org/10.1038/s41586-023-06825-8, including SAR detection, deep learning models, and analyses is open source and freely available at https://github.com/GlobalFishingWatch/paper-industrial-activity.
Sources data and citations
Copernicus Sentinel data 2017-current
Lujala, Päivi; Jan Ketil Rød & Nadia Thieme, 2007. 'Fighting over Oil: Introducing A New Dataset', Conflict Management and Peace Science 24(3), 239-256
Sabbatino, M., Romeo, L., Baker, V., Bauer, J., Barkhurst, A., Bean, A., DiGiulio, J., Jones, K., Jones, T.J., Justman, D., Miller III, R., Rose, K., and Tong., A., Global Oil & Gas Infrastructure Features Database Geocube Collection, 2019-03-25, https://edx.netl.doe.gov/dataset/global-oil-gas-infrastructure-features-database-geocube-collection, DOI: 10.18141/1502839
License
Non-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.
Global Fishing Watch metadata
Infrastructure development methods should reference the paper:
Paolo, F.S., Kroodsma, D., Raynor, J. et al. Satellite mapping reveals extensive industrial activity at sea. Nature 625, 85–91 (2024). https://doi.org/10.1038/s41586-023-06825-8
All code developed for the paper, including SAR detection, deep learning models, and analyses is open source and freely available at https://github.com/GlobalFishingWatch/paper-industrial-activity. All the data generated and used by these scripts can reference the following data repos:
Analysis and Figures: https://doi.org/10.6084/m9.figshare.24309475
Training and Evaluation: https://doi.org/10.6084/m9.figshare.24309469
",
"schema": {
"label": {
"keyword": "Label",
"enum": {
"oil": "Oil",
"wind": "Wind",
"unknown": "Unknown"
}
},
"structure_id": "Structure ID",
"label_confidence": {
"keyword": "Confidence",
"enum": {
"high": "High",
"low": "Low",
"medium": "Medium"
}
},
"structure_end_date": "End date",
"structure_start_date": "Start date"
}
},
"public-fixed-infrastructure-filtered": {
"name": "Offshore Fixed Infrastructure (SAR, Optical)",
"description": "Overview
Offshore fixed infrastructure is a global dataset that uses AI and machine learning to detect and classify structures throughout the world’s oceans.
Classification labels (oil, wind, and unknown) are provided, as well as confidence levels (high, medium, or low) reflecting our certainty in the assigned label. Detections can be filtered and colored on the map using both label and confidence level.The data is updated on a monthly basis, and new classified detections are added at the beginning of every month. Viewing change using the timebar is simple, and allows anyone to recognize the rapid industrialization of the world’s oceans. For example, you can easily observe the expansion of wind farms in the North and East China Seas, or changes in oil infrastructure in the Gulf of Mexico or Persian Gulf.
By overlaying the existing map layers, you can explore how vessels interact with oil and wind structures, visualise the density of synthetic aperture radar (SAR) and Visible Infrared Imaging Radiometer Suite (VIIRS) vessel detections around infrastructure, or determine which marine protected areas (MPAs) contain wind, oil, or other infrastructure types. These are only examples of the types of questions we can now ask. Offshore fixed infrastructure is a first of its kind dataset that not only brings to light the extensive industrialization of our oceans, but enables users across industries to use this information in research, monitoring and management.
Use cases
- Maritime domain awareness
- Infrastructure locations can support maritime domain awareness, and understanding of other activities occurring at sea.
- Infrastructure data supports assessments of ocean industrialization, facilitating monitoring of areas experiencing build-up or new development
- Monitoring vessels
- Infrastructure locations can be used to analyse the behaviour of vessels associated with infrastructure, including grouping vessels based on their interaction with oil and wind structures.
- Interactions between vessels and infrastructure can help quantify the resources required to support offshore industrial activity
- The impacts of infrastructure on fishing, including attracting or deterring fishing, can be analysed.
- Marine protected areas (MPAs) and marine spatial planning
- During the planning stage in the designation of new protected areas, knowing the location of existing infrastructure will be vital to understand which stakeholders shall be included in the consultation process, to understand potential conflicts, and identify easy wins.
- Environmental impacts
- Infrastructure locations can be used to help detect marine pollution events, and to differentiate between types of pollution events (e.g. pollution from vessels versus pollution from platforms)
Caveats
- Sentinel-1 and Sentinel-2 satellites do not sample most of the open ocean.
- Most industrial activity happens relatively close to shore.
- The extent and frequency of SAR acquisitions is determined by the mission priorities.
- For more info see: https://www.nature.com/articles/s41586-023-06825-8/figures/5
- We do not provide detections of infrastructure within 1 km of shore
- We do not classify objects within 1 km of shore because it is difficult to map where the shoreline begins, and ambiguous coastlines and rocks cause false positives.
- The bulk of industrial activities, including offshore development with medium-to-large oil rigs and wind farms, occur several kilometers from shore.
- False positives can be produced from noise artifacts.
- Rocks, small islands, sea ice, radar ambiguities (radar echoes), and image artifacts can cause false positives
- Detections in some areas including Southern Chile, the Arctic, and the Norwegian Sea have been filtered to remove noise.
- Spatial coverage varies over time, which can produce different detections results year on year - Example
- Infrastructure detentions from 2017-01-01 to near real time are available, and updated on a monthly basis.
- Labels can change over time
- The label assigned to a structure is the greatest predicted label averaged across time. As we get more data, the label may change, and more accurately predict the true infrastructure type.
- Global datasets aren’t perfect
- We’ve done our best to create the most accurate product possible, but there will be infrastructure that isn’t detected, or has been classified incorrectly. This will be most evident when working at the project level.
- We strongly encourage users to provide feedback to the research team so that we may improve future versions of the model. All feedback is greatly appreciated.
Methods
SAR imagery
We use SAR imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA) [1]. The images are sourced from two satellites (S1A and S1B up until December 2021 when S1B stopped operating, and S1A only from 2022 onward) that orbit 180 degrees out of phase with each other in a polar, sun-synchronous orbit. Each satellite has a repeat-cycle of 12 days, so that together they provide a global mapping of coastal waters around the world approximately every six days for the period that both were operating. The number of images per location, however, varies greatly depending on mission priorities, latitude, and degree of overlap between adjacent satellite passes. Spatial coverage also varies over time [2]. Our data consist of dual-polarization images (VH and VV) from the Interferometric Wide (IW) swath mode, with a resolution of about 20 m.
[1] https://sedas.satapps.org/wp-content/uploads/2015/07/Sentinel-1_User_Handbook.pdf
[2] https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/observation-scenario
Infrastructure detection by SAR
Detecting infrastructure with SAR is based on the widely used Constant False Alarm Rate (CFAR) algorithm, an anomaly detection method conceived for detecting ships in synthetic aperture radar images, that has been modified to remove non-stationary objects. This algorithm is designed to search for pixel values that are unusually bright (the targets) compared to those in the surrounding area (the sea clutter). This method sets a threshold based on the pixel values of the local background (within a window), scanning the whole image pixel-by-pixel. Pixel values above the threshold constitute an anomaly and are likely to be samples from a target.
Infrastructure classification
To classify every detected offshore infrastructure, we used deep learning and designed a ConvNet based on the ConvNeXt architecture. A novel aspect of our deep learning classification approach is the combination of SAR imagery from Sentinel-1 with optical imagery from Sentinel-2. From six-month composites of dual-band SAR (VH and VV) and four-band optical (RGB and NIR) images, we extracted small tiles for every detected fixed infrastructure, with the respective objects at the center of the tile. A single model output includes the probabilities for the specified classes: wind, oil, unknown, lake maracaibo, and noise.
Filtering
GFW post-processed the classified SAR detections to reduce noise (false positives), remove vessels, exclude areas with sea ice at high latitudes, and incorporate expert feedback. We used a clustering approach to identify detections across time (within a 50 m radius) that were likely the same structure but their coordinates differed slightly, and assigned them the greatest average predicted label of the cluster. We also filled in gaps for fixed structures that were missing in one timestep but detected in the previous and following timesteps, and dropped detections appearing in a single timestep. Finally, the dataset underwent extensive manual review and editing by researchers and industry experts in order to refine the final product, and provide the most accurate dataset possible.
Data field descriptions
Each detection has a unique individual identifier (detection_id). A six-month image composite is used in the classification, therefore the detection_date represents the middle of the six month period. This helps to remove non-stationary objects (i.e. vessels), and avoid confusion in the model if a structure is being built, or there isn’t adequate imagery available. structure_id allows you to track a structure through time. There are therefore many detection_id (one for each month the structure is detected) for each structure_id. Labels of wind and oil represent any wind or oil related structure respectively. Unknown represents a structure that is not oil or wind related, such as bridges or navigational buoys.
Label confidence levels of ‘High’. ‘Medium’ and ‘Low’ are assigned to each structure, and are conditional on where the detections fell in relation to the boundaries of manually developed wind and oil polygons, and whether the label has changed from the previous month. The label_confidence field can be used to filter analysis.
Resources, code and other notes
Two repos are used in the automation process, both of which are private, and should not be shared publicly.
Detection and classification: https://github.com/GlobalFishingWatch/sentinel-1-ee/tree/master
Clustering and reclassification: https://github.com/GlobalFishingWatch/infrastructure-post-processing
All code developed for the paper, Paolo, F.S., Kroodsma, D., Raynor, J. et al. Satellite mapping reveals extensive industrial activity at sea. Nature 625, 85–91 (2024). https://doi.org/10.1038/s41586-023-06825-8, including SAR detection, deep learning models, and analyses is open source and freely available at https://github.com/GlobalFishingWatch/paper-industrial-activity.
Sources data and citations
Copernicus Sentinel data 2017-current
Lujala, Päivi; Jan Ketil Rød & Nadia Thieme, 2007. 'Fighting over Oil: Introducing A New Dataset', Conflict Management and Peace Science 24(3), 239-256
Sabbatino, M., Romeo, L., Baker, V., Bauer, J., Barkhurst, A., Bean, A., DiGiulio, J., Jones, K., Jones, T.J., Justman, D., Miller III, R., Rose, K., and Tong., A., Global Oil & Gas Infrastructure Features Database Geocube Collection, 2019-03-25, https://edx.netl.doe.gov/dataset/global-oil-gas-infrastructure-features-database-geocube-collection, DOI: 10.18141/1502839
License
Non-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.
Global Fishing Watch metadata
Infrastructure development methods should reference the paper:
Paolo, F.S., Kroodsma, D., Raynor, J. et al. Satellite mapping reveals extensive industrial activity at sea. Nature 625, 85–91 (2024). https://doi.org/10.1038/s41586-023-06825-8
All code developed for the paper, including SAR detection, deep learning models, and analyses is open source and freely available at https://github.com/GlobalFishingWatch/paper-industrial-activity. All the data generated and used by these scripts can reference the following data repos:
Analysis and Figures: https://doi.org/10.6084/m9.figshare.24309475
Training and Evaluation: https://doi.org/10.6084/m9.figshare.24309469
",
"schema": {
"label": {
"keyword": "label",
"enum": {
"oil": "oil",
"wind": "wind",
"unknown": "unknown"
}
},
"structure_id": "structure_id",
"label_confidence": {
"keyword": "label_confidence",
"enum": {
"high": "high",
"low": "low",
"medium": "medium"
}
},
"structure_end_date": "structure_end_date",
"structure_start_date": "structure_start_date"
}
},
"public-gfcm-fao": {
"name": "GFCM FAO",
"description": "A fisheries restricted area (FRA) is a geographically defined area in which some specific fishing activities are temporarily or permanently banned or restricted in order to improve the exploitation patterns and conservation of specific stocks as well as of habitats and deep-sea ecosystems. In the Mediterranean and the Black Sea, 1,760,000 square kilometers of sea habitats are protected by ten FRAs established by the GFCM. This includes one large deep-water FRA (1,730,000 square kilometers) in which the use of towed dredges and trawl nets in all waters deeper than 1000 metres is banned to protect deep-sea benthic habitats. The layer was taken from the GFCM website but manually adjusted to reflect the original coordinates of the FRAs as stipulated in the document REC. GFCM/29/2005/1."
},
"public-global-all-tracks": {
"name": "Tracks",
"description": "The dataset contains the tracks from all vessels (AIS) - Version 3.0",
"schema": {
"speed": {
"keyword": "speed",
"enum": {
"0": "0",
"20": "20"
}
}
}
},
"public-global-all-vessels": {
"name": "AIS (All Vessels)",
"description": "Vessels from AIS",
"schema": {
"source": "source"
}
},
"public-global-bathymetry": {
"name": "Bathymetry",
"description": "Overview
\n\n- Bathymetry is the measurement of water depth and provides details of the physical features of the ocean floor. This gridded bathymetric dataset is a continuous global terrain model that provides elevation data for the ocean in meters on a 15 arc-second interval grid.
\n - Disclaimer:\nThe bathymetry layer shown on the map is based on gridded data from GEBCO and represents average depth values per tile, calculated in meters. These tiles are at a coarser resolution, so the depth shown may differ from the specific value at a point where you click. For example, if you click on a vessel track within the 50-meter isoline, the depth shown at that point may not exactly match the tile's average due to differences in resolution.\nKeep in mind that bathymetry datasets are generalized and may not reflect precise seafloor conditions at fine scales. Use caution when interpreting these values for regulatory or analytical purposes. \n\n If you require finer detail, you can also upload your own bathymetry polygons for a more precise analysis.
\n\n \nSource
\n\n GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)."
},
"public-global-carrier-vessels": {
"name": "AIS (Carrier Vessels)",
"description": "Carriers from AIS"
},
"public-global-chlorophyl-max": {
"name": "Chlorophyll-a concentration",
"description": "Chlorophyll-a is the light-harvesting pigment found in all photosynthetic plants. Ocean phytoplankton chemically fix carbon through photosynthesis, taking in dissolved carbon dioxide and producing oxygen. Through this process, marine plants capture about an equal amount of carbon as does photosynthesis by land vegetation. Changes in the amount of phytoplankton indicate the change in productivity of the oceans and provide a key ocean link for global climate change monitoring.
\n
\nChlorophyll-a concentration in the ocean is used as an index of phytoplankton biomass and, as such, is a key input to primary productivity models. The moderate resolution imaging spectroradiometer (MODIS) instrument aboard NASA's Terra and Aqua satellites measures ocean color every day, from which global chlorophyll-a concentrations are derived.
\n
\nScientists use chlorophyll in modeling Earth's biogeochemical cycles such as the carbon cycle or the nitrogen cycle. Additionally, on short time scales, chlorophyll can be used to trace oceanographic currents, jets, and plumes. The 1 kilometer resolution and nearly daily global coverage of the MODIS data thus allows scientists to observe mesoscale oceanographic features in coastal and estuarine environments, which are of increasing importance in marine science studies.
\n
\nSource
\n
\nThis level 3 product includes ocean color and satellite ocean biology data produced or collected under EOSDIS - https://earthdata.nasa.gov/about.
\n
\nThis dataset may be used for studying the biology and hydrology of coastal zones, changes in the diversity and geographical distribution of coastal marine habitats, biogeochemical fluxes and their influence in Earth's oceans and climate over time, and finally the impact of climate and environmental variability and change on ocean ecosystems and the biodiversity they support.
\n
\nScale factor and offset are already applied.
\n
\nCitations
\n
\nNASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA.
"
},
"public-global-chlorophyl-min": {
"name": "Chlorophyll-a concentration",
"description": "Chlorophyll-a is the light-harvesting pigment found in all photosynthetic plants. Ocean phytoplankton chemically fix carbon through photosynthesis, taking in dissolved carbon dioxide and producing oxygen. Through this process, marine plants capture about an equal amount of carbon as does photosynthesis by land vegetation. Changes in the amount of phytoplankton indicate the change in productivity of the oceans and provide a key ocean link for global climate change monitoring.
\n
\nChlorophyll-a concentration in the ocean is used as an index of phytoplankton biomass and, as such, is a key input to primary productivity models. The moderate resolution imaging spectroradiometer (MODIS) instrument aboard NASA's Terra and Aqua satellites measures ocean color every day, from which global chlorophyll-a concentrations are derived.
\n
\nScientists use chlorophyll in modeling Earth's biogeochemical cycles such as the carbon cycle or the nitrogen cycle. Additionally, on short time scales, chlorophyll can be used to trace oceanographic currents, jets, and plumes. The 1 kilometer resolution and nearly daily global coverage of the MODIS data thus allows scientists to observe mesoscale oceanographic features in coastal and estuarine environments, which are of increasing importance in marine science studies.
\n
\nSource
\n
\nThis level 3 product includes ocean color and satellite ocean biology data produced or collected under EOSDIS - https://earthdata.nasa.gov/about.
\n
\nThis dataset may be used for studying the biology and hydrology of coastal zones, changes in the diversity and geographical distribution of coastal marine habitats, biogeochemical fluxes and their influence in Earth's oceans and climate over time, and finally the impact of climate and environmental variability and change on ocean ecosystems and the biodiversity they support.
\n
\nScale factor and offset are already applied.
\n
\nCitations
\n
\nNASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA.
"
},
"public-global-chlorophyl": {
"name": "Chlorophyll-a concentration",
"description": "Overview
\n\n- Chlorophyll-a is the light-harvesting pigment found in all photosynthetic plants. Marine phytoplankton chemically fix carbon through photosynthesis by taking in dissolved carbon dioxide and producing oxygen. This dataset is comprised of biogeochemical parameters, including chlorophyll, over the global ocean displayed with a 1/4 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00015."
},
"public-global-currents-uo": {
"name": "Currents",
"description": "Currents eastward component"
},
"public-global-currents-vo": {
"name": "Currents",
"description": "Currents northward component"
},
"public-global-encounters-events-carriers-fishing": {
"name": "Encounter Events for Carriers-Fishing Vessels (AIS)",
"description": "Identified from AIS data as locations where two vessels, a carrier and fishing vessel, were within 500 meters for at least 2 hours and traveling at a median speed under 2 knots, while at least 10 km from a coastal anchorage."
},
"public-global-encounters-events": {
"name": "Encounter events (AIS)",
"description": "Overview
Encounter events identify instances where two vessels appear to meet at sea.
Users can filter encounter events by vessel characteristics and context, including:
- Duration of encounter
- Vessel flag - Shows encounters where at least one vessel is flagged to the selected country
- Next port visit after the encounter - Shows encounters where at least one vessel is has visited the selected port(s)
- Vessel types involved in the encounter
- User saved vessel group
To view more details about a specific encounter—such as its location or the identity of the encountered vessel—click the “See more” icon associated with the event.
Caveats
- Global Fishing Watch events are the result of rule-based algorithms being applied to AIS positional data. There are many potential reasons for vessels to meet at sea. Such interactions may include transshipment of catch or supplies, equipment transfers, crew changes, safety-related matters, and more. Encounter data should therefore be viewed as an indicator for review.
- Encounters that do not meet the specifications of GFW encounter events described above are not included in the map, such as encounters less than two hours and in-port encounters.
- Encounters between the same two vessels occurring within a 4-hour window are consolidated into a single encounter event. While multiple encounters within such a short timeframe are possible, they remain exceptionally rare.
- The 500-meter proximity threshold is calculated using implied positions—not raw AIS messages. Since AIS transmissions occur at irregular intervals, vessel positions are estimated by a 10-minute time grid using reported course and speed. Proximity is then calculated based on these estimated positions. Due to this modeling approach, it is possible that vessels identified in an encounter may not have been physically within 500 meters of each other for the entire 2-hour period.
- Bias in vessel identification and gear classification can result in the unexpected presence or absence of an encounter. Misclassifications in vessel type may occur due to inconsistent or incomplete vessel registry data. Misclassifications can also happen when algorithms struggle to appropriately categorize vessels, for instance, where vessels use several gears (thus changing their behavioral patterns) or when a vessel’s MMSI (maritime mobile service identity) number is used by more than one vessel.
- An encounter event position may not align exactly with the vessels' tracks. Global Fishing Watch determines a single location for each event by calculating the average latitude and longitude of all positions within the event. As a result it is possible the vessels never occupied that precise location during the encounter event.
- Global Fishing Watch recommends to visually inspect vessel tracks, always refer to additional data source and/or information, and request records from a vessel to confirm any findings, as part of the users’ due diligence process.
Learn more
You can read more about transshipment behaviour from our report or scientific publication.
",
"schema": {
"duration": {
"keyword": "duration",
"enum": {
"2": "2",
"48": "48"
}
},
"next_port_id": "next_port_id",
"encounter_type": {
"keyword": "Vessel types",
"enum": {
"CARRIER-FISHING": "Carrier <> Fishing",
"SUPPORT-FISHING": "Support <> Fishing",
"FISHING-CARRIER": "Fishing <> Carrier",
"FISHING-BUNKER": "Fishing <> Bunker",
"FISHING-FISHING": "Fishing <> Fishing",
"CARRIER-BUNKER": "Carrier <> Bunker",
"FISHING-SUPPORT": "Fishing <> Support"
}
}
}
},
"public-global-fishing-effort": {
"name": "AIS",
"description": "Global Fishing Watch uses data about a vessel’s identity, type, location, speed, direction and more that is broadcast using the Automatic Identification System (AIS) and collected via satellites and terrestrial receivers. AIS was developed for safety/collision-avoidance. Global Fishing Watch analyzes AIS data collected from vessels that our research has identified as known or possible commercial fishing vessels, and applies a fishing presence algorithm to determine “apparent fishing activity” based on changes in vessel speed and direction. The algorithm classifies each AIS broadcast data point for these vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch fishing activity heat map. AIS data as broadcast may vary in completeness, accuracy and quality. Also, data collection by satellite or terrestrial receivers may introduce errors through missing or inaccurate data. Global Fishing Watch’s fishing presence algorithm is a best effort mathematically to identify “apparent fishing activity.” As a result, it is possible that some fishing activity is not identified as such by Global Fishing Watch; conversely, Global Fishing Watch may show apparent fishing activity where fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies designations of vessel fishing activity, including synonyms of the term “fishing activity,” such as “fishing” or “fishing effort,” as “apparent,” rather than certain. Any/all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at your own risk. Global Fishing Watch is taking steps to make sure fishing activity designations are as accurate as possible. Global Fishing Watch fishing presence algorithms are developed and tested using actual fishing event data collected by observers, combined with expert analysis of vessel movement data resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and automated classification techniques.",
"schema": {
"speed": "speed",
"bearing": "bearing",
"distance_from_port_km": {
"keyword": "Port buffer",
"enum": {
"0": "0",
"1": "1",
"2": "2",
"3": "3",
"4": "4",
"5": "5"
}
}
}
},
"public-global-fishing-events": {
"name": "Fishing Events (AIS)",
"description": "The dataset contains fishing events for AIS",
"schema": {
"type": {
"keyword": "type",
"enum": {
"CARGO": "CARGO",
"DISCREPANCY": "DISCREPANCY",
"CARRIER": "CARRIER",
"FISHING": "FISHING",
"GEAR": "GEAR",
"OTHER": "OTHER",
"PASSENGER": "PASSENGER",
"SEISMIC_VESSEL": "SEISMIC_VESSEL",
"SUPPORT": "SUPPORT"
}
}
}
},
"public-global-fishing-vessels": {
"name": "AIS (Fishing Vessels)",
"description": "Vessels from AIS",
"schema": {
"source": "source"
}
},
"public-global-gaps-events": {
"name": "Disabling Events (AIS)",
"description": "The dataset contains gaps events for AIS"
},
"public-global-loitering-events-carriers": {
"name": "Loitering Events for Carriers Vessels (AIS)",
"description": "The dataset contains loitering events for AIS (Carriers)."
},
"public-global-loitering-events": {
"name": "Loitering events (AIS)",
"description": "Overview
Loitering events identify when a vessel is operating away from shore while moving at low speeds. Loitering events highlight when a vessel is behaving in a manner consistent with a potential encounter event (i.e. stationary or drifting slowly at sea) but no encountering vessel is visible on AIS. As such, these should not be considered indicators of likely encounter events, but rather periods of time when undetected encounters could have potentially occurred.
Caveats
- Global Fishing Watch events are the result of a rule-based algorithm being applied to AIS positional data. Loitering events should be cross-verified with other sources of information (like RFMO transshipment records) when possible.
- Vessels in loitering events may not be meeting any other vessels at the time of the event. Other events in which a vessel may remain fairly stationary or moving slowly while at sea include: maintenance, losing vessel power, idling during poor weather, waiting outside of port for permission to dock, normal fishing behavior, and more.
- Due to the individual definitions of loitering events, fishing events, and encounter events, it is possible for a single vessel movement pattern to trigger multiple event types.
- A loitering event position may not align exactly with the vessel’s tracks. Global Fishing Watch determines a single location for each event by calculating the average latitude and longitude of all positions within that event. As a result it is possible the vessel never occupied that precise location during the event.
- Global Fishing Watch recommends to visually inspect vessel tracks, always refer to additional data source and/or information, and request records from a vessel to confirm any findings, as part of the users’ due diligence process.
Learn more
Learn more about considerations of using AIS data by looking at the AIS limitations section in the Apparent fishing events (AIS) data documentation..
",
"schema": {
"type": {
"keyword": "Vessel types",
"enum": {
"CARGO": "CARGO",
"DISCREPANCY": "DISCREPANCY",
"CARRIER": "CARRIER",
"FISHING": "FISHING",
"GEAR": "GEAR",
"OTHER": "OTHER",
"PASSENGER": "PASSENGER",
"SEISMIC_VESSEL": "SEISMIC_VESSEL",
"SUPPORT": "SUPPORT"
}
},
"duration": {
"keyword": "duration",
"enum": {
"0": "0",
"48": "48"
}
},
"next_port_id": "next_port_id"
}
},
"public-global-nitrate": {
"name": "Nitrate concentration (NO3)",
"description": "Overview
\n\n- Nitrate is required by marine plants for photosynthesis and is a major nutrient for the ocean's productivity, but high concentrations can be an indicator of pollution. This dataset is comprised of biogeochemical parameters, including nitrate, over the global ocean displayed with a 1/4 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00015.\""
},
"public-global-other-vessels": {
"name": "AIS (Other Vessels)",
"description": "Other vessels from AIS",
"schema": {
"source": "source"
}
},
"public-global-oxygen": {
"name": "Oxygen concentration (O2)",
"description": "Overview
\n\n- Dissolved oxygen is the amount of oxygen present in water and is crucial for the growth and survival of marine organisms. Warming ocean temperatures can lead to an expansion of low oxygen zones, which can impact the distribution and abundance of marine life. This dataset is comprised of biogeochemical parameters, including dissolved oxygen, over the global ocean displayed with a 1/4 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00015."
},
"public-global-ph": {
"name": "potential of hydrogen (PH)",
"description": "Overview
\n\n- The acidity of the ocean is measured by pH. Increased carbon dioxide absorption has lowered the ocean's pH, resulting in ocean acidification which can have wide-ranging impacts on marine organisms. This dataset is comprised of biogeochemical parameters, including pH, over the global ocean displayed with a 1/4 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00015."
},
"public-global-phosphate": {
"name": "Phosphate concentration (PO4)",
"description": "Overview
\n\n- Phosphate is a major nutrient for marine foodwebs and ocean productivity, but high concentrations can also be an indicator of pollution. This dataset is comprised of biogeochemical parameters, including phosphate, over the global ocean displayed with a 1/4 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00015."
},
"public-global-port-visits-events": {
"name": "Port visit events (AIS)",
"description": "Overview
Global Fishing Watch detects vessel movements in and out of ports and classifies them into four distinct port event types:
- Port entry
- Port stop
- Port gap (a gap in AIS transmission while in port)
- Port exit
These events are grouped to form port visit events, which represent a vessel’s presence at port based on AIS activity patterns.
In the Global Fishing Watch map, only high-confidence port visits are shown. A port visit is determined with high confidence when a vessel is detected with a port entry, stop or gap, and exit. More specifically:
A port visit is shown on the map when AIS data shows a vessel:
- Entering within 3 kilometers of an anchorage point at the port entry,
- Exiting within 4 kilometers of a known port exit, and either:
- Experiencing a port gap (i.e. no AIS transmissions for 4 hours or more, which may indicate AIS was turned off while in port), or
- Undergoing a port stop, where the vessel’s speed drops below 0.2 knots and later increases above 0.5 knots.
Port stops are used to distinguish actual port visits from coastal transits.
Caveats
- Lower-confidence port visits—often caused by noisy, sparse, or incomplete AIS transmissions—are currently excluded from the map. Lower-confidence port visits, while sometimes legitimate, can falsely suggest a port visit that did not occur.
- Ongoing port visits will not be identified on the map, as a port exit is required by definition for high-confidence port visits to be detected.
- Additionally, note that the Global Fishing Watch anchorages dataset may not capture all actual anchorage locations, which could lead to missing port events. Refer to the learn more section for a full list of anchorages.
- Global Fishing Watch recommends to verify port visits, always refer to additional data source and/or information, and request records from a vessel to confirm any findings, as part of the users’ due diligence process.
Learn more
Learn more about anchorages, ports and voyages.
Learn more about considerations of using AIS data by looking at the AIS limitations section in the Apparent fishing events (AIS) data documentation..",
"schema": {
"type": {
"keyword": "Vessel type",
"enum": {
"CARGO": "CARGO",
"DISCREPANCY": "DISCREPANCY",
"CARRIER": "CARRIER",
"FISHING": "FISHING",
"GEAR": "GEAR",
"OTHER": "OTHER",
"PASSENGER": "PASSENGER",
"SEISMIC_VESSEL": "SEISMIC_VESSEL",
"SUPPORT": "SUPPORT"
}
},
"duration": {
"keyword": "duration",
"enum": {
"2": "2",
"48": "48"
}
}
}
},
"public-global-ports-footprint": {
"name": "Anchorages convex hulls by portId",
"description": "Anchorages footprint using the convex hull grouped by portId",
"schema": {
"area": "area",
"label": {
"keyword": "label",
"enum": {}
},
"perimeter": "perimeter"
}
},
"public-global-presence-tracks": {
"name": "Tracks",
"description": "The dataset contains the tracks from all vessels (AIS) - Version 20201001",
"schema": {
"speed": {
"keyword": "speed",
"enum": {
"0": "0",
"20": "20"
}
}
}
},
"public-global-presence": {
"name": "AIS",
"description": "Global Fishing Watch uses data about a vessel’s identity, type, location, speed, direction and more that is broadcast using the Automatic Identification System (AIS) and collected via satellites and terrestrial receivers. AIS was developed for safety/collision-avoidance. The activity layer displays a heatmap of vessel presence. The presence is determined by taking one position per hour per vessel from the positions transmitted by the vessel's AIS.",
"schema": {
"speed": {
"keyword": "speed",
"enum": {
"<2": "<2",
"2-4": "2-4",
"4-6": "4-6",
"6-10": "6-10",
"10-15": "10-15",
"15-25": "15-25",
">25": ">25"
}
},
"bearing": "bearing"
}
},
"public-global-salinity": {
"name": "Salinity",
"description": "
Overview
\n\n- Sea surface salinity is the measurement of salt concentration at the ocean surface. It determines the density of ocean water along with temperature and is a key parameter to estimate the ocean's influence on climate. This dataset salinity is displayed with a 1/12 degree horizontal resolution.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00016."
},
"public-global-sar-footprints": {
"name": "Detection footprints",
"description": "Detection footprints are areas within each satellite scan (or scene) that the platform uses to perform detections. These filters help to keep relevant detections and exclude data that may be inaccurate.
\n
\nDetection footprints are smaller than the total scene as they exclude any land areas and islands, and exclude a 500 meter buffer from the boundaries of the scene and a 1 kilometer buffer from shorelines. "
},
"public-global-sar-presence": {
"name": "Radar vessel detections (SAR)",
"description": "Overview
\nSatellite synthetic aperture radar (SAR) is a spaceborne radar imaging system that can detect at-sea vessels and structures in any weather conditions. Microwave pulses are transmitted by a satellite-based antenna towards the Earth surface. The microwave energy scattered back to the spacecraft is then measured and integrated to form a “backscatter” image. The SAR image contains rich information about the different objects on the water, such as their size, orientation and texture. SAR imaging systems overcome most weather conditions and illumination levels, including clouds or rain due to the cloud penetrating property of microwaves, and daylight or darkness due to radar being an “active” sensor (it shoots and records back its own energy). SAR gives an advantage over some other “passive” satellite sensors, such as electro-optical imagery, consisting of a satellite-based camera recording the sunlight/infrared radiation reflected from/emitted by objects on the ground. This latter method can be confounded by cloud cover, haze, weather events and seasonal darkness at high latitudes.
\nUse cases
\n\n - Monitor vessel presence (both fishing and non-fishing) in areas of interest such as marine protected areas (MPAs), exclusive economic zones (EEZs), inshore exclusion zones (IEZs) and Regional Fisheries Management Organisations (RFMOs).
\n - Assess presence of vessels that don’t show up on cooperative tracking systems—including automatic identification system (AIS) and vessel monitoring system (VMS)—near vulnerable marine ecosystems and essential fish habitats.
\n
\nLimitations
\n\n - Sentinel-1 SAR data does not sample most of the open ocean.
\n \n - Sentinel-1 does not sample most of the open ocean. However, the vast majority of industrial activity is close to shore. Also, farther from shore, more fishing vessels use AIS (60-90%), far more than the average for all fishing vessels (about 25%). Thus, for most of the world, our detection data complemented by AIS will capture the vast majority of human activity in the global ocean.
\n
\n - False positives can be produced from image artifacts (noise).
\n - We do not provide detections of vessels 1 kilometer from shore as it’s difficult to accurately map where the shoreline begins.
\n \n - We do not include objects within 1 km of shore because of ambiguous coastlines and rocks. Nor do we include objects in much of the Arctic and Antarctic, where sea ice can create too many false positives; in both regions, however, vessel traffic is either very low (Antarctic) or in countries that have a high adoption of AIS (northern European or northern North American countries). The bulk of industrial activities occur several kilometers from shore, such as fishing along the continental shelf break, ocean transport over shipping lanes, and offshore development on medium-to-large oil rigs and wind farms. Also, much of the vessel activity within 1 km of shore is by smaller boats such as pleasure crafts.
\n
\n - Vessel detection by SAR imagery is limited primarily by the resolution of the images (~20 m in the case of Sentinel-1 IW GRD products).
\n \n - As a result, we miss most vessels under 15 m in length, although an object smaller than a pixel can still be seen if it is a strong reflector, such as a vessel made of metal rather than wood or fiberglass. Especially for smaller vessels (25 m), detection also depends on wind speed and the state of the ocean, as a rougher sea surface will produce higher backscatter, making it difficult to separate a small target from the sea clutter. Conversely, the higher the radar incidence angle, the higher the probability of detection, as less backscatter from the background will be received by the antenna. The vessel orientation relative to the satellite antenna also matters, as a vessel perpendicular to the radar line of sight will have a larger backscatter cross section, increasing the probability of being detected.
\n
\n - Vessel length estimates are limited by the quality of ground truth data
\n \n - Although we selected only high-confidence AIS-SAR matches to construct our training data, we found that some AIS records contained an incorrectly reported length. These errors, however, resulted in only a small fraction of imprecise training labels, and deep learning models can accommodate some noise in the training data.
\n
\n - Not all geographies are covered equally
\n \n - Our fishing classification may be less accurate in certain regions. In areas of high traffic from pleasure crafts and other service boats, such as near cities in some countries and in the fjords of Norway and Iceland, some of these smaller craft might be misclassified as fishing vessels. Conversely, some misclassification of fishing vessels as non-fishing vessels is expected in areas where all activity is not publicly shared. More importantly, however, is that many industrial fishing vessels are between 10 and 20 meters in length, and the detection capability of our model falls off quickly within these lengths. As a result, the total number of industrial fishing vessels is likely significantly higher than what we detect.
\n - Our data likely underestimates the concentration of fishing in some regions, where we see areas of vessel activity being \"cut off\" by the edge of the Sentinel-1 footprint and we miss very small vessels (e.g., most artisanal fishing) that are less likely to carry AIS devices.
\n
\n
\nMethods
\nSAR imagery
\nWe use SAR imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA) [1]. The images are sourced from two satellites (S1A and S1B up until December 2021 when S1B stopped operating, and S1A only from 2022 onward) that orbit 180 degrees out of phase with each other in a polar, sun-synchronous orbit. Each satellite has a repeat-cycle of 12 days, so that together they provide a global mapping of coastal waters around the world approximately every six days for the period that both were operating. The number of images per location, however, varies greatly depending on mission priorities, latitude, and degree of overlap between adjacent satellite passes. Spatial coverage also varies over time [2]. Our data consist of dual-polarization images (VH and VV) from the Interferometric Wide (IW) swath mode, with a resolution of about 20 m.
\n[1]\n \n https://sedas.satapps.org/wp-content/uploads/2015/07/Sentinel-1_User_Handbook.pdf\n \n
\n[2]\n \n https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/observation-scenario\n \n
\nDetection footprints
\nDetection footprints are areas within each satellite scan (or scene) that our system uses to perform detections. These filters help to keep relevant detections and exclude data that may be inaccurate. Detection footprints are smaller than the total scene as they exclude any land areas and islands, and exclude a 500 meter buffer from the boundaries of the scene and a 1 kilometer buffer from shorelines.
\nFiltering
\nGFW has post-processed the SAR detections to reduce noise (false positives), remove offshore infrastructure from this layer focused on vessels, and exclude areas with sea ice at high latitudes.
\nVessel detection by SAR
\nDetecting vessels with SAR is based on an known as Constant False Alarm Rate (CFAR), a threshold algorithm used for anomaly detection in radar imagery. This algorithm is designed to search for pixel values that are unusually bright (the targets) compared to those in the surrounding area (the sea clutter). This method sets a threshold based on the pixel values of the local background (within a window), scanning the whole image pixel-by-pixel. Pixel values above the threshold constitute an anomaly and are likely to be samples from a target, and therefore are included as a detection.
\nVessel presence and length estimation
\nTo estimate the length of every detected object and also to identify when our CFAR algorithm made false detections, we designed a deep convolutional neural network (ConvNet) based on the modern ResNet (Residual Networks) architecture. This single-input/multi-output ConvNet takes dual-band SAR image tiles of 80 by 80 pixels as input, and outputs the probability of object presence (known as a “binary classification task”) and the estimated length of the object (known as a “regression task”).
\nFishing and non-fishing classification
\nTo identify whether a detected vessel was a fishing or non-fishing vessel we use a machine learning model. For this classification task we used a ConvNeXt architecture modified to process the following two inputs: the estimated length of the vessel from SAR (a scalar quantity) and a stack of environmental rasters centered at the vessel’s location (a multi-channel image). This multi-input-mixed-data/single-output model passes the raster stack (11 channels) through a series of convolutional layers and combines the resulting feature maps with the vessel length value to perform a binary classification: fishing or non-fishing.
\nThe environmental layers used to differentiate between fishing and non-fishing include:
\n\n - vessel density (based on SAR)
\n - average vessel length (based on SAR)
\n - bathymetry
\n - distance from port
\n - hours of non-fishing vessel presence, under 50 m (from AIS)
\n - hours of non-fishing vessel presence, over 50 m (from AIS)
\n - average surface temperature
\n - average current speed
\n - standard deviation of daily temperature
\n - standard deviation of daily current speed
\n - average chlorophyll
\n
\nAIS matching and vessel identity
\nAIS data can reveal the identity of vessels, their owners and corporations, and fishing activity. Not all vessels, however, are required to use AIS devices, as regulations vary by country, vessel size, and activity. Vessels engaged in illicit activities can also turn off their AIS transponders or manipulate the locations they broadcast. Also, large “blind spots” along coastal waters arise from nations that restrict access to AIS data that are captured by terrestrial receptors instead of satellites or from poor reception due to high vessel density and low-quality AIS devices. Unmatched SAR detections therefore provide the missing information about vessel traffic in the ocean.
\nSAR and AIS matching
\nMatching SAR detections to vessels’ GPS coordinates (from the automatic identification system (AIS) is challenging because the timestamp of the SAR images and AIS records do not coincide, and a single AIS message can potentially match to multiple vessels appearing in the image, and vice versa. To determine the likelihood that a vessel broadcasting AIS corresponded to a specific SAR detection, we followed a matching approach based on probability rasters of where a vessel is likely to be minutes before and after an AIS position was recorded. These rasters were developed from one year of global AIS data from the Global Fishing Watch pipeline which uses Spire Global and Orbcomm sources of satellite data, including roughly 10 billion vessel positions, and computed for six different vessel classes, considering six different speeds and 36 time intervals. So we obtain the likely position of a vessel that could match a SAR detection based on the vessel class, speed and time interval.
\nAIS matching and vessel identity
\nAutomatic identification system (AIS) data can reveal the identity of vessels, their owners and corporations, and fishing activity. Not all vessels, however, are required to use AIS devices, as regulations vary by country, vessel size, and activity. Vessels engaged in illicit activities can also turn off their AIS transponders or manipulate the locations they broadcast. Also, large “blind spots” along coastal waters arise from nations that restrict access to AIS data that are captured by terrestrial receptors instead of satellites or from poor reception due to high vessel density and low-quality AIS devices. Unmatched SAR detections therefore provide the missing information about vessel traffic in the ocean.
\nResources, code and other notes
\nAll code developed in this study for SAR detection, deep learning models, and analyses is open source and freely available at\n \n https://github.com/GlobalFishingWatch/paper-industrial-activity\n .\n
\nSource data and citations
\nAll vessel data are freely available through the Global Fishing Watch data portal at\n \n https://globalfishingwatch.org\n . All data to reproduce our supporting scientific paper can be downloaded from\n \n https://doi.org/10.6084/m9.figshare.24309475\n \n (statistical analyses and figures) and\n \n https://doi.org/10.6084/m9.figshare.24309469\n \n (model training and evaluation).\n
\nLicense
\nNon-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.",
"schema": {
"speed": "speed",
"bearing": "bearing",
"matched": {
"keyword": "Matching AIS vessels",
"enum": {
"true": "AIS Matched",
"false": "AIS Unmatched"
}
},
"shiptype": {
"keyword": "shiptype",
"enum": {
"carrier": "Carrier",
"seismic_vessel": "seismic_vessel",
"passenger": "passenger",
"other": "other",
"support": "Support",
"bunker": "bunker",
"gear": "gear",
"cargo": "cargo",
"fishing": "fishing",
"discrepancy": "discrepancy"
}
},
"neural_vessel_type": {
"keyword": "SAR Vessel Type",
"enum": {
"Likely non-fishing": "Likely non-fishing",
"Likely Fishing": "Likely fishing",
"Unknown": "Unknown"
}
}
}
},
"public-global-sentinel2-footprints": {
"name": "Detection footprints",
"description": "Detection footprints"
},
"public-global-sentinel2-presence": {
"name": "Imagery vessel detections (Optical)",
"description": "
Overview
\n This layer shows vessels detected using optical satellite imagery collected by the European Space Agency's Sentinel-2 satellites. Optical imagery is similar to high-quality aerial photography from space, using reflected sunlight in visible and near-infrared wavelengths. This type of imagery provides high-resolution detail that allows us to spot small vessels, identify wake patterns, and better understand activity near shore.
\n Global Fishing Watch uses a machine learning model that processes each image to identify vessels and estimate their length, orientation, and speed based on wake features. The detections are then filtered using a secondary classifier to remove objects that are not vessels, such as clouds, rocks or icebergs. Each detection is linked to a cropped image (a thumbnail) so users can visually inspect what the model identified.
\n Because optical satellites rely on sunlight and clear skies, detections are only possible during the day and when the area is not obscured by clouds or haze. Despite these limitations, detections with optical imagery are especially helpful in identifying small untracked vessels that may not appear in other tracking systems.
\nUse cases
\n\n - Monitor vessel presence (both fishing and non-fishing) in areas of interest such as marine protected areas (MPAs), exclusive economic zones (EEZs), inshore exclusion zones (IEZs) and Regional Fisheries Management Organisations (RFMOs). In some cases, activity like bottom trawling can be seen through disturbance to seabed sediment.
\n - Assess presence of vessels that don't show up on cooperative tracking systems—including automatic identification system (AIS) and vessel monitoring system (VMS)—near vulnerable marine ecosystems and essential fish habitats.
\n - Goes beyond vessel detection in other satellite remote sensors like Sentinel-1 SAR and VIIRS which simply detect the presence of an object, with Sentinel-2 users can often infer the object's activity based on the wake of a detection, and in some cases, the dataset can be used to identify fishing activity e.g. sediment plumes of trawlers, net encircling fish in purse seine vessels.
\n - Support analyses on small-scale fishing. While the 10m resolution is still too coarse to comprehensively map small-scale fishing, Sentinel-2 detections have been integrated into multiple analyses related to regional small-scale fishery and demonstrated the potential as a valuable addition to the limited vessel tracking data.
\n
\nLimitations
\n\n - Vessel detection with optical imagery requires daylight and clear skies
\n - Unlike radar, optical satellites cannot see through clouds, fog, or haze. Detections are only possible during daylight hours when the view is unobstructed.
\n
\n \n - Not all geographies are covered equally
\n - Sentinel-2 coverage is mostly limited to coastal waters. It revisits most areas every five days, but the image availability depends on the weather. Cloudy or hazy regions have lower effective revisit frequencies than regions with better weather conditions.
\n
\n \n - The detections may include false positives
\n - Despite post-processing, the model may still produce occasional false detections—e.g., picking up buoys, debris, fixed infrastructure, or image artifacts. These false positives are reduced using a secondary classifier, but not completely eliminated.
\n
\n \n - Uncertainty in some vessel features
\n - Smaller or slower-moving vessels may not produce visible wakes, making it more difficult to estimate their speed or heading. Therefore, these values may be inaccurate for small boats.
\n
\n \n - Not all detections unmatched to AIS are untracked vessels
\n - The detections include both vessels on AIS and untracked vessels. We try to match detections to AIS tracks, but sometimes matching is not feasible due to large time gaps between AIS positions and in areas with high density of detections.
\n
\n \n
\nMethods
\nOptical imagery
\n This layer is based on images from the Sentinel-2 satellites operated by the European Space Agency (ESA). These satellites capture medium-resolution images (10 m per pixel) of the ocean using visible and near-infrared light (among several other bands). Combined, the satellites acquire images of most coastal waters and dedicated areas in the open ocean roughly every five days, and the imagery is made freely available by the ESA.
\nImage processing and selection
\n We use pre-processed Sentinel-2 images that have been corrected for geometric distortions and aligned to the Earth's surface. These images are split into manageable tiles, and we selected the tiles that cover only ocean areas (image tiles over land are excluded). We use four image bands: red, green, blue (RGB), and near-infrared (NIR), all at 10-meter resolution. These bands give us the detail and contrast needed to detect and classify vessels.
\nVessel detection
\n Our machine learning model scans each image tile to detect vessels. It is trained to look for features such as the shape, brightness, and wake of a vessel. When it finds a likely candidate, the model predicts a score for vessel presence alongside estimates of the vessel's location, size, orientation, and speed.
\n The detection model was trained on over 11,000 manually reviewed vessel examples across thousands of Sentinel-2 scenes. This training process included many small vessels and scenes from around the world, helping the model to perform well across different environments and vessel types.
\nImage thumbnails
\n Each detection includes a small visual \"chip\" showing the detected vessel at the center. These thumbnails come in two formats: a color version from the RGB bands, and a grayscale version from the near-infrared band. Each chip covers an area of 1 km². These thumbnails are helpful for visually confirming a detection or understanding its context. For very small vessels (under 15 meters), it may still be difficult to see them clearly.
\nReducing false positives
\n Not everything that looks like a vessel in satellite imagery actually is one. To help remove false detections (like buoys, offshore platforms, sea ice, or clouds), we run each detection through a secondary classifier. This classifier is a machine learning model that uses both the image thumbnail and additional information about the detection (such as distance from shore, local depth, and vessel density nearby, among others) to decide whether the object is likely to be a vessel. We also flag detections that are close to known fixed infrastructure or in areas with substantial sea ice or iceberg presence.
\n If a detection is classified as likely non-vessel or flagged as potential infrastructure or ice, we remove it from the map layer so only high-confidence detections are included. We also clip the satellite footprints (displayed on the map layer) to exclude the areas under the icy-region mask. However, we provide all the false positives with labels through the data download portal for stakeholders who require a more complete dataset.
\nAIS matching and vessel identity
\n AIS data can reveal the identity of vessels, their owners and corporations, and fishing activity. Not all vessels, however, are required to use AIS devices, as regulations vary by country, vessel size, and activity. Vessels engaged in illicit activities can also turn off their AIS transponders or manipulate the locations they broadcast. Also, large \"blind spots\" along coastal waters arise from nations that restrict access to AIS data that are captured by terrestrial receptors instead of satellites or from poor reception due to high vessel density and low-quality AIS devices. Unmatched imagery detections therefore provide the missing information about vessel traffic in the ocean.
\n Matching imagery detections to vessels' GPS coordinates from AIS is challenging because the timestamps of the images and AIS records do not coincide, and a single AIS identity can potentially match to multiple vessels appearing in the image, and vice versa. To determine the likelihood that a vessel broadcasting AIS corresponded to a specific detection, we developed a matching approach based on probability rasters of where a vessel is likely to be minutes before and after an AIS position was recorded. These rasters were produced from one year of global AIS data from the Global Fishing Watch pipeline, which sources satellite data from Spire Global and Orbcomm. The probability rasters are based on roughly 10 billion vessel positions and are computed for six different vessel classes, considering six different speeds and 36 time intervals. So we obtain the likely position of a vessel that could match a detection based on the vessel class, speed and time interval. In addition to the spatiotemporal matching, we factor in the similarity between the model-inferred vessel length and the length from AIS identity data to avoid (likely incorrect) matches with large discrepancies in size, e.g., AIS of a tugboat and the detection of a large vessel behind it.
\nDetection footprints
\n To help users understand where detections were possible, we show the detection \"footprints\" on the map. These polygons are the portions of the satellite images that cover the ocean and that were used for detection. Thus, if you see a footprint but no detections, it means no vessels were detected in that area. If there is no footprint, no image was processed for that location and time.
\nAutomation and updates
\n Our detection and matching system runs automatically each day. It checks for new Sentinel-2 images published to Google Cloud and processes those that meet our quality criteria. New detections are typically available within 1–2 days of the satellite capturing the image. The automated pipeline also re-checks any images published late to ensure any data gaps are filled.
\nSource data and citations
\n All vessel data are freely available through the Global Fishing Watch data portal at https://globalfishingwatch.org/data-download/.
\nLicense
\n Non-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.
",
"schema": {
"length": {
"keyword": "Vessel length",
"enum": {
"<20": "<20",
"20-60": "20-60",
"60-100": "60-100",
">100": ">100"
}
},
"bearing": "Bearing",
"matched": {
"keyword": "Matching",
"enum": {
"true": "AIS matched",
"false": "AIS unmatched"
}
},
"shiptype": {
"keyword": "Vessel type",
"enum": {
"carrier": "Carrier",
"seismic_vessel": "Seismic vessel",
"passenger": "Passenger",
"other": "Other",
"support": "Support",
"bunker": "Bunker",
"gear": "Gear",
"cargo": "Cargo",
"fishing": "Fishing",
"discrepancy": "Discrepancy"
}
}
}
},
"public-global-sst-anomalies-max": {
"name": "Sea Surface Temperatures anomalies (Max)",
"description": "Sea surface temperatures anomalies (Max)"
},
"public-global-sst-anomalies-min": {
"name": "Sea Surface Temperatures anomalies (Min)",
"description": "Sea surface temperatures anomalies (Max)"
},
"public-global-sst-anomalies": {
"name": "Sea Surface Temperatures anomalies",
"description": "Sea surface temperatures anomalies (Mean)"
},
"public-global-sst-max": {
"name": "Surface Temperature Max",
"description": "Surface Temperature"
},
"public-global-sst-min": {
"name": "Surface Temperature Min",
"description": "Surface Temperature"
},
"public-global-sst": {
"name": "Sea surface temperature",
"description": "Overview
\n\n- Sea surface temperature is the water temperature at or near the surface. It can impact weather and regional climates, and together with salinity can drive ocean circulation and large-scale movement of ocean currents globally. This dataset provides sea surface temperature at 1/20 degree horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers.
\n\nSource
\n\n Generated using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00165."
},
"public-global-support-vessels": {
"name": "AIS (Support Vessels)",
"description": "Support vessels from AIS",
"schema": {
"source": "source"
}
},
"public-global-thgt": {
"name": "Wave height",
"description": "Overview
\n\n- Wave height is the vertical distance from the trough (bottom) to the crest (top) of a wave on the sea surface. This dataset uses the significant wave height, which is the average of the highest one-third of all wave heights over a given sample period.
\n\nSource
\n\n Cheung, K.F. 2010, updated 2021. WaveWatch III (WW3) Global Wave Model. Distributed by the Pacific Islands Ocean Observing System (PacIOOS), which is a part of the U.S. Integrated Ocean Observing System (IOOS®), funded in part by National Oceanic and Atmospheric Administration (NOAA) Awards #NA16NOS0120024 and #NA21NOS0120091."
},
"public-global-tracks": {
"name": "Tracks",
"description": "The dataset contains the tracks from all vessels (AIS) - Version 3.0",
"schema": {
"speed": "speed"
}
},
"public-global-vessel-identity": {
"name": "AIS (all vessel types)",
"description": "Vessel Identity (all shiptypes)",
"schema": {
"registryTmtExtraFields": "registryTmtExtraFields"
}
},
"public-global-vessel-insights": {
"name": "AIS (Insights)",
"description": "Vessel Insights from AIS"
},
"public-global-viirs-presence": {
"name": "Night light detections (VIIRS)",
"description": "The night lights vessel detections layer, known as visible infrared imaging radiometer suite or VIIRS, shows vessels at sea that satellites have detected by the light they emit at night. Though not exclusively associated with fishing vessels, this activity layer is likely to show vessels associated with activities like squid fishing, which use bright lights and fish at night.
\n
\nBased on the Suomi NPP satellite, the VIIRS sensor makes a pass across the entire planet at least once every night, detecting lights to provide at least one daily observation globally. Due to the orbit design of polar orbiting satellites, regions closer to polar will have more over-passes per day, while equatorial regions have only one over-pass daily.
\n
\nBecause the vessels are detected solely based on light emission, we can detect individual vessels and even entire fishing fleets that may not broadcast identity information and so may not be represented elsewhere on the Global Fishing Watch map. Global Fishing Watch ingests boat detections processed from low light imaging data collected by the U.S. National Oceanic and Atmospheric Administration (NOAA) VIIRS. The boat detections are processed in near-real time by the Earth Observation Group at the Colorado School of Mines. The data, known as VIIRS boat detections, picks up the presence of vessels, including those fishing using lights to attract catch or to conduct operations at night.Lights from fixed offshore infrastructure and other non-vessel sources are excluded. Read more about VIIRS night light vessel detections, and download the data.
\n
\nGlobal Fishing Watch has developed a sophisticated system to match available automatic identification system (AIS) data to respective night light vessel detections. This matching is done using a probabilistic model that determines AIS-message/VIIRS-detection pairs based on all available AIS records right before and right after the time the satellite VIIRS image was taken, as well as the probability of pairing a specific AIS message to any of the vessels appearing on that image. Using this information, Global Fishing Watch has added the experimental ability to filter detections based on vessel type and gear type within the VIIRS activity layer.
\n
\nMore than 85% of the detections are from vessels that lack AIS or publicly shared vessel monitoring system (VMS) transponders. The global addition of the VIIRS layer enables you to rapidly filter the night light detections that either were matched or not with AIS where vessel identification is available.
\n
\nRadiance indicates the brightness of the light source received by the VIIRS sensor. Radiance is impacted by the moon, clouds, and the angle of the vessel from the satellite. Two vessels with the same brightness, or light intensity, may have different radiance levels depending on the conditions. In general, vessels that are not actively fishing using light may have lower radiance levels. Exceptions should be considered when vessels are approaching a coastline. To further explore how vessel lights at night emit different radiance levels, the VIIRS activity layer can be filtered to specific ranges of interest associated with different human behaviours.
\n
\nThose using night light detections data should consider the South Atlantic Anomaly (SAA), an area where the Earth's inner Van Allen radiation belt is at its lowest altitude, allowing more energetic particles from space to penetrate. When such particles hit the sensors on a satellite, this can create a false signal which might cause the algorithm to recognize it as a boat detection. A filtration algorithm has been applied but there may still be some mis-identification",
"schema": {
"speed": "speed",
"bearing": "bearing",
"matched": {
"keyword": "matched",
"enum": {
"true": "AIS Matched",
"false": "AIS Unmatched"
}
},
"radiance": {
"keyword": "Radiance",
"enum": {
"0": "0",
"10000": "10000"
}
},
"shiptype": {
"keyword": "shiptype",
"enum": {
"unknown": "unknown",
"fishing": "fishing",
"carrier": "carrier",
"support": "support"
}
},
"qf_detect": {
"keyword": "qf_detect",
"enum": {
"1": "1",
"2": "2",
"3": "3",
"5": "5",
"7": "7",
"10": "10"
}
}
}
},
"public-global-water-salinity-max": {
"name": "Salinity",
"description": "Sea surface salinity is a key parameter to estimate the influence of oceans on climate. Along with temperature, salinity is a key factor that determines the density of ocean water and thus determines the convection and re-emergence of water masses.
\n
\nGlobal circulation crosses all the oceans in surface and at depth, driven by temperature and salinity. A global “conveyor belt” is a simple model of large-scale circulation, known as “thermohaline” circulation. Deep-water forms in the North Atlantic, sinks, moves south, circulates around Antarctica, and finally enters the Indian, Pacific, and Atlantic basins. Currents bring cold water masses from north to south and vice versa.
\n
\nThis thermohaline circulation greatly influences the formation of sea ice at the world’s poles, and carries ocean food sources and sea life around the planet, as well as affects rainfall patterns, wind patterns, hurricanes and monsoons.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-water-salinity-min": {
"name": "Salinity",
"description": "Sea surface salinity is a key parameter to estimate the influence of oceans on climate. Along with temperature, salinity is a key factor that determines the density of ocean water and thus determines the convection and re-emergence of water masses.
\n
\nGlobal circulation crosses all the oceans in surface and at depth, driven by temperature and salinity. A global “conveyor belt” is a simple model of large-scale circulation, known as “thermohaline” circulation. Deep-water forms in the North Atlantic, sinks, moves south, circulates around Antarctica, and finally enters the Indian, Pacific, and Atlantic basins. Currents bring cold water masses from north to south and vice versa.
\n
\nThis thermohaline circulation greatly influences the formation of sea ice at the world’s poles, and carries ocean food sources and sea life around the planet, as well as affects rainfall patterns, wind patterns, hurricanes and monsoons.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-water-salinity": {
"name": "Salinity",
"description": "Sea surface salinity is a key parameter to estimate the influence of oceans on climate. Along with temperature, salinity is a key factor that determines the density of ocean water and thus determines the convection and re-emergence of water masses.
\n
\nGlobal circulation crosses all the oceans in surface and at depth, driven by temperature and salinity. A global “conveyor belt” is a simple model of large-scale circulation, known as “thermohaline” circulation. Deep-water forms in the North Atlantic, sinks, moves south, circulates around Antarctica, and finally enters the Indian, Pacific, and Atlantic basins. Currents bring cold water masses from north to south and vice versa.
\n
\nThis thermohaline circulation greatly influences the formation of sea ice at the world’s poles, and carries ocean food sources and sea life around the planet, as well as affects rainfall patterns, wind patterns, hurricanes and monsoons.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-water-temperature-max": {
"name": "Sea surface temperature",
"description": "Sea surface temperature is the water temperature at or near the surface of the ocean. Sea surface temperature impacts weather and regional climates. Temperature and salinity drive global ocean circulation and large-scale movement of ocean currents.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-water-temperature-min": {
"name": "Sea surface temperature",
"description": "Sea surface temperature is the water temperature at or near the surface of the ocean. Sea surface temperature impacts weather and regional climates. Temperature and salinity drive global ocean circulation and large-scale movement of ocean currents.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-water-temperature": {
"name": "Sea surface temperature",
"description": "Sea surface temperature is the water temperature at or near the surface of the ocean. Sea surface temperature impacts weather and regional climates. Temperature and salinity drive global ocean circulation and large-scale movement of ocean currents.
\n
\nSource
\n
\nThe Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.
\n
\nThe HYCOM Consortium, which includes the National Ocean Partnership Program (NOPP), is part of the U.S. Global Ocean Data Assimilation Experiment (GODAE).
\n
\nFunded by the National Ocean Partnership Program, the Office of Naval Research (ONR), and DoD High Performance Computing Modernization Program.
\n
\nCitations
\n
\nJ. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.
"
},
"public-global-winds-uo": {
"name": "Winds",
"description": "Winds eastward component"
},
"public-global-winds-vo": {
"name": "Winds",
"description": "Winds northward component"
},
"public-graticules": {
"name": "Latitude longitude grids",
"description": "Grids or graticules of latitude and longitude at 1, 5, 10 and 30° intervals depending on the zoom level of the map (Source: Natural Earth)."
},
"public-gs-as-simplified": {
"name": "GSAs simplified",
"description": "GSAs simplified"
},
"public-gulf-of-lion-french-zones": {
"name": "Gulf of Lion French Zones",
"description": "Gulf of lion french zones"
},
"public-high-seas": {
"name": "High seas",
"description": "The High Seas are any area of the ocean beyond Exclusive Economic Zones (EEZ). High Seas pockets are areas totally enclosed by EEZs. These pockets can be hard to distinguish from the multiple EEZ jurisdictions that surround them, thus, we have a layer that highlights them. See more detailed metadata information for this layer."
},
"public-high-seas-pockets": {
"name": "High seas pockets",
"description": " The United Nations Convention on the Law of the Sea describes the high seas as ‘all parts of the sea that are not included in the exclusive economic zone, in the territorial sea or in the internal waters of a State, or in the archipelagic waters of an archipelagic State.’ High Seas pockets are areas totally enclosed by EEZs. These pockets can be hard to distinguish from the multiple EEZ jurisdictions that surround them, thus, we have a layer that highlights them. Citation: Flanders Marine Institute (2024). Maritime Boundaries Geodatabase: High Seas, version 2. Available online at https://www.marineregions.org/.
"
},
"public-indian-ocean-isa-claim-areas": {
"name": "Indian Ocean ISA License Areas",
"description": "This layer depicts the ISA license areas for seabed mining in the Indian Ocean, which primarily contain polymetallic sulfides and polymetallic nodules. Exploration leases in this region are currently held by India, Germany, South Korea, and China.",
"schema": {
"type": "type",
"label": "label",
"contractor": "contractor"
}
},
"public-indonesia-fishing-effort": {
"name": "Indonesia VMS",
"description": "VMS data for Indonesia is not currently available for the period from July 2020.\n\nVessel monitoring system (VMS) data provided by the Indonesian Government’s Ministry of Maritime Affairs and Fisheries. Data is collected using their VMS via satellites and terrestrial receivers, and contains a vessel identities, gear type, location, speed, direction and more. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) data to identify fishing activity and behaviors. The algorithm classifies each broadcast data point for these vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch fishing activity heat map. VMS broadcasts data quite differently from AIS and may give different measures of completeness, accuracy and quality. Over time our algorithms will improve across all our broadcast data formats. Global Fishing Watch’s fishing presence algorithm for VMS, as for AIS, is a best effort to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified, or that the heat map may show apparent fishing activity where fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as “apparent,” rather than certain. Any/all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at your own risk. Global Fishing Watch fishing presence algorithms are developed and tested using actual fishing event data collected by observers, combined with expert analysis of AIS vessel movement data resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and automated classification techniques."
},
"public-indonesia-fishing-vessels": {
"name": "Indonesia VMS (Fishing Vessels)",
"description": "Dataset for VMS Indonesia (Public)"
},
"public-indonesia-pelagic-fishing-effort": {
"name": "Indonesia Pelagic (Pilot)",
"description": "Indonesia Pelagic Fishing Effort Public Data"
},
"public-indonesia-pelagic-presence": {
"name": "Indonesia Pelagic (Pilot)",
"description": "This layer of Global Fishing Watch uses data provided by Rare, Aruna and AP2HI. The data is collected using Pelagic devices that tracks location and speed. The information shown represents the vessel presence. The presence is determined by taking one position per hour transmitted by the vessel's tracking device."
},
"public-indonesia-zebrax-presence": {
"name": "Indonesia Zebrax",
"description": "This layer of Global Fishing Watch uses data provided by Rare, Aruna and AP2HI. The data is collected using devices from different providers that tracks location and speed. The information shown represents the vessel presence. The presence is determined by taking all positions transmitted by the vessel's tracking device.",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-inshore-fishing-zone-1618837176535": {
"name": "Inshore Fishing Zone",
"description": "50 nm around the Tristan Archipelago and 40nm around Gough (8% of EEZ)"
},
"public-isa-areas-contractor": {
"name": "ISA Areas by Contractor",
"description": "ISA Contractor Information
| Contractor ID | Preferred Contractor Name | Nationality |
| BGRPMN1 | Federal Institute for Geosciences and Natural Resources of Germany - PMN | Germany |
| BGRPMS1 | Federal Institute for Geosciences and Natural Resources of Germany - PMS | Germany |
| BMJPMN1 | Blue Minerals Jamaica Limited (BMJ) - PMN | Jamaica |
| BPHDCPMN1 | Beijing Pioneer Hi-Tech Development Corporation - PMN | China |
| BrazilCRFC1 | Companhia de Pesquisa de Recursos Minerais S.A. - CRFC | Brazil |
| CIICPMN1 | Cook Islands Investment Corporation - PMN | Cook Islands |
| CMMPMN1 | China Minmetals Corporation - PMN | China |
| COMRACRFC1 | China Ocean Mineral Resources Research and Development Association - CRFC | China |
| COMRAPMN1 | China Ocean Mineral Resources Research and Development Association - PMN | China |
| COMRAPMS1 | China Ocean Mineral Resources Research and Development Association - PMS | China |
| DORDPMN1 | Deep Ocean Resources Development Co. Ltd. - PMN | Japan |
| GSRPMN1 | Global Sea Mineral Resources NV - PMN | Belgium |
| IFREMERPMN1 | Institut français de recherche pour l'exploitation de la mer - PMN | France |
| IFREMERPMS1 | Institut français de recherche pour l'exploitation de la mer - PMS | France |
| IndiaPMN1 | Government of India - PMN | India |
| IndiaPMS1 | Government of India - PMS | India |
| IOMPMN1 | Interoceanmetal Joint Organization - PMN | Poland |
| KOREACRFC1 | Government of the Republic of Korea - CRFC | Korea |
| KOREAPMN1 | Government of the Republic of Korea - PMN | Korea |
| KOREAPMS1 | Government of the Republic of Korea - PMS | Korea |
| MARAWAPMN1 | Marawa Research and Exploration Ltd. - PMN | Kiribati |
| NORIPMN1 | Nauru Ocean Resources Inc. - PMN | Nauru |
| OMSPMN1 | Ocean Mineral Singapore Pte. Ltd. - PMN | Singapore |
| POLPMS1 | Government of the Republic of Poland - PMS | Poland |
| RUSFEDPMS1 | Government of the Russian Federation - PMS | Russia |
| RUSMNRCRFC1 | Ministry of Natural resources and environment of the russian federation - CRFC | Russia |
| TOMLPMN1 | Tonga Offshore Mining Limited - PMN | Tonga |
| UKSRLPMN1 | UK Seabed Resources Ltd. - I - PMN | UK |
| UKSRLPMN2 | UK Seabed Resources Ltd. - II - PMN | UK |
| YUZHPMN1 | Yuzhmorgeologiya - PMN | Russia |
",
"schema": {
"layer": {
"keyword": "Resource Area",
"enum": {
"CFC Reserved Areas": "CFC Reserved Areas",
"CFC Exploration Areas": "CFC Exploration Areas",
"PMN Reserved Areas": "PMN Reserved Areas",
"PMN Exploration Areas": "PMN Exploration Areas",
"PMS Exploration Areas": "PMS Exploration Areas"
}
},
"contract": {
"keyword": "Contractor ID",
"enum": {
"BrazilCRFC1": "BrazilCRFC1",
"BGRPMN1": "BGRPMN1",
"IFREMERPMN1": "IFREMERPMN1",
"KOREACRFC1": "KOREACRFC1",
"KOREAPMN1": "KOREAPMN1",
"JOGMECCRFC1": "JOGMECCRFC1",
"IFREMERPMS1": "IFREMERPMS1",
"IOMPMN1": "IOMPMN1",
"BPHDCPMN1": "BPHDCPMN1",
"DORDPMN1": "DORDPMN1",
"POLPMS1": "POLPMS1",
"UKSRLPMN2": "UKSRLPMN2",
"UKSRLPMN1": "UKSRLPMN1",
"YUZHPMN1": "YUZHPMN1",
"BMJPMN1": "BMJPMN1",
"RUSMNRCRFC1": "RUSMNRCRFC1",
"CIICPMN1": "CIICPMN1",
"IndiaPMS1": "IndiaPMS1",
"IndiaPMN1": "IndiaPMN1",
"OMSPMN1": "OMSPMN1",
"RUSFEDPMS1": "RUSFEDPMS1",
"CRFCReserved": "CRFCReserved",
"GSRPMN1": "GSRPMN1",
"KOREAPMS1": "KOREAPMS1",
"MARAWAPMN1": "MARAWAPMN1",
"COMRAPMS1": "COMRAPMS1",
"COMRACRFC1": "COMRACRFC1",
"CMMPMN1": "CMMPMN1",
"NORIPMN1": "NORIPMN1",
"TOMLPMN1": "TOMLPMN1",
"BGRPMS1": "BGRPMS1",
"COMRAPMN1": "COMRAPMN1",
"PMNReserved": "PMNReserved"
}
}
}
},
"public-isa-layers": {
"name": "ISA Areas by Resource Type",
"description": "This layer shows the areas managed by the International Seabed Authority across all ocean regions and seabed mineral resource types. \n\nExploration areas are those that have been licensed to States or individual contractors sponsored by a State for exploration of mineral resources under a 15-year contract. This enables the contract holder to conduct seabed surveys and environmental studies to assess the resource and region. These are the areas that could ultimately become licensed for exploitation if a State or contractor were to apply for a commercial exploitation permit. \n\nReserved areas are those that have been set aside by the ISA on behalf of developing nations to safeguard opportunity and access to these common heritage resources in the future. \n\nAreas of Particular Environmental Interest (APEI): These areas are protected from future exploitation of mineral resources to safeguard biodiversity and ecosystem health. The Regional Environmental Management Plan (REMP) of the International Seabed Authority initially established nine APEIs, but four more have been added as of December 2021. A total of thirteen APEIs have been designated by the ISA to date, protecting nearly 2 million square kilometers of the Clarion-Clipperton Zone in the Pacific Ocean. \n\nCFC Exploration Areas: Cobalt-rich ferromanganese crust resource areas licensed for exploration under the International Seabed Authority.\n\nCFC Reserved Areas: Cobalt-rich ferromanganese crust resource areas reserved under the International Seabed Authority.\n\nClarion-Clipperton Zone Management Area: This area encompasses the entire management region of the Clarion-Clipperton Zone of the Eastern Pacific Ocean, including all exploration areas, reserved areas, and areas of particular environmental interest. The CCZ spans 4.5 million square kilometers between Hawai’i and Mexico. \n\nPMN Exploration Areas: Polymetallic nodule resource areas licensed for exploration by the International Seabed Authority.\n\n\nPMN Reserved Areas: Polymetallic nodule resource areas reserved under the International Seabed Authority.\n\nPMS Exploration Areas: Polymetallic sulphide resource areas licensed for exploration by the International Seabed Authority.",
"schema": {
"label": {
"keyword": "Resource Area",
"enum": {
"CFC Reserved Areas": "CFC Reserved Areas",
"PMN Exploration Areas": "PMN Exploration Areas",
"PMS Exploration Areas": "PMS Exploration Areas",
"PMN Reserved Areas": "PMN Reserved Areas",
"Areas of particular environmental interest within the Clarion-Clipperton Zone": "Areas of particular environmental interest within the Clarion-Clipperton Zone",
"Clarion-Clipperton Zone management area": "Clarion-Clipperton Zone management area",
"CFC Exploration Areas": "CFC Exploration Areas"
}
},
"layer": {
"keyword": "layer",
"enum": {
"GIS-CCZ-Management-Areas — CCZ_Management_Areas.shp copy": "GIS-CCZ-Management-Areas — CCZ_Management_Areas.shp copy",
"ISAwebsite_APEIs — ISAwebsite_APEIs Dissolved": "ISAwebsite_APEIs — ISAwebsite_APEIs Dissolved",
"ISAwebsite_fclContractAreasCRFC — ISAwebsite_fclContractAreasCRFC Dissolved": "ISAwebsite_fclContractAreasCRFC — ISAwebsite_fclContractAreasCRFC Dissolved",
"ISAwebsite_fclContractAreasCRFCres — ISAwebsite_fclContractAreasCRFCres Dissolved": "ISAwebsite_fclContractAreasCRFCres — ISAwebsite_fclContractAreasCRFCres Dissolved",
"ISAwebsite_fclContractAreasPMN — ISAwebsite_fclContractAreasPMN Dissolved": "ISAwebsite_fclContractAreasPMN — ISAwebsite_fclContractAreasPMN Dissolved",
"ISAwebsite_fclContractAreasPMNres — ISAwebsite_fclContractAreasPMNres Dissolved": "ISAwebsite_fclContractAreasPMNres — ISAwebsite_fclContractAreasPMNres Dissolved",
"ISAwebsite_fclContractAreasPMS — ISAwebsite_fclContractAreasPMS Dissolved": "ISAwebsite_fclContractAreasPMS — ISAwebsite_fclContractAreasPMS Dissolved"
}
}
}
},
"public-location-labels": {
"name": "Location Labels",
"description": "Combination of countries, places and seas dataset labels"
},
"public-mangroves": {
"name": "Mangroves",
"description": "Overview
\n\n- Mangroves are trees or shrubs that can survive in saline environments and typically grow within the intertidal zone of tropical and subtropical regions. Considered blue carbon habitats, mangrove forests improve water quality, stabilize and protect coastlines, and provide shelter for birds, animals and marine organisms. This dataset uses earth observation satellite imagery to show the global distribution of mangroves.
\n\nSource
\n\n Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data (version 1.4, updated by UNEP-WCMC). Global Ecology and Biogeography 20: 154-159. DOI: https://doi.org/10.34892/1411-w728."
},
"public-marine-ecoregions": {
"name": "Marine ecoregions (MEOW)",
"description": "Overview
\n\n- Marine ecoregions are a biogeographic classification of the world's coastal and continental shelf waters. The dataset provides a geographic framework for a broad range of analyses relating to biodiversity in the marine environment.
\n\nSource
\n\n The Nature Conservancy.",
"schema": {
"REALM": {
"keyword": "REALM",
"enum": {
"Temperate Southern Africa": "Temperate Southern Africa",
"Temperate Northern Pacific": "Temperate Northern Pacific",
"Tropical Atlantic": "Tropical Atlantic",
"Southern Ocean": "Southern Ocean",
"Western Indo-Pacific": "Western Indo-Pacific",
"Temperate South America": "Temperate South America",
"Central Indo-Pacific": "Central Indo-Pacific",
"Temperate Northern Atlantic": "Temperate Northern Atlantic",
"Arctic": "Arctic",
"Temperate Australasia": "Temperate Australasia",
"Tropical Eastern Pacific": "Tropical Eastern Pacific",
"Eastern Indo-Pacific": "Eastern Indo-Pacific"
}
}
}
},
"public-mediterranean-area-of-interest-1": {
"name": "Area of Interest",
"description": "Area of Interest"
},
"public-mexico-fishing-effort": {
"name": "Mexico VMS",
"description": "Vessel monitoring system (VMS) data is provided by the Costa Rican Fisheries and Aquaculture Institute. Data is collected using Ecuador's vessel monitoring system via satellites and is published on a three-day delay containing information on vessels’ location, speed, course, and movement. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques",
"schema": {
"speed": "speed",
"bearing": "bearing",
"shiptype": {
"keyword": "shiptype",
"enum": {
"fishing": "fishing"
}
}
}
},
"public-mexico-fishing-vessels": {
"name": "Mexico VMS (Fishing vessels)",
"description": "Dataset for VMS Mexico (Public)",
"schema": {
"source": "source"
}
},
"public-mexico-presence": {
"name": "Mexico VMS",
"description": "This Mexico VMS data is publicly available in the Open Data portal of CONAPESCA. Data was published March 24th of 2021 and it continues being uploaded on a monthly basis.\\\\n\\\\nCONAPESCA is a decentralized agency of SAGARPA, focused on legality, quality, transparency, and in charge of promoting and developing coordination mechanisms with different agencies in order to implement policy, programs and norms that drive and facilitate competitive and sustainable development in the Mexican fisheries and aquaculture sector; which in turn will improve the quality of life of Mexicans.\\\\n\\\\nUse of VMS is mandatory for valid fishing license and/or permit holders operating on vessels with a stationary motor above 80 horsepower (equivalent to 59.68 kilowatts), a flush deck, length greater than 10.5 meters, that operate in federal jurisdiction waters within the Pacific Ocean or Gulf of Mexico and the Caribbean, operate within the Mexican Exclusive Economic Zone, or for vessel with a Mexican flag that carry out fishing activities in the open sea.\\\\n\\\\nExempt from this norm are vessels that dedicate themselves in a regular and continuous fashion to interior navigation, recreational or sport fishing, those specified by fraction XVII of Article 4 in the General Sustainable Fisheries and Aquaculture Law, and for those under the “Agreement that established criteria for the assigning and installation of a transmitting device on vessels having a gross tonnage below three hundred units and a length greater than seven meters”, published May 2nd of 2013 in the Official Journal of the Federation.",
"schema": {
"speed": "speed",
"bearing": "bearing",
"shiptype": {
"keyword": "shiptype",
"enum": {
"fishing": "fishing",
"international traffic": "international traffic",
"national traffic": "national traffic",
"tug": "tug",
"boat": "boat",
"auxiliary": "auxiliary"
}
}
}
},
"public-mexico-vessel-identity-fishing": {
"name": "Mexico VMS (Fishing vessels)",
"description": "Dataset for VMS Mexico (Public)"
},
"public-mid-atlantic-isa-claim-areas": {
"name": "Mid-Atlantic ISA License Areas",
"description": "This layer depicts the ISA license areas for seabed mining on the Mid-Atlantic Ridge, which contain polymetallic sulfides. Exploration leases in this region are currently held by France, Russia, and Poland.",
"schema": {
"type": "type",
"label": "label",
"contractor": "contractor"
}
},
"public-mp-as-mp-atlas": {
"name": "MPAs (MPAtlas)",
"description": "Marine protected areas (MPAs) are areas of the ocean set aside for long-term conservation. These can have different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably"
},
"public-mp-atlas": {
"name": "MPAs (MPAtlas)",
"description": "The MPA Guide is intended to fill a gap in existing MPA classification and assessment tools to help determine how likely MPAs are to deliver the desired conservation outcomes. Over the past few years, the MPAtlas team and key collaborators have applied The MPA Guide framework to MPAs around the world to develop a clearer picture of global marine protection, which includes a recent study establishing a baseline for global \"30x30\" targets - Publication: Ocean Protection Quality is Lagging Behind Quantity. MPAtlas is the online repository for MPA Guide assessments and associated data. Stage of Establishment represents where the MPA is in its process of being an MPA. Proposed/Committed: The intent to create an MPA is made public. Designated: MPA is established/recognized through legal means or other authoritative rulemaking. Implemented: MPA is acknowledged to be operation ‘in the water’ with plans for management activated. Actively managed: MPA management is ongoing, with monitoring, periodic review and adjustments made as needed to achieve biodiversity conservation and other ecological and social goals. Level of Protection represents the extent to which the MPA protected from seven main types of human activities and is likely to generate positive biodiversity outcomes. Fully Protected: No impact from extractive or destructive activities is allowed, and all abatable impacts are minimized. Highly Protected: Only light extractive activities are allowed that have low total impact, and all other abatable impacts are minimized. Lightly Protected: Some protection of biodiversity exists, but extractive or destructive activities that can have moderate to significant impact are allowed. Minimally Protected: Extensive extraction and other activities with high total impact are allowed, but the site can still be considered an MPA under the IUCN protected area definition and provides some conservation benefit. Some areas allow activities that have an impact so large that they are incompatible with the conservation of biodiversity, as defined by the IUCN. For more information, please visit https://mpatlas.org/. Each assessed MPA has a score card that describes its stage of establishment and level of protection, as well as more details about the components that contributed to these assessments. Note: The MPAtlas dataset does not contain boundaries for all global MPAs, only those assessed against MPA quality frameworks.
",
"schema": {
"establishment_stage": {
"keyword": "Establishment stage",
"enum": {
"proposed/committed": "proposed/committed",
"designated": "designated",
"actively managed": "actively managed",
"implemented": "implemented",
"unknown": "unknown"
}
},
"mpaguide_protection_level": {
"keyword": "Level of protection",
"enum": {
"unknown": "unknown",
"light": "light",
"full": "full",
"high": "high",
"incompatible": "incompatible",
"minimal": "minimal"
}
}
}
},
"public-mpa-all": {
"name": "MPAs (WDPA)",
"description": "Marine protected areas (MPAs) are areas of the ocean set aside for long-term conservation. These can have different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably. Source: World Database on Protected Areas. See more detailed metadata information for this layer."
},
"public-mpa-no-take": {
"name": "MPAs - No take",
"description": "The term Marine Protected Areas include marine reserves, fully protected marine areas, no-take zones, marine sanctuaries, ocean sanctuaries, marine parks, locally managed marine areas, to name a few. Many of these have quite different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably too. No Take layer was created using the data available from the Marine Protected Planet WDPA using the filter NO_TAKE = 'All'. Source: World Database on Protected Areas (WDPA)"
},
"public-mpa-no-take-partial": {
"name": "MPAs - No take Partial",
"description": "The term Marine Protected Areas include marine reserves, fully protected marine areas, no-take zones, marine sanctuaries, ocean sanctuaries, marine parks, locally managed marine areas, to name a few. Many of these have quite different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably too. Source: World Database on Protected Areas (WDPA)"
},
"public-mpa-restricted": {
"name": "MPAs - Restricted (Source: WDPA)",
"description": "The term Marine Protected Areas include marine reserves, fully protected marine areas, no-take zones, marine sanctuaries, ocean sanctuaries, marine parks, locally managed marine areas, to name a few. Many of these have quite different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably too. No Take layer was created using the data available from the Marine Protected Planet WDPA using the filter NO_TAKE = 'Part'. Source: World Database on Protected Areas (WDPA)"
},
"public-no-take-zone-1618836692786": {
"name": "Marine Protection Zone",
"description": "90% of EEZ"
},
"public-northwest-pacific-isa-claim-areas": {
"name": "Northwest Pacific ISA License Areas",
"description": "This layer depicts the ISA license areas for seabed mining in the Northwestern Pacific, which primarily contain resources from cobalt-rich ferromanganese crusts and polymetallic nodules. Several countries hold exploration leases in this region including China, Japan, Russia, and South Korea.",
"schema": {
"type": "type",
"label": "label",
"contractor": "contractor"
}
},
"public-norway-fishing-effort": {
"name": "Norway VMS",
"description": "Vessel monitoring system (VMS) data is provided by the The Norwegian Directorate of Fisheries. Data is collected using Norway’s vessel monitoring system via satellites and is published on a three-day delay containing information on vessels’ location, speed, course, and movement. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-norway-fishing-vessels": {
"name": "Norway VMS (Fishing Vessels)",
"description": "Dataset for VMS Norway (Public)",
"schema": {
"source": "source"
}
},
"public-norway-non-fishing-vessels": {
"name": "Norway VMS (Non Fishing Vessels)",
"description": "Dataset for VMS Norway (Public)",
"schema": {
"source": "source"
}
},
"public-norway-presence": {
"name": "Norway VMS",
"description": "Vessel monitoring system (VMS) data is provided by the The Norwegian Directorate of Fisheries. Data is collected using Norway's vessel monitoring system via satellites and is published on a three-day delay containing information on vessels' location, speed, course, and movement. The activity layer displays a heatmap of vessel presence. Each point in the presence layer represents a position of the vessel, but not all positions are displayed. Vessel positions are displayed once per hour. You can select a position to view the vessel's complete track.",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-norway-vessel-identity-fishing": {
"name": "Norway VMS (Fishing Vessels)",
"description": "Dataset for VMS Norway (Public)"
},
"public-norway-vessel-identity-non-fishing": {
"name": "Norway VMS (Non Fishing Vessels)",
"description": "Dataset for VMS Norway (Public)"
},
"public-paa-duke": {
"name": "PAAS",
"description": " This global Preferential Access Area (PAA) database was built with data for 44 countries identified by the Illuminating Hidden Harvests (IHH) initiative following Basurto et al. (2023) and the FAOLEX database (https://www.fao.org/faolex/en/). The total resulting PAAs identified in this study (n = 63) in 44 countries may be considered a lower bound and not comprehensive, as governments can also establish PAAs via laws or regulations either: (i) not relating to the fisheries sector and categorized elsewhere in the FAOLEX database and hence not searched, or (ii) not reported or captured in it. The most recent review of FAOLEX to identify PAAs was conducted in 2022. For mapping and spatial analysis of the PAAs, a geodatabase of PAA boundaries was created using definition parameters for each PAA (i.e., distance from shoreline or the baseline, depth, or a given set of coordinates or buffered distances around undersea features). Baselines were extracted from Flanders Marine Institute’s (2023) version 12 global EEZ database, defined by ‘Straight Baseline’ features. The shoreline dataset was ESRI’s 2014 global shoreline, the same shoreline dataset as the Flanders Marine Institute global EEZ database. Depth data was extracted from the GEBCO 2023 global relief dataset. When possible, Duke reached out to local experts to review the boundaries for confirmation that our interpretation of FAOLEX and the relevant law or regulation matches what is being used by local fishers. If you have feedback on the PAA boundaries, information regarding PAAs that are not reflected here, or other questions, please reach out to ssf-paa-info@duke.edu. For more information on PAAs and how they are managed, please see Duke’s publication by Basurto et al. (2024) here: https://doi.org/10.1038/s44183-024-00096-0. Please note that there is no singular definition of a PAA, therefore each country will decide what activities, vessels, gear types, etc. are prohibited or restricted in the PAA. For more information of the specific designation of each PAA, please see the law or regulation in FAOLex. Citation: DeLand, S., Vegh, T., Cleary, J., Basurto, X., Virdin, J., & Halpin, P. N. (2025). A global dataset of preferential access areas for small-scale fishing. Duke Research Data Repository. https://doi.org/10.7924/r40s01h5j
"
},
"public-panama-fishing-effort": {
"name": "Panama VMS",
"description": "Vessel monitoring system (VMS) data provided by the Panamanian Authority of Aquatic Resources (ARAP). Data is received by Panama’s VMS system via satellite and contains vessel identities, gear type, location, speed, direction and more. Panama’s carrier vessel data is also available here. Each point in the carrier vessel data layer represents a position of the carriers, but not all positions are displayed. Carrier vessel positions are displayed once per day. In the future, we expect to be able to display more positions. Click on a carrier vessel’s position to view the vessel’s complete track. Global Fishing Watch analyzes this data using the same algorithms we developed for automatic identification system (AIS) data to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch fishing activity heat map. VMS broadcasts data quite differently from AIS and may give different measures of completeness, accuracy and quality. Over time our algorithms will improve across all our broadcast data formats. Global Fishing Watch’s fishing presence algorithm for VMS, as for AIS, is a best effort to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified, or that the heat map may show apparent fishing activity where fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing,” and “fishing effort,” as “apparent” rather than certain. Any/all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at your own risk. Global Fishing Watch fishing presence algorithms are developed and tested using actual fishing event data collected by observers, combined with expert analysis of AIS vessel movement data resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and automated classification.",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-panama-fishing-vessels": {
"name": "Panama VMS (Public Fishing Vessels)",
"description": "Dataset for VMS Panama (Public)",
"schema": {
"source": "source"
}
},
"public-panama-non-fishing-vessels": {
"name": "Panama VMS (Public Non fishing vessels)",
"description": "Dataset for VMS Panama - Carriers (Public)",
"schema": {
"source": "source"
}
},
"public-panama-vessel-identity-fishing": {
"name": "Panama VMS (Public Fishing Vessels)",
"description": "Dataset for VMS Panama (Public)"
},
"public-panama-vessel-identity-non-fishing": {
"name": "Panama VMS (Public Non fishing vessels)",
"description": "Dataset for VMS Panama - Carriers (Public)"
},
"public-peru-fishing-effort": {
"name": "Peru VMS",
"description": "Vessel monitoring system (VMS) data provided by the Peruvian Government’s Ministry of Production, Fisheries Sector (PRODUCE). Permission to include Peruvian Data required that a 10 day delay to publishing was implemented. Data is collected using their vessel monitoring system (VMS) via satellites and terrestrial receivers, and contains a vessel’s identity gear type, location, speed, direction and more. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) data to identify fishing activity and behaviors. The algorithm classifies each broadcast data point for these vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch fishing activity heat map. VMS broadcasts data quite differently from AIS and may give different measures of completeness, accuracy and quality. Over time our algorithms will improve across all our broadcast data formats. Global Fishing Watch’s fishing presence algorithm for VMS, as for AIS, is a best effort to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified, or that the heat map may show apparent fishing activity where fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as “apparent,” rather than certain. Any/all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at your own risk. Global Fishing Watch fishing presence algorithms are developed and tested using actual fishing event data collected by observers, combined with expert analysis of AIS vessel movement data resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and automated classification techniques.",
"schema": {
"fleet": {
"keyword": "fleet",
"enum": {
"industrial": "industrial",
"artisanal": "artisanal",
"small-scale": "small-scale",
"not defined": "not defined"
}
},
"speed": "speed",
"origin": {
"keyword": "origin",
"enum": {
"PER": "PER",
"Foreign": "Foreign"
}
},
"bearing": "bearing"
}
},
"public-peru-fishing-vessels": {
"name": "Peru VMS (Fishing Vessels)",
"description": "Dataset for VMS Peru (Public)"
},
"public-peru-presence": {
"name": "Peru VMS",
"description": "Vessel monitoring system (VMS) data provided by the Peruvian Government’s Ministry of Production, Fisheries Sector (PRODUCE). Permission to include Peruvian Data required that a 10 day delay to publishing was implemented. Data is collected using their vessel monitoring system (VMS) via satellites and terrestrial receivers, and contains a vessel’s identity gear type, location, speed, direction and more. Global Fishing Watch analyzes this data using the same algorithms developed for automatic identification system (AIS) data to identify fishing activity and behaviors. The algorithm classifies each broadcast data point for these vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch fishing activity heat map. VMS broadcasts data quite differently from AIS and may give different measures of completeness, accuracy and quality. Over time our algorithms will improve across all our broadcast data formats. Global Fishing Watch’s fishing detection algorithm for VMS, as for AIS, is a best effort to algorithmically identify “apparent fishing activity.” It is possible that some fishing activity is not identified, or that the heat map may show apparent fishing activity where fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity,” “fishing” or “fishing effort,” as “apparent,” rather than certain. Any/all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at your own risk. Global Fishing Watch fishing detection algorithms are developed and tested using actual fishing event data collected by observers, combined with expert analysis of AIS vessel movement data resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and automated classification techniques.",
"schema": {
"fleet": {
"keyword": "fleet",
"enum": {
"industrial": "industrial",
"artisanal": "artisanal",
"small-scale": "small-scale",
"not defined": "not defined"
}
},
"origin": {
"keyword": "origin",
"enum": {
"PER": "PER",
"Foreign": "Foreign"
}
}
}
},
"public-peru-vessel-identity-fishing": {
"name": "Peru VMS (Fishing Vessels)",
"description": "Dataset for VMS Peru (Public)"
},
"public-pmn-exploration-areas-individual": {
"name": "PMN Exploration Areas",
"description": "Polymetallic nodules occur across abyssal plains. These nodules can be found at depths from 3000 meters to 6500 meters with high abundance in the Pacific Ocean and the Central Indian Ocean Basin. Nodules form at a rate of 1-3 millimeters per million years as metals from the surrounding water precipitate onto small particles, such as a grain of sand or a fragment of a shell, forming habitat for unique and poorly understood biodiversity. They are primarily composed of manganese, nickel, cobalt, copper, and rare earth elements. Exploration Areas under the International Seabed Authority are areas licensed for exploration of mineral resources to particular States and associated contractors. 75,000 square kilometers are allocated to each contractor for polymetallic nodule Exploration Areas.",
"schema": {
"status": "status",
"remarks": {
"keyword": "remarks",
"enum": {
"xx": "xx",
"Total exploration area of GSR (PMN) is 41,468.91606 sq.km": "Total exploration area of GSR (PMN) is 41,468.91606 sq.km",
"Total exploration area of IOM (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of IOM (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of Yuzhmorgeologiya (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of Yuzhmorgeologiya (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of Korea (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of Korea (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of COMRA (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of COMRA (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of DORD (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of DORD (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of IFREMER (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of IFREMER (PMN) is 75,000 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of India (PMN) is 75,000 sq.km, AreaKM2 was calculated by ISA": "Total exploration area of India (PMN) is 75,000 sq.km, AreaKM2 was calculated by ISA",
"Total exploration area of GSR (PMN) is 76,728 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of GSR (PMN) is 76,728 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of MARAWA (PMN) is 74,990 sq.km; AreaKM2 was calculated by ISA": "Total exploration area of MARAWA (PMN) is 74,990 sq.km; AreaKM2 was calculated by ISA",
"Total exploration area of CIIC (PMN) is 71,937 sq.km; AreaKM2 was calcualted by ISA": "Total exploration area of CIIC (PMN) is 71,937 sq.km; AreaKM2 was calcualted by ISA"
}
},
"act_date": {
"keyword": "act_date",
"enum": {
"985816800000": "985816800000",
"1717970400000": "1717970400000"
}
},
"area_key": "area_key",
"blk_cell": "blk_cell",
"blk_orig": "blk_orig",
"sub_area": "sub_area",
"area_km_2": {
"keyword": "area_km_2",
"enum": {
"74919": "74919",
"2182.876": "2182.876"
}
},
"area_type": "area_type",
"clst_orig": "clst_orig",
"cluster_id": "cluster_id",
"blk_cell_id": "blk_cell_id",
"contract_id": {
"keyword": "contract_id",
"enum": {
"KOREAPMN1": "KOREAPMN1",
"IOMPMN1": "IOMPMN1",
"DORDPMN1": "DORDPMN1",
"BMJPMN1": "BMJPMN1",
"IndiaPMN1": "IndiaPMN1",
"TOMLPMN1": "TOMLPMN1",
"IFREMERPMN1": "IFREMERPMN1",
"BGRPMN1": "BGRPMN1",
"BPHDCPMN1": "BPHDCPMN1",
"UKSRLPMN2": "UKSRLPMN2",
"UKSRLPMN1": "UKSRLPMN1",
"YUZHPMN1": "YUZHPMN1",
"CIICPMN1": "CIICPMN1",
"OMSPMN1": "OMSPMN1",
"GSRPMN1": "GSRPMN1",
"MARAWAPMN1": "MARAWAPMN1",
"CMMPMN1": "CMMPMN1",
"NORIPMN1": "NORIPMN1",
"COMRAPMN1": "COMRAPMN1"
}
}
}
},
"public-pmn-reserved-areas-individual": {
"name": "PMN Reserved Areas",
"description": "Polymetallic nodules occur across abyssal plains. These nodules can be found at depths from 3000 meters to 6500 meters with high abundance in the Pacific Ocean and the Central Indian Ocean Basin. Nodules form at a rate of 1-3 millimeters per million years as metals from the surrounding water precipitate onto small particles, such as a grain of sand or a fragment of a shell, forming habitat for unique and poorly understood biodiversity. They are primarily composed of manganese, nickel, cobalt, copper, and rare earth elements. Reserved Areas under the International Seabed Authority are a critical mechanism to ensure developing countries have access to deep-sea mineral resources in the future. These areas are typically contributed by developed States when they apply for exploration rights.",
"schema": {
"status": "status",
"remarks": {
"keyword": "remarks",
"enum": {
"Origin:INDIA": "Origin:INDIA",
"Origin:COMRA": "Origin:COMRA",
"Origin:Yuzhmorgeologia": "Origin:Yuzhmorgeologia",
"Origin:DORD": "Origin:DORD",
"Origin:IFREMER": "Origin:IFREMER",
"Origin:Government of the Republic of Korea": "Origin:Government of the Republic of Korea",
"Origin:IOM": "Origin:IOM",
"Origin:BGR": "Origin:BGR",
"Origin:UKSRL-ll": "Origin:UKSRL-ll"
}
},
"act_date": {
"keyword": "act_date",
"enum": {
"988322400000": "988322400000",
"1617487200000": "1617487200000"
}
},
"area_key": "area_key",
"blk_cell": "blk_cell",
"blk_orig": {
"keyword": "blk_orig",
"enum": {
"983401200000": "983401200000",
"986076000000": "986076000000"
}
},
"sub_area": "sub_area",
"area_km_2": {
"keyword": "area_km_2",
"enum": {
"150000": "150000",
"1032.952": "1032.952"
}
},
"area_type": "area_type",
"clst_orig": "clst_orig",
"cluster_id": "cluster_id",
"blk_cell_id": "blk_cell_id",
"contract_id": {
"keyword": "contract_id",
"enum": {
"BPHDCPMN1": "BPHDCPMN1",
"PMNReserved": "PMNReserved"
}
}
}
},
"public-pms-exploration-areas-individual": {
"name": "PMS Exploration Areas",
"description": "Polymetallic sulphides, or seafloor massive sulphides, are formed at hydrothermal vents near mid-ocean ridges and back-arc basins between approximately 1,000 and 4,000 meters in depth. They are formed when superheated, mineral-rich fluids from the Earth’s mantle are rapidly cooled by deep water. Rapid cooling results in dissolved metals in the fluid precipitating as metal sulfides. These sulfides and surrounding polymetallic muds can contain copper, zinc, silver, and gold. For polymetallic sulphides, the exploration area allocated to each contractor is 10,000 square kilometres and consists of 100 blocks. Each block is no greater than 100 square kilometres.",
"schema": {
"status": "status",
"act_date": {
"keyword": "act_date",
"enum": {
"1474840800000": "1474840800000",
"1698098400000": "1698098400000"
}
},
"area_key": "area_key",
"blk_cell": "blk_cell",
"blk_orig": {
"keyword": "blk_orig",
"enum": {}
},
"sub_area": "sub_area",
"area_km_2": {
"keyword": "area_km_2",
"enum": {
"1": "1",
"101": "101"
}
},
"area_type": "area_type",
"clst_orig": {
"keyword": "clst_orig",
"enum": {
"A": "A",
"B": "B",
"C": "C",
"D": "D",
"E": "E"
}
},
"cluster_id": {
"keyword": "cluster_id",
"enum": {
"NR": "NR",
"A": "A",
"B": "B",
"C": "C",
"D": "D",
"E": "E",
"NA": "NA"
}
},
"blk_cell_id": {
"keyword": "blk_cell_id",
"enum": {}
},
"contract_id": {
"keyword": "contract_id",
"enum": {
"KOREAPMS1": "KOREAPMS1",
"COMRAPMS1": "COMRAPMS1",
"IFREMERPMS1": "IFREMERPMS1",
"POLPMS1": "POLPMS1",
"RUSFEDPMS1": "RUSFEDPMS1",
"BGRPMS1": "BGRPMS1",
"IndiaPMS1": "IndiaPMS1"
}
}
}
},
"public-png-fishing-effort": {
"name": "Papua New Guinea VMS",
"description": "Vessel monitoring system (VMS) data is provided by the The National Fisheries Authority of Papua New Guinea. Data is collected using Papua New Guinea's vessel monitoring (VMS) system via satellites, that contains vessel's identifiers and location, and is published on a five-day delay. Global Fishing Watch infers speed and course for each vessel location and analyzes this data using the same algorithms developed for automatic identification system (AIS) to identify fishing activity and behaviors. The algorithm classifies each broadcast data point from vessels as either apparently fishing or not fishing and shows the former on the Global Fishing Watch’s fishing activity heat map. VMS broadcasts data differently from AIS and may give different measures of completeness, accuracy, and quality. Global Fishing Watch is continually improving its algorithms across all broadcast data formats to algorithmically identify “apparent fishing activity”. It is possible that some fishing activity is not identified or that the heat map may show apparent fishing activity when fishing is not actually taking place. For these reasons, Global Fishing Watch qualifies the terms “fishing activity”, “fishing” or “fishing effort”, as apparent rather than certain. Any and all Global Fishing Watch information about “apparent fishing activity” should be considered an estimate and must be relied upon solely at the user’s discretion. Global Fishing Watch’s fishing detection algorithms are developed and tested using actual fishing event data collected by observers and is combined with expert analysis of AIS vessel movement data, resulting in the manual classification of thousands of known fishing events. Global Fishing Watch also collaborates extensively with academic researchers through our research program to share fishing activity classification data and to improve automated classification techniques",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-png-fishing-vessels": {
"name": "Papua New Guinea VMS (Fishing Vessels)",
"description": "Dataset for VMS Papua New Guinea (Public)",
"schema": {
"source": "source"
}
},
"public-png-presence": {
"name": "Papua New Guinea VMS",
"description": "Vessel monitoring system (VMS) data is provided by the The National Fisheries Authority of Papua New Guinea. Data is collected using Papua New Guinea's national VMS that is provided by the Fisheries Information and Management System (FIMS). VMS data includes vessel identifiers and location, and is published with a five-day delay.\n\nThe activity layer displays a heatmap of vessel presence. The presence is determined by taking two positions per hour per vessel from the positions transmitted by the vessel's VMS.",
"schema": {
"speed": "speed",
"bearing": "bearing"
}
},
"public-png-vessel-identity-fishing": {
"name": "Papua New Guinea VMS (Fishing Vessels)",
"description": "Dataset for VMS Papua New Guinea (Public)"
},
"public-ports": {
"name": "Ports",
"description": "Named ports",
"schema": {
"name": "name",
"port_id": "port_id"
}
},
"public-ports-v1": {
"name": "Ports",
"description": "Named ports v1"
},
"public-presence-viirs-match-prototype": {
"name": "VIIRS",
"description": "The night lights vessel detections layer, known as visible infrared imaging radiometer suite or VIIRS, shows vessels at sea that satellites have detected by the light they emit at night. Though not exclusively associated with fishing vessels, this activity layer is likely to show vessels associated with activities like squid fishing, which use bright lights and fish at night.
\n
\nBased on the Suomi NPP satellite, the VIIRS sensor makes a pass across the entire planet at least once every night, detecting lights to provide at least one daily observation globally. Due to the orbit design of polar orbiting satellites, regions closer to polar will have more over-passes per day, while equatorial regions have only one over-pass daily.
\n
\nBecause the vessels are detected solely based on light emission, we can detect individual vessels and even entire fishing fleets that may not broadcast identity information and so may not be represented elsewhere on the Global Fishing Watch map. Global Fishing Watch ingests boat detections processed from low light imaging data collected by the U.S. National Oceanic and Atmospheric Administration (NOAA) VIIRS. The boat detections are processed in near-real time by the Earth Observation Group at the Colorado School of Mines. The data, known as VIIRS boat detections, picks up the presence of vessels, including those fishing using lights to attract catch or to conduct operations at night.Lights from fixed offshore infrastructure and other non-vessel sources are excluded. Read more about VIIRS night light vessel detections, and download the data.
\n
\nGlobal Fishing Watch has developed a sophisticated system to match available automatic identification system (AIS) data to respective night light vessel detections. This matching is done using a probabilistic model that determines AIS-message/VIIRS-detection pairs based on all available AIS records right before and right after the time the satellite VIIRS image was taken, as well as the probability of pairing a specific AIS message to any of the vessels appearing on that image. Using this information, Global Fishing Watch has added the experimental ability to filter detections based on vessel type and gear type within the VIIRS activity layer.
\n
\nMore than 85% of the detections are from vessels that lack AIS or publicly shared vessel monitoring system (VMS) transponders. The global addition of the VIIRS layer enables you to rapidly filter the night light detections that either were matched or not with AIS where vessel identification is available.
\n
\nRadiance indicates the brightness of the light source received by the VIIRS sensor. Radiance is impacted by the moon, clouds, and the angle of the vessel from the satellite. Two vessels with the same brightness, or light intensity, may have different radiance levels depending on the conditions. In general, vessels that are not actively fishing using light may have lower radiance levels. Exceptions should be considered when vessels are approaching a coastline. To further explore how vessel lights at night emit different radiance levels, the VIIRS activity layer can be filtered to specific ranges of interest associated with different human behaviours.
\n
\nThose using night light detections data should consider the South Atlantic Anomaly (SAA), an area where the Earth's inner Van Allen radiation belt is at its lowest altitude, allowing more energetic particles from space to penetrate. When such particles hit the sensors on a satellite, this can create a false signal which might cause the algorithm to recognize it as a boat detection. A filtration algorithm has been applied but there may still be some mis-identification",
"schema": {
"source": {
"keyword": "source",
"enum": {
"unknown": "unknown",
"chile_vms_aquaculture": "chile_vms_aquaculture",
"peru.trasat_api": "peru.trasat_api",
"namibia_vms": "namibia_vms",
"indovms": "indovms",
"mexico_vms": "mexico_vms",
"chile_vms_small_fisheries": "Small fisheries",
"peru.historic": "peru.historic",
"chile_vms_industry": "Industry",
"ais_scored": "ais_scored",
"AIS": "AIS",
"panama_vms": "panama_vms",
"chile_vms_transport": "chile_vms_transport"
}
},
"matched": {
"keyword": "Matching",
"enum": {
"true": "1",
"false": "false"
}
},
"radiance": {
"keyword": "Radiance",
"enum": {
"1": "1",
"1000": "1000"
}
},
"shiptype": {
"keyword": "shiptype",
"enum": {
"unknown": "unknown",
"fishing": "fishing",
"carrier": "Carrier",
"support": "Support"
}
},
"qf_detect": {
"keyword": "qf_detect",
"enum": {
"1": "1",
"2": "2",
"3": "3",
"5": "5",
"7": "7",
"10": "10"
}
}
}
},
"public-protectedseas": {
"name": "MPAs (ProtectedSeas)",
"description": "Marine protected areas (MPAs) are areas of the ocean set aside for long-term conservation. These can have different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably. Source: ProtectedSeas Navigator. ProtectedSeas assigns a Level of Fishing Protection (LFP) score to each area based on an analysis of restrictions on marine life extraction. You can use the filter icon to explore the different LFP scores, which are, Least restrictive: No known fishing restrictions; Less restrictive: Few species- or gear-specific restrictions apply; Moderately restrictive: Several species- or gear-specific restrictions apply; Heavily restrictive: Fishing is mostly prohibited, with few exceptions; Most restrictive: Fishing is prohibited. See more detailed metadata information for this layer. ",
"schema": {
"removal_of": {
"keyword": "Level of Fishing Protection",
"enum": {
"1": "1. Least restrictive",
"2": "2. Less restrictive",
"3": "3. Moderately restrictive",
"4": "4. Heavily restrictive",
"5": "5. Most restrictive"
}
}
}
},
"public-protectedseas-all": {
"name": "Protected Seas all",
"description": "Protected seas all",
"schema": {
"category_name": {
"keyword": "category_name",
"enum": {
"IUCN MPA": "IUCN MPA",
"Other": "Other",
"Jurisdictional Authority Area": "Jurisdictional Authority Area",
"TBD": "TBD",
"Fisheries Management Area": "Fisheries Management Area",
"Recreational Area": "Recreational Area",
"OECM": "OECM",
"Vessel Restricted Area": "Vessel Restricted Area",
"Voluntary Conservation Measure Area": "Voluntary Conservation Measure Area",
"Vessel Reporting Area": "Vessel Reporting Area",
"Water Quality/Human Health Area": "Water Quality/Human Health Area"
}
},
"removal_of_marine_life_is_prohibited": {
"keyword": "Level of Fishing Protection",
"enum": {
"1": "1. Least restrictive",
"2": "2. Less restrictive",
"3": "3. Moderately restrictive",
"4": "4. Heavily restrictive",
"5": "5. Most restrictive"
}
}
}
},
"public-rfmo": {
"name": "RFMOs",
"description": "Regional fisheries management organizations (RFMOs) are international bodies formed by countries with a shared interest in managing or conserving fish stocks in a particular region. Some manage all the fish stocks found in a given area, while others focus on specific highly migratory species, notably tuna. The regional fisheries management organization on the Global Fishing Watch map currently includes all regional fishery bodies (RFB) areas provided by the FAO Geonetwork along with the convention areas provided by the five tuna RFMOs. See more detailed metadata information for this layer.",
"schema": {
"ID": {
"keyword": "RFMO",
"enum": {
"APFIC": "APFIC - Asia-Pacific Fishery Commission",
"BOBP-IGO": "BOBP-IGO - Bay of Bengal Programme – Intergovernmental Organization",
"CCAMLR": "CCAMLR - Convention on Conservation of Antarctic Marine Living Resources",
"CCBSP": "CCBSP - Convention on the Conservation and Management of Pollock Resources in the Central Bering Sea",
"CCSBT": "CCSBT - Commission for the Conservation of Southern Bluefin Tuna",
"CCSBT Primary Area": "CCSBT Primary Area",
"COREP": "COREP - Commission Régionale des Pêches du Golfe de Guinée",
"CPPS": "CPPS - Permanent Commission for the South Pacific",
"CRFM": "CRFM - Caribbean Regional Fisheries Mechanism",
"CTMFM": "CTMFM - Joint Technical Commission for the Argentina/Uruguay Maritime Front",
"FCWC": "FCWC - Fisheries Committee for the West Central Gulf of Guinea",
"FFA": "FFA - Pacific Islands Forum Fisheries Agency",
"GFCM": "GFCM - General Fisheries Commission for the Mediterranean",
"IATTC": "IATTC - Inter-American Tropical Tuna Commission",
"ICCAT": "ICCAT - International Commission for the Conservation of Atlantic Tunas",
"ICES": "ICES - International Council for the Exploration of the Sea",
"IOTC": "IOTC - Indian Ocean Tuna Commission",
"IPHC": "IPHC - International Pacific Halibut Commission",
"LTA": "LTA - Lake Tanganyika Authority",
"NAFO": "NAFO - Northwest Atlantic Fisheries Organization",
"NAMMCO": "NAMMCO - North Atlantic Marine Mammal Commission",
"NASCO": "NASCO - North Atlantic Salmon Conservation Organisation",
"NEAFC": "NEAFC - North-East Atlantic Fisheries Commission",
"NPAFC": "NPAFC - North Pacific Anadromous Fish Commission",
"NPFC": "NPFC - North Pacific Fisheries Commission",
"OSPESCA": "OSPESCA - Central America Fisheries and Aquaculture Organization",
"PERSGA": "PERSGA - The Regional Organization for the Conservation of the Environment of the Red Sea & Gulf of Aden",
"PICES": "PICES - North Pacific Marine Science Organization",
"RECOFI": "RECOFI - Regional Commission for Fisheries",
"SEAFDEC": "SEAFDEC - Southeast Asian Fisheries Development Center",
"SIOFA": "SIOFA - Southern Indian Ocean Fisheries Agreement",
"SPC": "SPC - The Pacific Community",
"SPRFMO": "SPRFMO - South Pacific Regional Fisheries Management Organisation",
"SRFC": "SRFC - Sub-Regional Fisheries Commission",
"SWIOFC": "SWIOFC - South West Indian Ocean Fisheries Commission",
"WCPFC": "WCPFC - Western and Central Pacific Fisheries Commission"
}
}
}
},
"public-seagrasses": {
"name": "Seagrasses",
"description": "Overview
\n\n- Seagrasses are a productive ecosystem found globally in shallow marine areas where they provide food and habitat for organisms and play a key role in nutrient cycling. This dataset shows the global distribution of seagrasses.
\n\nSource
\n\n UNEP-WCMC, Short FT (2021). Global distribution of seagrasses (version 7.1). Seventh update to the data layer used in Green and Short (2003). Cambridge (UK): UN Environment World Conservation Monitoring Centre. Data DOI: https://doi.org/10.34892/x6r3-d211. ",
"schema": {
"GENUS": {
"keyword": "GENUS",
"enum": {}
},
"FAMILY": {
"keyword": "FAMILY",
"enum": {}
},
"habitat": {
"keyword": "habitat",
"enum": {}
},
"BIO_CLASS": {
"keyword": "BIO_CLASS",
"enum": {}
}
}
},
"public-seamounts": {
"name": "Seamounts",
"description": "Overview
\n\n- Seamounts are underwater mountains of volcanic origin that can generate an upwelling of nutrients, supporting increased biological productivity, species richness and distinct communities. This dataset of global seafloor geomorphic features includes seamounts, which are defined as peaks that rise over 1,000 m above the seafloor.
\n\nSource
\n\n Blue Habitats. Seafloor Geomorphic Features Map by Harris, P.T., Macmillan-Lawler, M., Rupp, J. and Baker, E.K. 2014. Geomorphology of the oceans. Marine Geology, 352: 4-24.",
"schema": {
"Height": {
"keyword": "Height",
"enum": {
"0": "0",
"8000": "8000"
}
}
}
},
"public-south-atlantic-isa-claim-areas": {
"name": "South Atlantic ISA License Areas",
"description": "This layer depicts the ISA license areas for seabed mining in the South Atlantic Ocean, which contain resources cobalt-rich ferromanganese crusts. Brazil is currently the only country holding leases in this region.",
"schema": {
"label": "label",
"contractor": "contractor"
}
},
"public-tristan-seamounts-200-1618586314138": {
"name": "Depth: -200 m",
"description": "Depth: -200 m"
},
"public-tristan-seamounts-3000-1618586349746": {
"name": "Depth: -3000 m",
"description": "Depth: -3000 m"
},
"public-tristan-seamounts-existing-1618586378121": {
"name": "Existing Seamount Fishing Zones",
"description": "2% of EEZ"
},
"public-tuna-rfmo": {
"name": "RFMOs",
"description": "Regional fisheries management organizations (RFMOs) are international bodies formed by countries with a shared interest in managing or conserving fish stocks in a particular region. Some manage all the fish stocks found in a given area, while others focus on specific highly migratory species, notably tuna. The regional fisheries management organization on the Global Fishing Watch map currently includes the five tuna regional fisheries management organizations. See more detailed metadata information for this layer."
},
"public-wdpa-may-2021-marine": {
"name": "MPAs (Source: WDPA)",
"description": "Marine protected areas (MPAs) are areas of the ocean set aside for long-term conservation. These can have different levels of protection, and the range of activities allowed or prohibited within their boundaries varies considerably. Source: World Database on Protected Areas. Last updated: May 2021."
},
"public-wpp-nri": {
"name": "WPP NRI",
"description": "The WPP-NRI (Wilayah Pengelolaan Perikanan Negara Republik Indonesia) are fisheries management areas for fishing, conservation, research and fisheries development which cover inland waters, archipelagic waters, and territorial seas within and outside the exclusive economic zone of Indonesia."
}
}