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.
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
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.
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.
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.
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.
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.
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
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.
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-fixed-infrastructure-filtered": { "name": "Infraestructura fija offshore (SAR, Óptica)", "description": "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.
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
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.
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.
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.
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.
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.
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
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.
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": "Trayectorias", "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": "fuente" } }, "public-global-bathymetry": { "name": "Batimetría", "description": "Global Fishing Watch classifies an event as an encounter when two vessels are detected:
Users can filter encounter events by vessel characteristics and context, including:
Currently displayed encounter types include:
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.
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": "Tipo de embarcaciones", "enum": { "CARRIER-FISHING": "Embarcación de transporte <> Pesca", "SUPPORT-FISHING": "Soporte <> Pesca", "FISHING-CARRIER": "Pesca <> Embarcación de transporte", "FISHING-BUNKER": "Pesca <> Aprovisionamiento", "FISHING-FISHING": "Pesca <> Pesca", "CARRIER-BUNKER": "Embarcación de transporte <> Aprovisionamiento", "FISHING-SUPPORT": "Fishing <> Soporte" } } } }, "public-global-fishing-effort": { "name": "AIS", "description": "Global Fishing Watch utiliza datos sobre la identidad, el tipo, la ubicación, la velocidad, y otros datos que son emitidos a través del Sistema de Identificación Automática (AIS) y recolectados a través de satélites y receptores terrestres. El AIS fue desarrollado para la prevención de colisión/esquivación. Global Fishing Watch analiza los datos AIS recopilados de los buques que nuestra investigación ha identificado como conocidos o posibles buques pesqueros comerciales, y aplica un algoritmo de detección de pesca para determinar una “aparente actividad pesquera” basado en cambios en la velocidad y dirección de los buques. El algoritmo clasifica cada punto de datos para estos buques como pesca aparente o sin actividad pesquera y muestra el primero en el mapa de calor de la actividad pesquera de Global Fishing Watch. Los datos AIS pueden variar en integridad, precisión y calidad. Además, la recolección de datos por parte de receptores satelitales o terrestres, puede introducir errores a través de datos faltantes o inexactos. El algoritmo de detección de pesca de Global Fishing Watch representa un esfuerzo matemático para identificar una “aparente actividad pesquera”. Como resultado, es posible que algunas actividades pesqueras no sean identificadas como tales por Global Fishing Watch. Por el contrario, Global Fishing Watch puede mostrar una actividad pesquera aparente en la que la pesca no se está llevando a cabo. Por estas razones, Global Fishing Watch califica “actividad pesquera”,“pesca” o “esfuerzo pesquero” como “aparente”, en lugar de brindar certeza sobre la actividad. En todo o en parte, la información de Global Fishing Watch sobre “aparente actividad de pesca” debe ser considerada una estimación y debe ser confiada únicamente bajo su propio riesgo. Global Fishing Watch está tomando medidas para asegurar que la identificación del comportamiento de la actividad pesquera sea lo más exacta posible. Los algoritmos de detección de pesca de Global Fishing Watch son desarrollados y probados usando datos reales de eventos de pesca recopilados por observadores, combinado con el análisis experto de los datos del movimiento de buques que dan lugar a la clasificación manual de miles de eventos de pesca conocidos. Global Fishing Watch también colabora ampliamente con investigadores académicos a través de nuestro programa de investigación para compartir datos de clasificación de actividades pesqueras y técnicas de clasificación automatizadas.", "schema": { "speed": "speed", "bearing": "bearing", "distance_from_port_km": { "keyword": "Búfer del puerto", "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 (Buques de pesca)", "description": "Vessels from AIS", "schema": { "source": "fuente" } }, "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": "A la deriva (AIS)", "description": "Global Fishing Watch classifies an event as a loitering event when:
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": "Tipo de embarcaciones", "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": "Concentración de nitrato (NO3)", "description": "Global Fishing Watch detects vessel movements in and out of ports and classifies them into four distinct port event types:
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:
Port stops are used to distinguish actual port visits from coastal transits.
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": "Tipo de embarcaciones", "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 utiliza datos sobre la identidad, el tipo, la ubicación, la velocidad, y otros datos que son emitidos a través del Sistema de Identificación Automática (AIS) y recolectados a través de satélites y receptores terrestres. El AIS fue desarrollado para la prevención de colisión/esquivación. La capa de actividad muestra un mapa de calor de la presencia de buques. La presencia se determina tomando una posición al hora por buque a partir de las posiciones transmitidas por el AIS de la buque.", "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": "Salinidad", "description": "
Satellite 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.
\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 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.
\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.
\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.
\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”).
\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:
\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.
\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.
\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.
\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
\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
\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": "Coincidencia con AIS vessels", "enum": { "true": "Coincidencia con AIS", "false": "Sin coincidencia con AIS" } }, "shiptype": { "keyword": "shiptype", "enum": { "carrier": "carrier", "seismic_vessel": "seismic_vessel", "passenger": "passenger", "other": "other", "support": "support", "bunker": "bunker", "gear": "Arte", "cargo": "cargo", "fishing": "fishing", "discrepancy": "discrepancy" } }, "neural_vessel_type": { "keyword": "neural_vessel_type", "enum": { "Likely non-fishing": "Likely non-fishing", "Likely Fishing": "Likely Fishing", "Unknown": "Desconocido" } } } }, "public-global-sentinel2-footprints": { "name": "Zonas de detección", "description": "Zonas de detección" }, "public-global-sentinel2-presence": { "name": "Detecciones de imágenes (Ópticas)", "description": "
Esta capa muestra embarcaciones detectadas mediante imágenes satelitales ópticas recopiladas por los satélites Sentinel-2 de la Agencia Espacial Europea. Las imágenes ópticas son similares a fotografías aéreas de alta calidad tomadas desde el espacio, utilizando la luz solar reflejada en longitudes de onda visibles y del infrarrojo cercano. Este tipo de imágenes ofrece un nivel de detalle de alta resolución que permite identificar embarcaciones pequeñas, observar patrones en la estela de la embarcación, así como comprender mejor la actividad cercana a la costa.
\nGlobal Fishing Watch utiliza un modelo de aprendizaje automático que procesa cada imagen para identificar embarcaciones y estimar su longitud, orientación y velocidad a partir de las características de su estela. Luego, las detecciones se filtran mediante una clasificación secundaria que elimina elementos que no corresponden a embarcaciones, como nubes, rocas o icebergs. Cada detección está vinculada a una imagen recortada (o miniatura) para que los usuarios puedan inspeccionar visualmente lo que el modelo ha identificado.
\nDado que los satélites ópticos dependen de la luz solar y del cielo despejado, las detecciones solo son posibles durante el día y cuando el área no está cubierta por nubes o neblina. A pesar de estas limitaciones, las detecciones con imágenes ópticas son especialmente útiles para identificar embarcaciones pequeñas y no rastreadas que podrían no aparecer en otros sistemas de seguimiento.
\nMonitorear la presencia de embarcaciones (tanto pesqueras como no pesqueras) en zonas de interés como Áreas Marinas Protegidas (AMP), Zonas Económicas Exclusivas (ZEE), zonas de exclusión costera (IEZ, por sus siglas en inglés) y organizaciones regionales de ordenación pesquera (OROP). En algunos casos, actividades como la pesca de arrastre de fondo pueden ser identificadas por la alteración de los sedimentos del lecho marino.
\nEvaluar la presencia de embarcaciones que no aparecen en sistemas de seguimiento cooperativo —incluidos el sistema de identificación automática (AIS) y el sistema de monitoreo de embarcaciones (VMS)— cerca de ecosistemas marinos vulnerables y hábitats esenciales para peces.
\nApoyar el análisis de la pesca en pequeña escala. Aunque la resolución de 10 metros aún es demasiado baja para cartografiar completamente esta actividad, las detecciones del Sentinel-2 se han integrado en varios análisis regionales y han demostrado su potencial como un valioso complemento a los limitados datos disponibles sobre el seguimiento de embarcaciones.
\nDetecciones de embarcaciones mediante imágenes ópticas requieren luz diurna y cielos despejados.\n
\nA diferencia del radar, los satélites ópticos no pueden atravesar nubes, niebla o bruma. Las detecciones solo son posibles durante las horas de luz solar y cuando la visibilidad no está obstruida.\n
\nLa cobertura geográfica no es uniforme.\n
\nLa cobertura del Sentinel-2 se limita principalmente a aguas costeras. Revisa la mayoría de las zonas cada cinco días, pero la disponibilidad de las imágenes depende de las condiciones meteorológicas. Las regiones con nubosidad o bruma frecuentes son visitadas con menor frecuencias que aquellas con mejor clima.\n
\nLas detecciones pueden incluir falsos positivos\n
\nA pesar del posprocesamiento, el modelo puede generar detecciones erróneas ocasionales, como boyas, residuos, infraestructura fija o artefactos de imagen. Estos falsos positivos se reducen mediante un clasificador secundario, pero no serán eliminados por completo\n
\nIncertidumbre sobre algunas características de las embarcaciones\n
\nLas embarcaciones más pequeñas o de movimiento lento pueden no dejar una estela visible, lo que dificulta estimar su velocidad o rumbo. Por lo tanto, estos valores podrían ser imprecisos en el caso de las embarcaciones pequeñas.\n
\nNo todas las detecciones sin coincidencia con AIS son a embarcaciones no rastreadas\n
\nLas detecciones incluyen tanto embarcaciones con AIS como sin él. Se intenta cotejar las detecciones con trayectorias AIS, pero esto no siempre es posible debido a grandes intervalos entre posiciones AIS o a la alta densidad de embarcaciones detectadas.\n
\nEsta capa se basa en imágenes de los satélites Sentinel-2, operados por la Agencia Espacial Europea (ESA). Estos satélites capturan imágenes del océano con resolución media (10 metros por píxel), utilizando luz visible y del infrarrojo cercano, entre otras bandas. En conjunto, los satélites cubren la mayoría de las aguas costeras y ciertas zonas del mar abierto aproximadamente cada cinco días. Las imágenes están disponibles de forma gratuita a través de la ESA.
\nUtilizamos imágenes preprocesadas de Sentinel-2 que han sido corregidas debido a distorsiones geométricas y alineadas con la superficie terrestre. Estas imágenes se dividen en recuadros manejables y hemos elegido los recuadros que cubren únicamente zonas oceánicas (se excluyen áreas terrestres). Utilizamos cuatro bandas espectrales: rojo, verde, azul (RGB) y el infrarrojo cercano (NIR), todas con una resolución de 10 metros. Estas bandas nos proporcionan el detalle y contraste necesarios para detectar y clasificar embarcaciones.
\nNuestro modelo de aprendizaje automático escanea cada recuadro con el objetivo de detectar embarcaciones. Ha sido entrenado para buscar características como la forma, el brillo y la estela de una embarcación. Cuando encuentra un candidato probable, el modelo estima una puntuación de presencia del buque junto con estimaciones de su ubicación, tamaño, orientación y velocidad.
\nEl modelo fue entrenado utilizando más de 11.000 ejemplos de embarcaciones revisados manualmente en miles de escenas Sentinel-2. Este entrenamiento incluyó muchas embarcaciones pequeñas y escenas de todo el mundo, lo que le permite desempeñarse bien en distintos entornos y con diversos tipos de buques.
\nCada detección incluye una pequeña “muestra visual” con la embarcación detectada en el centro. Estas miniaturas están disponibles en dos formatos: una versión en color basada en las bandas RGB, y una versión en escala de grises basada en la banda del infrarrojo cercano. Cada muestra visual cubre un área de 1 km². Estas miniaturas son útiles para confirmar visualmente una detección o comprender su contexto. Sin embargo, en el caso de embarcaciones muy pequeñas (menores a 15 metros), es posible que sea difícil verlas con claridad.
\nNo todo lo que parece una embarcación en una imagen satelital realmente lo es. Para ayudar a eliminar detecciones erróneas (como boyas, plataformas marinas, hielo marino o nubes), cada detección pasa por un clasificador secundario. Este clasificador —también basado en aprendizaje automático— utiliza tanto la miniatura de la imagen como información adicional sobre la detección (como, por ejemplo, la distancia de la costa, profundidad local y densidad de embarcaciones cercanas) para evaluar si el objeto probablemente es una embarcación.
\nAdemás, marcamos las detecciones que se encuentren cerca de infraestructuras fijas conocidas o en zonas con presencia significativa de hielo marino o icebergs. Si una detección se clasifica como “probablemente no embarcación” o se marca como posible infraestructura o hielo, se elimina de la capa del mapa, de modo que solo incluye detecciones con alta probabilidad. Asimismo, recortamos los límites de las escenas satelitales mostradas en el mapa para excluir las áreas cubiertas por la máscara de regiones heladas.
\nSin embargo, todos los falsos positivos se proporcionan con etiquetas a través del portal de descarga de datos, para los usuarios que necesiten conjuntos de datos más completos.
\nLos datos del sistema AIS pueden revelar la identidad de una embarcación, sus propietarios, operadores y su actividad pesquera. Sin embargo, no todas las embarcaciones están obligadas a usar dispositivos AIS, ya que las regulaciones varían según el país, el tamaño del buque y su actividad. Además, embarcaciones involucradas en actividades ilícitas pueden apagar sus transpondedores AIS o manipular las ubicaciones que transmiten.
\nTambién existen grandes “zonas ciegas” en aguas costeras, debido a países que restringen el acceso a los datos AIS captados por receptores terrestres (en lugar de satelitales), o a una mala recepción provocada por alta densidad de embarcaciones o equipos de baja calidad. Por este motivo, las detecciones no cotejadas con AIS ofrecen la información faltante del tráfico marítimo.
\nCotejar las detecciones con coordenadas GPS del AIS es un desafío, ya que las marcas de tiempo de las imágenes y de los registros AIS no coinciden, y una sola identidad AIS puede corresponder a múltiples embarcaciones visibles en una imagen, o viceversa. Para determinar la probabilidad de que una embarcación con señal AIS coincida con una detección específica, hemos desarrollado un enfoque basado en rásters de probabilidad que indica dónde es probable que se encuentre una embarcación minutos antes y después de una posición AIS registrada.
\nEstos rásters fueron producidos a partir de un año de datos globales AIS en el flujo de datos de Global Fishing Watch, que obtiene datos satelitales de Spire Global y Orbcomm. Los rásters de probabilidad se basan en aproximadamente 10.000 millones de posiciones de embarcaciones y se calculan para seis clases de buques, considerando seis velocidades distintas y 36 intervalos de tiempo. Esto permite estimar la posición probable de una embarcación que podría coincidir con una detección, en función de su clase, velocidad e intervalo temporal.
\nAdemás del cotejamiento espaciotemporal, también se toma en cuenta la similitud entre la eslora inferida por el modelo y la eslora reportada en el AIS, con el fin de evitar coincidencias (probablemente erróneas) con grandes diferencias de tamaño, como asociar el AIS de un remolcador con la detección de un buque grande que le sigue.
\nPara ayudar a los usuarios a entender dónde eran posibles las detecciones, mostramos las “huellas de detección” en el mapa. Estos polígonos corresponden a las porciones de las imágenes satelitales que cubren zonas oceánicas y que se utilizan para detección. Por lo tanto, si encuentra una huella sin detecciones, significa que no se detectaron embarcaciones en esa área. Si no hay huella, no se procesó ninguna imagen para ese lugar y momento.
\nNuestro sistema de detección y cotejamiento funciona de forma automatizada cada día. Verifica si hay nuevas imágenes Sentinel-2 publicadas en Google Cloud y procesa las que cumplan con nuestros criterios de calidad. Las nuevas detecciones suelen estar disponibles entre 1 y 2 días después de que el satélite captura la imagen. El flujo automático también vuelve a revisar cualquier imagen publicadas con retraso, para asegurar que cualquier brecha en los datos sea completada.
\nTodos los datos de embarcaciones están disponibles gratuitamente a través del portal de datos de Global Fishing Watch https://globalfishingwatch.org/data-download.
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", "schema": { "length": { "keyword": "Longitud de la embarcación", "enum": { "<20": "<20", "20-60": "20-60", "60-100": "60-100", ">100": ">100" } }, "bearing": "Rumbo", "matched": { "keyword": "Coincidencia con AIS", "enum": { "true": "Coincidencia con AIS", "false": "Sin coincidencia con AIS" } }, "shiptype": { "keyword": "Tipos de Embarcaciones", "enum": { "carrier": "Embarcación de transporte", "seismic_vessel": "Buque sismológico", "passenger": "Pasajeros", "other": "Otro", "support": "Soporte", "bunker": "Aprovisionamiento", "gear": "Arte de pesca", "cargo": "Mercancía", "fishing": "Pesca", "discrepancy": "Discrepancia" } } } }, "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": "Temperatura de la superficie del mar", "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-indonesia-fishing-effort": { "name": "VMS de Indonesia", "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": "VMS de Indonesia (Pesqueros)", "description": "Conjunto de Datos para VMS de Indonesia (Privado)" }, "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": "Pelagic Presence" }, "public-indonesia-zebrax-presence": { "name": "Indonesia Zebrax", "description": "Esta capa de Global Fishing Watch utiliza datos proporcionados por Rare, Aruna y AP2HI. Los datos se recopilan mediante distintos dispositivos que rastrean la ubicación y la velocidad. La información mostrada representa la presencia de la embarcación. La presencia se determina tomando todas las posiciones transmitidas por el dispositivo de la embarcación.", "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": "| 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 |
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": "mpaguide_protection_level", "enum": { "unknown": "unknown", "light": "light", "full": "full", "high": "high", "incompatible": "incompatible", "minimal": "minimal" } } } }, "public-mpa-all": { "name": "AMP (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: November 2022. See more detailed metadata information for this layer." }, "public-mpa-no-take": { "name": "MPAs - No take", "description": "El término Áreas Marinas Protegidas incluye, por ejemplo, reservas marinas, áreas marinas totalmente protegidas, zonas sin tomar santuarios marinos, santuarios oceánicos, parques marinos y áreas marinas administradas localmente. Muchos de ellos tienen diferentes niveles de protección y el abanico de actividades permitidas o prohibidas dentro de sus límites también varía considerablemente. La capa de áreas sin actividad pesquera se creó utilizando los datos disponibles en el Planeta Marino Protegido WDPA usando el filtro NO_TAKE = 'Todos'. Fuente: Base de datos mundial sobre áreas protegidas (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-norway-fishing-effort": { "name": "VMS de Noruega", "description": "Los datos del sistema de monitoreo de embarcaciones (VMS) son proporcionados por la Dirección de Pesca de Noruega. Los datos se recopilan utilizando el sistema de seguimiento de embarcaciones de Noruega a través de satélites y se publican con un retraso de tres días que contienen información sobre la ubicación, velocidad, rumbo y movimiento de las embarcaciones. Global Fishing Watch analiza estos datos utilizando los mismos algoritmos desarrollados para el sistema de identificación automática (AIS) para identificar la actividad y los comportamientos de pesca. El algoritmo clasifica cada punto de datos de transmisión de los barcos como pesca aparente o no, y muestra el primero en el mapa de actividad pesquera de Global Fishing Watch. El VMS transmite datos de manera diferente al AIS y puede brindar diferentes medidas de integridad, precisión y calidad. Global Fishing Watch mejora continuamente sus algoritmos en todos los formatos de datos de transmisión para identificar algorítmicamente la \"actividad de pesca aparente\". Es posible que no se identifique alguna actividad pesquera o que el mapa de calor muestre actividad pesquera aparente cuando en realidad no se está pescando. Por estas razones, Global Fishing Watch califica los términos “actividad pesquera”, “pesca” o “esfuerzo pesquero” como aparentes en lugar de confirmados. Toda la información de Global Fishing Watch sobre la \"actividad de pesca aparente\" debe considerarse una estimación y debe confiarse únicamente en la discreción del usuario. Los algoritmos de detección de pesca de Global Fishing Watch se desarrollan y prueban utilizando datos de eventos de pesca reales recopilados por observadores y se combinan con análisis expertos de datos de movimiento de embarcaciones AIS, lo que da como resultado la clasificación manual de miles de eventos de pesca conocidos. Global Fishing Watch también colabora ampliamente con investigadores académicos a través de nuestro programa de investigación para compartir datos de clasificación de actividades pesqueras y mejorar las técnicas de clasificación automatizadas", "schema": { "speed": "speed", "bearing": "bearing" } }, "public-norway-fishing-vessels": { "name": "Norway VMS (Fishing Vessels)", "description": "Dataset for VMS Norway (Public)", "schema": { "source": "fuente" } }, "public-norway-non-fishing-vessels": { "name": "Norway VMS (Non Fishing Vessels)", "description": "Dataset for VMS Norway (Public)", "schema": { "source": "fuente" } }, "public-norway-presence": { "name": "VMS de Noruega", "description": "Esta capa utiliza los datos del sistema de monitoreo de embarcaciones (VMS) proporcionados por la Dirección de Pesca de Noruega. Los datos son recibidos por el sistema VMS de Noruega vía satélite y se publican con un retraso de tres días conteniendo información sobre la ubicación, velocidad, rumbo y movimiento de las embarcaciones. La capa de actividad muestra un mapa de calor de la presencia de embarcaciones. Cada punto en la capa de presencia representa una posición de las embarcaciones, sin embargo no son mostradas todas las posiciones. Las posiciones de las embarcaciones se muestran una vez por hora. Haga clic en la posición de un barco para ver la ruta completa de la embarcación.", "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": "VMS de Panamá", "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": "Esta capa utiliza los datos del Sistema de Seguimiento de Buques (VMS) proporcionados por la Autoridad Panamana de Recursos Acuáticos (ARAP). Los datos son recibidos por el sistema VMS de Panama vía satélite y contienen identidades de buques, tipo de engranaje, ubicación, velocidad, dirección y más. Los datos del buque portador de Panama también están disponibles aquí. Cada punto en la capa de datos del buque de transporte representa una posición de los transportistas, pero no todas las posiciones se muestran. Las posiciones del buque portador se muestran una vez al día. En el futuro, esperamos poder mostrar más posiciones. Haga clic en la posición del transportista para ver el camino completo de la compañía. Global Fishing Watch analiza estos datos utilizando los mismos algoritmos que desarrollamos para los datos AIS para identificar la actividad y el comportamiento de la pesca. El algoritmo clasifica cada punto de datos de emisión de los buques como pesca aparente o sin actividad pesquera y muestra el primero en el mapa de calor de la actividad pesquera del Observatorio Mundial de Pesca. El VMS transmite datos de forma muy diferente al AIS y puede exhibir diferentes medidas de integridad, precisión y calidad. Con el tiempo, nuestros algoritmos mejorarán a través de todos nuestros formatos de datos de difusión. El algoritmo de detección de pesca de Global Fishing Watch para VMS, como para AIS, es un mejor esfuerzo para identificar algorítmicamente “aparente actividad pesquera”. Es posible que no se identifique alguna actividad pesquera, o que el mapa de calor pueda mostrar una actividad pesquera aparente donde la pesca no se está llevando a cabo. Por estas razones, Global Fishing Watch califica los términos “actividad pesquera”, “pesca” y “esfuerzo pesquero” como “aparente” en lugar de brindar certeza sobre esa actividad. En un todo o en parte, la información de Global Fishing Watch sobre “actividad pesqura aparente” debe ser considerada una estimación y debe ser confiada únicamente bajo su propio riesgo. Los algoritmos de detección de pesca Global Fishing Watch son desarrollados y probados usando datos reales de eventos de pesca recopilados por observadores, combinado con el análisis experto de los datos del movimiento de los buques AIS resultando en la clasificación manual de miles de eventos de pesca conocidos. Global Fishing Watch también colabora ampliamente con investigadores académicos a través de nuestro programa de investigación para compartir datos de clasificación de actividades pesqueras y clasificación automatizada.", "schema": { "source": "fuente" } }, "public-panama-non-fishing-vessels": { "name": "Panama VMS (Public Non fishing vessels)", "description": "Dataset for VMS Panama - Carriers (Public)", "schema": { "source": "fuente" } }, "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": "VMS de Perú", "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": "Flota", "enum": { "industrial": "industrial", "artisanal": "Artesanal", "small-scale": "small-scale", "not defined": "not defined" } }, "speed": "speed", "origin": { "keyword": "origin", "enum": { "PER": "Peru", "Foreign": "Foreign" } }, "bearing": "bearing" } }, "public-peru-fishing-vessels": { "name": "VMS de Perú (Embarcaciones de pesca)", "description": "Conjunto de Datos VMS Perú (Público)" }, "public-peru-presence": { "name": "VMS de Perú", "description": "Esta capa de Global Fishing Watch utiliza los datos proporcionados por el Ministerio de la Producción del Gobierno peruano (PRODUCE). El permiso para incluir datos peruanos exigió que se implementara un retraso de 10 días para la publicación. Los datos se recogen utilizando su Sistema de Monitorización de Buques (VMS) a través de satélites y receptores terrestres, y contiene un tipo de engranaje de identidad, ubicación, velocidad, dirección y más. Global Fishing Watch analiza estos datos utilizando los mismos algoritmos que desarrollamos para los datos AIS para identificar la actividad y el comportamiento de la pesca. El algoritmo clasifica cada punto de datos de emisión de los buques como pesca aparente o sin actividad pesquera y muestra el primero en el mapa de calor de la actividad pesquera del Observatorio Mundial de Pesca. El VMS transmite datos de forma muy diferente al AIS y puede exhibir diferentes medidas de integridad, precisión y calidad. Con el tiempo, nuestros algoritmos mejorarán a través de todos nuestros formatos de datos de difusión. El algoritmo de detección de pesca de Global Fishing Watch para VMS, como para AIS, es un mejor esfuerzo para identificar algorítmicamente “aparente actividad pesquera”. Es posible que no se identifique alguna actividad pesquera, o que el mapa de calor pueda mostrar una actividad pesquera aparente donde la pesca no se está llevando a cabo. Por estas razones, Global Fishing Watch califica los términos “actividad pesquera”, “pesca” y “esfuerzo pesquero” como “aparente” en lugar de brindar certeza sobre esa actividad. En un todo o en parte, la información de Global Fishing Watch sobre “actividad pesqura aparente” debe ser considerada una estimación y debe ser confiada únicamente bajo su propio riesgo. Los algoritmos de detección de pesca Global Fishing Watch son desarrollados y probados usando datos reales de eventos de pesca recopilados por observadores, combinado con el análisis experto de los datos del movimiento de los buques AIS resultando en la clasificación manual de miles de eventos de pesca conocidos. Global Fishing Watch también colabora ampliamente con investigadores académicos a través de nuestro programa de investigación para compartir datos de clasificación de actividades pesqueras y clasificación automatizada.", "schema": { "fleet": { "keyword": "Flota", "enum": { "industrial": "industrial", "artisanal": "Artesanal", "small-scale": "small-scale", "not defined": "not defined" } }, "origin": { "keyword": "origin", "enum": { "PER": "Peru", "Foreign": "Foreign" } } } }, "public-peru-vessel-identity-fishing": { "name": "VMS de Perú (Embarcaciones de pesca)", "description": "Conjunto de Datos VMS Perú (Público)" }, "public-png-fishing-effort": { "name": "VMS de Papúa Nueva Guinea", "description": "Los datos del sistema de seguimiento de embarcaciones (VMS) son proporcionados por la Autoridad Nacional de Pesca de Papúa Nueva Guinea. Los datos se recopilan utilizando el VMS nacional de Papúa Nueva Guinea proporcionado por el Sistema de Gestión e Información Pesquera (FIMS, por sus siglas en inglés). Los datos VMS incluyen los identificadores y la ubicación de las embarcaciones, y se publican con un retraso de cinco días. Global Fishing Watch infiere la velocidad y el rumbo para la ubicación de cada barco y analiza estos datos utilizando los mismos algoritmos desarrollados para el sistema de identificación automática (AIS) para identificar la actividad y los comportamientos de pesca. El algoritmo clasifica cada punto de datos de transmisión de las embarcaciones como pesca aparente o no pesca y muestra el primero en el mapa de actividad pesquera de Global Fishing Watch. VMS transmite datos de manera diferente a AIS y puede brindar diferentes medidas de integridad, precisión y calidad. Global Fishing Watch mejora continuamente sus algoritmos en todos los formatos de datos de transmisión para identificar algorítmicamente la \"actividad de pesca aparente\". Es posible que no se identifique alguna actividad pesquera o que el mapa de calor muestre actividad pesquera aparente cuando en realidad no se está pescando. Por estas razones, Global Fishing Watch califica los términos “actividad pesquera”, “pesca” o “esfuerzo pesquero” como aparentes en lugar de ciertos. Cualquier y toda la información de Global Fishing Watch sobre la \"actividad de pesca aparente\" debe considerarse una estimación y debe confiarse únicamente en la discreción del usuario. Los algoritmos de detección de pesca de Global Fishing Watch se desarrollan y prueban utilizando datos de eventos de pesca reales recopilados por observadores y se combinan con análisis expertos de datos de movimiento de embarcaciones AIS, lo que da como resultado la clasificación manual de miles de eventos de pesca conocidos. Global Fishing Watch también colabora ampliamente con investigadores académicos a través de nuestro programa de investigación para compartir datos de clasificación de actividades pesqueras y mejorar las técnicas de clasificación automatizadas", "schema": { "speed": "speed", "bearing": "bearing" } }, "public-png-fishing-vessels": { "name": "VMS de Papúa Nueva Guinea (Buques de pesca)", "description": "Dataset de VMS de Papúa Nueva Guinea (Buques de pesca)", "schema": { "source": "fuente" } }, "public-png-presence": { "name": "VMS de Papúa Nueva Guinea", "description": "Los datos del sistema de seguimiento de embarcaciones (VMS) son proporcionados por la Autoridad Nacional de Pesca de Papúa Nueva Guinea. Los datos se recopilan utilizando el VMS nacional de Papúa Nueva Guinea proporcionado por el Sistema de gestión e información pesqueras (FIMS, por sus siglas en inglés). Los datos de VMS incluyen los identificadores y la ubicación de las embarcaciones, y se publican con un retraso de cinco días.\n\nLa capa de actividad muestra un mapa de calor de presencia de embarcaciones. La presencia se determina tomando dos posiciones por hora por embarcación a partir de las posiciones transmitidas por el VMS de la embarcación.", "schema": { "speed": "speed", "bearing": "bearing" } }, "public-png-vessel-identity-fishing": { "name": "VMS de Papúa Nueva Guinea (Buques de pesca)", "description": "Dataset de VMS de Papúa Nueva Guinea (Buques de pesca)" }, "public-ports": { "name": "Ports", "description": "Named ports", "schema": { "name": "name", "port_id": "port_id" } }, "public-ports-v1": { "name": "Puertos", "description": "Named ports v1" }, "public-presence-viirs-match-prototype": { "name": "VIIRS", "description": "La capa de detección nocturna de barcos, conocida como conjunto de radiómetros de imágenes infrarrojas visibles (VIIRS, por sus siglas en ingles), muestra las embarcaciones en el mar que los satélites han detectado por la luz que emiten durante la noche. Aunque no se asocia exclusivamente con embarcaciones pesqueras, es probable que esta capa de actividad muestre embarcaciones asociadas con actividades como la pesca de calamar, que utilizan luces brillantes y pescan de noche.