New Crowdin updates (#2311)

New translations datasets

libs/i18n-labels/en/datasets.json

@@ -1255,12 +1255,12 @@
     "description": "Dataset for VMS Papua New Guinea (Private)",
     "schema": {
       "id": "id",
-      "flag": "flag",
-      "source": "source",
-      "callsign": "callsign",
-      "shipname": "shipname",
+      "registryInfo": "registryInfo",
+      "registryOwners": "registryOwners",
+      "selfReportedInfo": "selfReportedInfo",
       "lastTransmissionDate": "lastTransmissionDate",
-      "firstTransmissionDate": "firstTransmissionDate"
+      "firstTransmissionDate": "firstTransmissionDate",
+      "registryAuthorizations": "registryAuthorizations"
     }
   },
   "private-png-presence": {
@@ -1346,6 +1346,55 @@
       "vessel_id": "vessel_id"
     }
   },
+  "public-ais-presence-viirs-match": {
+    "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.<br/>\n<br/>\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.<br/> \n<br/>\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 <a href='https://eogdata.mines.edu/products/vbd/' target='_blank' rel=noopener'>Earth Observation Group</a> 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 <a href='https://eogdata.mines.edu/products/vbd/' target='_blank' rel=noopener'>data</a>.<br/>\n<br/>\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.<br/>\n<br/>\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.<br/>\n<br/>\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.<br/>\n<br/>\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": {
+      "cel": "cel",
+      "lat": "lat",
+      "lon": "lon",
+      "pos": "pos",
+      "flag": "flag",
+      "htime": "htime",
+      "matched": {
+        "keyword": "Matching",
+        "enum": {
+          "true": true,
+          "false": false
+        }
+      },
+      "radiance": {
+        "keyword": "Radiance",
+        "enum": {
+          "0": 0,
+          "10000": 10000
+        }
+      },
+      "shiptype": {
+        "keyword": "shiptype",
+        "enum": {
+          "unknown": "unknown",
+          "fishing": "fishing",
+          "carrier": "Carrier",
+          "support": "Support"
+        }
+      },
+      "detect_id": "detect_id",
+      "qf_detect": {
+        "keyword": "qf_detect",
+        "enum": {
+          "1": 1,
+          "2": 2,
+          "3": 3,
+          "5": 5,
+          "7": 7,
+          "10": 10
+        }
+      },
+      "timestamp": "timestamp",
+      "vessel_id": "vessel_id"
+    }
+  },
   "public-areas-to-be-avoided-1618836788619": {
     "name": "Areas to be Avoided by Cargo Shipping",
     "description": "25 nm buffer around islands recommending shipping diversion",
@@ -1877,6 +1926,23 @@
     "description": "",
     "schema": {}
   },
+  "public-global-all-tracks": {
+    "name": "Tracks",
+    "description": "The dataset contains the tracks from all vessels (AIS) - Version 20231026",
+    "schema": {
+      "lat": "lat",
+      "lon": "lon",
+      "flag": "flag",
+      "night": "night",
+      "speed": "speed",
+      "course": "course",
+      "seg_id": "seg_id",
+      "elevation": "elevation",
+      "timestamp": "timestamp",
+      "distance_from_port": "distance_from_port",
+      "distance_from_shore": "distance_from_shore"
+    }
+  },
   "public-global-all-vessels": {
     "name": "AIS (All Vessels)",
     "description": "Vessels from AIS",
@@ -1963,7 +2029,9 @@
     "name": "Encounter Events. (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.",
     "schema": {
+      "flag": "flag",
       "fields": "fields",
+      "duration": "Duration",
       "event_id": "event_id",
       "event_end": "event_end",
       "vessel_id": "vessel_id",
@@ -2125,14 +2193,16 @@
       "vessel_type": {
         "keyword": "vessel_type",
         "enum": {
-          "fishing": "fishing",
           "carrier": "Carrier",
-          "cargo": "cargo",
-          "bunker_or_tanker": "bunker_or_tanker",
-          "passenger": "passenger",
           "seismic_vessel": "seismic_vessel",
-          "other_non_fishing": "other_non_fishing",
-          "unknown": "unknown"
+          "passenger": "passenger",
+          "other": "other",
+          "support": "Support",
+          "bunker": "bunker",
+          "gear": "gear",
+          "cargo": "cargo",
+          "fishing": "fishing",
+          "discrepancy": "discrepancy"
         }
       },
       "vessel-groups": "vessel-groups"
@@ -2144,14 +2214,15 @@
     "schema": {}
   },
   "public-global-sar-presence": {
-    "name": "SAR",
+    "name": "SAR with Neural classification",
     "description": "Synthetic aperture radar (SAR) can detect at-sea vessels and structures in any weather conditions. SAR is a satellite-based sensor that shoots microwaves to the Earth surface and measures the amplitude and phase of the signals that are reflected back from objects on the ground and water, known as backscatter.<br/>\\n<br/>\\nThe SAR image formed from this backscatter contains rich information about size, orientation, composition, condition and texture of the features on the water.<br/>\\n<br/>\\nThese imaging systems overcome any weather condition and illumination level, including clouds or rain, daylight or darkness.They give an advantage over some other satellite sensors, such as electro-optical imagery, which is similar to taking a picture with a camera and relies on sunlight and/or the infrared radiation emitted by objects on the ground. This latter method can be confounded by cloud cover, haze, weather events and seasonal darkness at high latitudes. SAR by comparison has proven to be the most consistent option for detecting vessels at sea.<br/>\\n<br/>\\n<strong>Detecting vessels with SAR</strong><br/>\\n<br/>\\nWe use SAR imagery from the Copernicus Sentinel-1 mission of the European Space Agency (ESA), which is sourced from polar-orbiting satellites (S1A and, formerly, S1B), to detect all vessels on each scene. Our approach combines a modified version of a well established ship detection method (Constant False Alarm Rate) with modern machine learning to identify the size of detections and eliminate false detections. This detection approach consists of identifying the pixels with a “brightness” level above the mean backscatter of the background, representing the sea clutter around the target, and then using machine learning to filter and improve the results.<br/> \\n<br/>\\n<strong>Matching SAR detections to automatic identification system (AIS) transmitters</strong><br/>\\n<br/>\\nAIS transmitters broadcast the vessel’s GPS positions to help nearby vessels avoid collisions, and these AIS messages can be recorded by satellite constellations. Global Fishing Watch has developed a sophisticated system to match available AIS data to respective SAR vessel detections. This matching is done using a likelihood model that determines AIS-message/SAR-detection pairs based on all available AIS records right before and right after the time the SAR image was taken, as well as the probability of pairing a specific AIS message to any of the vessels appearing on that image. The matching algorithm provides a score and a confidence value for each potential SAR-AIS match. Only higher confidence matches are included. Learn more about the methods in this pre-print paper - https://eartharxiv.org/repository/view/3239/<br/>\\n<br/>\\n<strong>AIS matching and vessel identity</strong><br/>\\n<br/>\\nNot all vessels are required to carry AIS transmitters (e.g. the European Union only requires use of AIS for vessels over 20 meters in length), and vessels engaged in unlawful activity may shut off their AIS transmitters. This means that for all “AIS matched” SAR detections, we have information available about the detected vessel from its AIS, such as the characteristics of the craft and whether it is fishing or not. On the other hand, all “AIS unmatched” SAR detections correspond to vessels that cannot be tracked with AIS, some of which may be engaged in illegal, unreported and unregulated (IUU) fishing. In any case, unmatched SAR detections provide the missing information about vessel traffic in the ocean.<br/>\\n<br/>\\n<strong>Source</strong><br/>\\n<br/>\\nWe use SAR imagery from ESA’s Sentinel-1 Interferometric Wide swath mode (IW) Level-1 Ground Range Detected (GRD) product, covering all coastal waters around the world with a resolution of about 20 meters. We analyze SAR scenes and detect objects using the Google Earth Engine platform and a neural net classifer.<br/>\\n<br/>\\n<strong>Detection footprints</strong><br/>\\n<br/>\\nDetection 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.<br/>\\n<br/>\\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.<br/>\\n<br/>\\n<strong>Filtering</strong><br/>\\n<br/>\\nGFW has post-processed the SAR detections to reduce noise (false positives), remove offshore infrastructure, and exclude areas with sea ice at high latitudes.<br/>",
     "schema": {
       "id": "id",
       "lat": "lat",
       "lon": "lon",
       "pos": "pos",
       "cell": "cell",
+      "flag": "flag",
       "htime": "htime",
       "ssvid": "ssvid",
       "matched": {
@@ -2161,8 +2232,65 @@
           "false": false
         }
       },
+      "geartype": {
+        "keyword": "geartype",
+        "enum": {
+          "dredge_fishing": "Dredge fishing",
+          "seiners": "Seiners",
+          "passenger": "passenger",
+          "pole_and_line": "Pole and line",
+          "cargo_or_tanker": "cargo_or_tanker",
+          "other": "other",
+          "other_purse_seines": "Other purse seines",
+          "purse_seine_support": "Purse seine support",
+          "specialized_reefer": "specialized_reefer",
+          "carrier": "Carrier",
+          "trawlers": "Trawlers",
+          "set_gillnets": "Set gillnets",
+          "squid_jigger": "Squid jigger",
+          "seismic_vessel": "seismic_vessel",
+          "tug": "tug",
+          "bunker": "bunker",
+          "gear": "gear",
+          "purse_seines": "Purse seines",
+          "patrol_vessel": "patrol_vessel",
+          "set_longlines": "Set longlines",
+          "tuna_purse_seines": "Tuna purse seines",
+          "trollers": "Trollers",
+          "drifting_longlines": "Drifting longlines",
+          "pots_and_traps": "Pots and traps",
+          "non_fishing": "non_fishing",
+          "inconclusive": "inconclusive",
+          "fixed_gear": "Fixed gear",
+          "cargo": "cargo"
+        }
+      },
+      "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"
+        }
+      },
       "timestamp": "timestamp",
-      "confidence": "confidence"
+      "vessel_id": "vessel_id",
+      "confidence": "confidence",
+      "neural_vessel_type": {
+        "keyword": "neural_vessel_type",
+        "enum": {
+          "Likely non-fishing": "Likely non-fishing",
+          "Likely Fishing": "Likely Fishing",
+          "Unknown": "Other"
+        }
+      }
     }
   },
   "public-global-support-vessels": {