Summary
In November, 2022 a new machine learning model for fishing activity and dark rendezvous was released in Skylight. This single model classifies AIS track segments into one of three exclusive classes: fishing activity, dark rendezvous, or neither. This is a change from the previous binary models that predicted fishing and dark rendezvous separately. As a result of the new three-class model, there are no longer instances where track segments are identified as both dark rendezvous and fishing. So instances occurances like the below screen shot where the PROFESSOR LOGACHEV is shown as being involved in Fishing activity and a Dark Rendezvous at exactly the same time on the same track segment, can no longer occur.
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In addition to preventing exact overlapping fishing and dark rendezvous events, this new model improves the confidence in dark rendezvous events since the model can detect the difference between fishing and dark rendezvous. The previous binary machine learning model for dark rendezvous had not been trained with fishing data, so it would mistakenly classify fishing activity as dark rendezvous. The result is significantly fewer dark activity events are predicted globally, but there are fewer false positive dark rendezvous events predicted than with the previous model.
Model Training:
How it was trained:
The model was trained via supervised learning on both fishing and dark rendezvous labels in order to improve model accuracy over the previous binary models.
Training data includes a combination of expert annotated data and for fishing events, ground-truthed data from fisheries observers.
The model was trained on many four types of fishing behavior, including trawling, seining, long-lining, and squid jigging. Notably not on this list, though they may be picked up for their similarities with other fishing methods: gill netting, spear fishing, pole & line.
See Appendix B for additional information.
The main characteristics of the vessel movement the model considers are:
How the vessel’s speed is changing
How the vessel is turning and maneuvering
Depth of the ocean / bathymetry
The model needs a minimum amount of historical movement in order to determine the vessel is fishing right now. Depending on the movement of a vessel at that particular time, this could mean anywhere from 1 to 40+ hours of past movement to evaluate whether its current behavior is fishing (see Appendix A).The model does not take vessel type into account specifically so that vessels that display fishing activity though they are not transmitting themselves as fishing vessels can be found.
The event icon, whether fishing or dark rendezvous, is not placed on every event, just on the first behavior detected in that 24 hr period for that vessel.
“Accuracy”/Performance of Events
This is a model based on AIS positions alone - it will never be a guarantee that fishing or a dark rendezvous happened. Consider this a flag for potential activity.
Analysts need to continue using vessel event tracks and other tools and data to make judgement calls.
As always, we would love your feedback and especially contact us if you have information that ground truths whether the fishing or dark rendezvous event actually happened or not.
A common place where there will still be some potential false positives / where fishing events are harder to distinguish are places where navigation is restrained because of the shape of the coastline (e.g. the Inside Passage in the Pacific Northwest), high vessel traffic (e.g. Strait of Malacca), more generally complex navigation (tugs or port operations more generally), and some miscellaneous other activities (dredging, law enforcement, crew or material transfer / rendezvous activity, whale watching, etc).
Determining whether a vessel is actually a buoy uses a separate model from this model.
Fishing events in Skylight v. Global Fishing Watch (GFW)
GFW also generates fishing events using AIS-based tracks globally, but their model was trained on different data, with different methods, and with different AIS providers.
It is still of most benefit to the user to use both Skylight and GFW when analyzing fishing activities.
GFW’s Public Map provides fishing events with a minimum delay of 48 hours (compared to Skylight Fishing events in near real-time ).
Historical fishing events in Skylight are available back to October 2021. GFW fishing events go back longer.
Appendix A: Segmentation, features, model, and input duration
Track Segmentation
A key aspect to the fishing training data is track segmentation. The purpose of segmentation is to transform a vessel track from a somewhat randomly sampled collection of AIS position points to a series of sections of internally-similar behavior. The benefits of this are primarily that a track may be represented in terms of operator maneuvering decisions which has clear implications for behavior analysis, as well as a lighter-weight representation of vessels allowing for faster analysis and visualization. The engine behind the segmentation process is a version of the Ramer-Douglas-Peucker algorithm modified to account for speed changes and to adapt parametrically to vessel behavior characteristics. Segmentation effectively capturing fishing behavior (most challengingly, purse seining) was a guiding design principle of the process.
Features & Model Input
The specific model features used are the following measures on segments and their neighbors:
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While using twenty segments (as opposed to fewer) for activity recognition has substantial benefits to model performance since it provides the model with a bigger window into vessel operations, one downside is that if a vessel doesn’t have sufficient segment history within three days previous to a particular fishing event, the fishing model cannot generate an output for this event and our system will not be able to flag it. Given the complex maneuvering and thus relatively fine-grained segments associated with fishing behavior, analysis shows that the overwhelming majority of vessel activity falling into this category of not being able to generate an output represents nonfishing activity. However, if you have an example of a fishing event not being generated/vessel not being surfaced that you would expect to see, we would be interested to hear about it.
Model
The model architecture used for fishing classification is a Long-Short Term Memory network (LSTM), a type of model designed for operating on time series data such as this. The model outputs binary fishing/not-fishing classifications at a segment level.
Appendix B: Labeled data
Observer Data
One source of training data comes from fisheries observers. For this data source, we matched tracks from our system with reported fishing activity from observer records to automatically label fishing behavior sections in tracks. These tracks were then checked by an expert analyst to verify this process, and the labeled tracks were used in model training. Usage of this observer data substantially improved model performance (+50%) specifically on vessels fishing in the region that provided the observer data (as well as generalizable global improvements).
Expert Annotations
Our other major (and currently largest) source of model training data is AIS tracks (raw position points) hand-labeled by expert analysts.
Trawlers
Vessel overview and physical characteristics
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Trawlers typically target midwater and bottomfish by towing a large, cone-shaped net. Trawl nets can be set and hauled over the side of the boat (known as side trawlers), or over the stern (stern trawlers). When identifying trawlers, look for mechanical trawling winches used to haul the net.
Behaviors and track analysis
Transit. Trawlers typically transit at relatively high speeds (approximately 7-8 kts) along a relatively consistent heading.
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Figure 7. Twenty hour snapshot of a trawler’s trackline by activity; speed noted in knots
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Longliners
Vessel overview and physical characteristics
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Longliners set free floating (pelagic) or weighted (fixed) lines, sometimes many miles in length with thousands of baited hooks. Lines are set at sea, and recovered after a number of hours, requiring the longliner to return to the same location or find the line floats if it has drifted with the currents. When identifying longliners from images, look for vessels with wenches, weather covers on the stern to protect the crew, and numerous buoys and flags tied along the rails when not fishing.
Behaviors and track analysis
Transit. Longliners typically transit at relatively high speeds along a relatively consistent heading.
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Figure 10. Seven day snapshot of a longliner’s trackline by activity; speed noted in knots
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Purse Seiners
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Vessel overview and physical characteristics
Purse seiners catch fish by encircling them with a long net and drawing (pursing) the bottom closed to capture the fish. The net is paid into the water while the boat travels in a large circle around the fish and retrieved through a hydraulic power block or winch. When identifying purse seiners, look for long clean decks, a boom or pursing davit or crane with the hydraulic power block, and nets stacked on the back. Purse seiners also often have crows nest/lookout or helicopters onboard to help spot desirable schools of fish from a distance.
Behaviors and track analysis
Transit. Purse seiners typically transit at relatively high speeds (approximately 6-9kts) along a relatively consistent heading.
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Figure 13. Two hour snapshot of purse seiner fishing and brailing by activity, speed noted in knots
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Squid Jiggers
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Vessel overview and physical characteristics
Squid jiggers typically fish at night in continental-shelf waters between 60-120m deep. Overhead lights are used to attract the squid, which are then caught on barbless lures on lines jigged up and down in the water by mechanically-powered jigging machines. Each jigging machine has a roller extending from the side of the boat to allow the line to be jigged up and down without causing abrasion to the line.
Behaviors and track analysis
Transiting. Squid jiggers typically transit at relatively high speeds along a relatively consistent heading.
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