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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 fewer dark activity events are predicted globally, but <wording> than 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 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.
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