Method, device and system for predicting a fish-presence matrix for determining a fishing ground

A non-transitory computer readable medium storing instructions which, when executed cause at least one processor unit to perform steps comprising:

• a. acquiring first catch data for a first geographical region over a reference period, the first catch data comprising fish catch attributes or fish presence events, each associated with a location in the first geographical region and a time in the reference period;
• b. transforming the first catch data into a sparse catch tensor for the first geographical region over the reference period by time-aligning and geo-aligning the first catch data on a pre-defined spatial-temporal grid based on the location in the first geographical region and the time in the reference period associated with each of the fish catch attributes or fish presence events;
• c. densifying the sparse catch tensor into a training fish-presence matrix by using a motion-aware interpolation engine to determine motion data expanding temporal gaps in the sparse catch tensor using realistic motion paths that detour around land between consecutive first catch data and smoothing the motion data by at least one radially decaying probability distribution kernel;
• d. acquiring first environmental data layers for the first geographical region over the reference period, wherein the first environmental data layers comprise weather or meteorological data, marine biological data, and/or oceanographic data;
• e. stacking the first environmental data layers together with the training fish-presence matrix to form a training data cube;
• and
• f. producing the trained fish prediction network by iteratively supplying the training data cube as an input and using the training fish-presence matrix, or a loss-scaled variant thereof, as a target while minimizing a differentiable loss function until a loss is below a predetermined threshold.

A system for obtaining a trained fish-prediction network, the system comprising:

• g. a server-side training and/or prediction engine, comprising:
• i. at least one processor unit;
• and,
• ii. a non-transitory memory storing instructions which, when executed cause the at least one processor unit to perform steps comprising:
• 1. acquiring first catch data for a first geographical region over a reference period, the first catch data comprising fish catch attributes or fish presence events, each associated with a location in the first geographical region and a time in the reference period;
• 2. transforming the first catch data into a sparse catch tensor for the first geographical region over the reference period by time-aligning and geo-aligning the first catch data on a pre-defined spatial-temporal grid based on the location in the first geographical region and the time in the reference period associated with each of the fish catch attributes or fish presence events;
• 3. densifying the sparse catch tensor into a training fish-presence matrix by using a motion-aware interpolation engine to determine motion data expanding temporal gaps in the sparse catch tensor using realistic motion paths that detour around land between consecutive first catch data and smoothing the motion data by at least one radially decaying probability distribution kernel;
• 4. acquiring first environmental data layers for the first geographical region over the reference period, wherein the first environmental data layers comprise weather or meteorological data, marine biological data, and/or oceanographic data;
• 5. stacking the first environmental data layers together with the training fish-presence matrix to form a training data cube;
• 6. producing the trained fish prediction network by iteratively supplying the training data cube as an input and using the training fish-presence matrix, or a loss-scaled variant thereof, as a target while minimizing a differentiable loss function until a loss is below a predetermined threshold.