Behind the maps and charts on Peskas Coasts is an automated system. Every day, a schedule is triggered via GitHub Actions that runs the Peskas Coasts Pipeline.
This article breaks down exactly what happens during that automated run, step-by-step.
Phase 1: Gathering the Ground Truth
Before we can look at GPS tracks, we need to know who is fishing and what they are catching.
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ingest_assets(): The pipeline starts by downloading metadata—the “registry” of known boats, gear types, and landing sites. -
enrich_taxa(): It standardizes fish species data, ensuring that local fish names match global scientific databases (like FishBase). -
ingest_pds_trips(): We connect to Pelagic Data Systems (PDS) to get a high-level list of all recent boat trips. -
merge_survey_trips(): Here, we collect the landing surveys that each country pipeline has already checked and matched to GPS trips. If a surveyor recorded a catch at the dock, it is now linked to the GPS device that was on that specific boat.
Phase 2: Tracking and Machine Learning
Once we know who went out, we need to know what they did on the water.
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ingest_pds_tracks(): We download the raw, second-by-second GPS pings for the matched trips. -
predict_pds_tracks(): Boats move differently when they are travelling vs. when they are actively fishing. We pass the GPS tracks through a statistical model (ssfaitk) that classifies which parts of the boat’s journey were actual fishing activity.
Phase 3: Making Sense of the Ocean (Aggregation & Modeling)
Raw GPS points are messy and can compromise a fisher’s privacy. We aggregate them to make them safe and useful.
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aggregate_pds_effort(): We group the predicted fishing locations into standard hexagonal areas of the ocean (called H3 grids). We calculate how many unique trips visited a hex and how many hours were spent fishing there. -
model_cpue(): We calculate Catch Per Unit Effort (CPUE). By taking the total fish caught (from the ground surveys) and dividing it by the total hours fished in a specific hex grid (from the GPS predictions), we create a map of ocean productivity.
Phase 4: Delivery to the Web
Finally, the data must be packaged so the Peskas website can display it instantly.
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export_fishers_stats()&export_geos(): We calculate efficiency metrics (like fuel usage estimates and search efficiency) and push them directly to our MongoDB cloud database. -
export_pds_spatial(): The ocean maps are converted into web-readyJSONandGeoJSONfiles. These files are pushed to Google Cloud Storage, where the Peskas front-end consumes them to draw the interactive maps on the dashboard.