Coding Pirates synthesizes intermediate satellite frames using deep learning optical flow — turning a 30-minute INSAT-3DS cadence into 7.5-minute near-real-time coverage for disaster monitoring, without additional hardware.
We ingest raw thermal infrared data from geostationary satellites, extract motion vectors with RIFE-based optical flow, and synthesize photometrically valid intermediate frames that have never been captured by the sensor.
Dynamic weather events evolve faster than the 30-minute revisit window most geostationary satellites deliver. We close that gap with synthetic frames — validated against real high-temporal data — so meteorologists have more decision points during critical hours.
Model weights, training scripts, and the CLI pipeline are published under MIT/Apache license. Agencies and researchers can validate the approach independently. Enterprise tiers add managed hosting, SLA-backed inference endpoints, and dedicated support.
We work with national meteorological agencies and research institutions.