Geospatial Deep Learning

Purpose: deep learning on Earth observation with the two failure modes that dominate this field designed out from the start: spatial leakage (inflated metrics from nearby train/test pixels) and georeferencing loss (predictions that no longer align with the map).

Characterise the label set before naming an architecture

Architecture advice given without knowing the label set is guesswork. Before recommending U-Net versus a foundation model versus a non-deep baseline, state or ask for: