geo-deep-learning

# 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: - **Label count and labelled area** — polygons alone say nothing; 40 polygons covering 2 ha and 40 covering 2 000 km² are different problems. - **Geographic spread** — are the labels clustered in one scene, one season and one sensor, or distributed across the deployment domain? Clustered labels cap what any model can generalise to, and they decide whether a geographically independent validation split is even constructible. - **Class balance and minority-class pixel fraction**, so loss and sampling choices are grounded rather than assumed. - **Deployment geography** — where predictions will be made, relative to where the labels are. Do not answer "fine-tune a large model or use a simpler approach" before these are known. When the user has not supplied them, ask and give the provisional recommendation *conditioned on* the answers ("if the 40 polygons sit in one scene, then …; if they span the region, then …"), never a single unconditional recomm...