U‑Net vs Vision Transformers for Land Cover Change Detection

Not before you start, but you do need enough domain understanding to avoid obvious mistakes. You should know what the spectral bands represent, how seasonality changes appearance, and why misregistration can create false changes. Those details affect performance as much as the choice of architecture.

Not automatically. ViT-style models became strong largely through large-scale pretraining, while U‑Net was designed to make efficient use of limited annotated data. On small or medium change-detection datasets, U‑Net is often the safer baseline, and transformer models usually become more convincing when pretraining or broader geographic variation is available.

F1 and IoU are usually more informative than raw accuracy because the unchanged class often dominates the raster. Precision and recall also matter because the operational cost of a false positive and a false negative can be very different depending on whether you are monitoring urban sprawl, flood extent, or forest disturbance.

Start by asking whether the output could relate to an identified or identifiable person, directly or indirectly. If the answer might be yes, privacy rules and access controls become relevant, and you should pair technical safeguards with a documented governance process. NIST’s AI RMF is useful for structuring that process even when the data itself is not obviously personal.