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Land cover maps are a pivotal element in a wide range of Earth Observation(EO) applications. However, annotating large datasets to develop supervisedsystems for remote sensing (RS) semantic segmentation is costly andtime-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues byadapting a model trained on a source domain, where labels are available, to atarget domain, without annotations. UDA, while gaining importance in computervision, is still under-investigated in RS. Thus, we propose a new lightweightmodel, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module(GeoMT), which utilizes geographical coordinates to align the source and targetdomains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semanticsegmentation loss to the frequency of classes. This approach is the first touse geographical metadata for UDA in semantic segmentation. It reachesstate-of-the-art performances (47,22% mIoU), reducing at the same time thenumber of parameters (33M), on a subset of the FLAIR dataset, a recentlyproposed dataset properly shaped for RS UDA, used for the first time ever forresearch scopes here.

GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates