GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates

Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, anno- tating large datasets to develop supervised systems for re- mote sensing (RS) semantic segmentation is costly and time- consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the fre- quency of classes. This approach is the first to use geo- graphical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), re- ducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here.