  Land cover maps are a pivotal element in a wide range of Earth Observation
(EO) applications. However, annotating large datasets to develop supervised
systems for remote 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 frequency of classes. This approach is the first to
use geographical metadata for UDA in semantic segmentation. It reaches
state-of-the-art performances (47,22% mIoU), reducing 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.
