  Remote sensing semantic segmentation (RSS) is an essential task in Earth
Observation missions. Due to data privacy concerns, high-quality remote sensing
images with annotations cannot be well shared among institutions, making it
difficult to fully utilize RSS data to train a generalized model. Federated
Learning (FL), a privacy-preserving collaborative learning technology, is a
potential solution. However, the current research on how to effectively apply
FL in RSS is still scarce and requires further investigation. Remote sensing
images in various institutions often exhibit strong geographical heterogeneity.
More specifically, it is reflected in terms of class-distribution heterogeneity
and object-appearance heterogeneity. Unfortunately, most existing FL studies
show inadequate focus on geographical heterogeneity, thus leading to
performance degradation in the global model. Considering the aforementioned
issues, we propose a novel Geographic Heterogeneity-Aware Federated Learning
(GeoFed) framework to address privacy-preserving RSS. Through Global Feature
Extension and Tail Regeneration modules, class-distribution heterogeneity is
alleviated. Additionally, we design an Essential Feature Mining strategy to
alleviate object-appearance heterogeneity by constructing essential features.
Extensive experiments on three datasets (i.e., FBP, CASID, Inria) show that our
GeoFed consistently outperforms the current state-of-the-art methods. The code
will be available publicly.
