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.

通过GeoFed框架，我们提出了一种解决隐私保护遥感语义分割任务的方法，通过全局特征扩展和尾部重构模块来减轻分类分布的异质性，同时设计了一个关键特征挖掘策略来减轻目标外观的异质性。在三个数据集上进行的大量实验证明，我们的GeoFed方法始终优于目前的最优方法。

数据岛之桥：针对协作遥感语义分割的地理异质感知联邦学习