Remote Sensing (RS) data streams necessitate classification models capable of assimilating novel information without the computational burden of retraining from scratch. Although Few-Shot Incremental Learning (FSIL) offers a promising paradigm for adaptation using limited samples, existing methodologies often falter due to the high inter-class similarity and complex background clutter inherent in aerial imagery. To address these challenges, we introduce a novel framework, Geospatial Few-Shot Adaptive Compatible Training, designed to mitigate catastrophic forgetting in dynamic environments. This method ensures forward compatibility by strategically allocating embedding space for future catego... More >