LaGu-RCL: Language-Guided Resolution-Continual Learning for Semantic Segmentation of Remote Sensing Images

Remote sensing image semantic segmentation faces substantial challenges in training and transferring models across images with varying resolutions. This issue can be effectively mitigated by continuously learning knowledge derived from new resolutions; however, this learning process is severely plagued by catastrophic forgetting. To address this problem, this paper proposes a novel continual learning framework termed Language-Guided Resolution-Continual Learning (i.e., LaGu-RCL), which alleviates catastrophic forgetting through two complementary strategies. On the one hand, a multi-resolution image augmentation pipeline is introduced to synthesize higher- and lower-resolution variants for each training batch, allowing the model to learn from images of diverse resolutions at every training step. On the other hand, a language-guided learning strategy is proposed to aggregate features of the same resolution while separating those of different resolutions. This ensures that the knowledge acquired from previously learned resolutions is not disrupted by that from unseen resolutions, thereby mitigating catastrophic forgetting. To validate the effectiveness of the proposed approach, we construct MR-ExcavSeg, a multi-resolution dataset covering several counties in Chongqing, and conduct comparative experiments between LaGu-RCL and several state-of-the-art continual learning baselines. Experimental results demonstrate that LaGu-RCL achieves significantly superior segmentation performance and continual learning capability, verifying its advantages.