In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstream tasks. However, existing specialized PEFT methods often fail when applied to large-scale Earth observation tasks, as they are unable to fully handle the multifaceted and unpredictable domain gaps (\eg, spatial, semantic, and frequency shifts) inherent in RS data. To overcome this, we propose CrossEarth-Gate, which introduces two primary contributions. First, we establish a comprehensive RS module toolbox to address multifaceted domain gaps, comprising spatial, semantic, and frequency modules. Second, we develop a Fisher-guided adaptive selection mechanism that operates on this toolbox. This selection is guided by Fisher Information to quantify each module's importance by measuring its contribution to the task-specific gradient flow. It dynamically activates only the most critical modules at the appropriate layers, guiding the gradient flow to maximize adaptation effectiveness and efficiency. Comprehensive experiments validate the efficacy and generalizability of our method, where CrossEarth-Gate achieves state-of-the-art performance across 16 cross-domain benchmarks for RS semantic segmentation. The code of the work will be released.

翻译：在遥感领域，参数高效微调已成为激活基础模型在下游任务中泛化表征能力的关键方法。然而，现有专用PEFT方法在应用于大规模地球观测任务时往往失效，因为它们无法充分处理遥感数据中固有的多维度且不可预测的域差异（例如空间、语义和频域偏移）。为克服此问题，我们提出CrossEarth-Gate方法，其主要贡献包括两方面：首先，我们构建了一个涵盖空间、语义和频域模块的综合性遥感模块工具箱，以应对多维度域差异；其次，我们开发了基于该工具箱的Fisher信息引导自适应选择机制。该机制通过Fisher信息量化每个模块对任务特定梯度流的贡献度，从而动态激活关键层中最核心的模块，引导梯度流向以最大化适配效能与效率。综合实验验证了本方法的有效性与泛化能力：CrossEarth-Gate在16个跨域遥感语义分割基准测试中均达到最先进性能。本工作的代码将公开释放。