FlexiMo: A Flexible Remote Sensing Foundation Model
Xuyang Li, Chenyu Li, Pedram Ghamisi, Danfeng Hong, Jón Atli Benediktsson, Jocelyn Chanussot
Abstract
The rapid expansion of multi-source satellite imagery is driving innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness diverse data. However, many existing models remain constrained by fixed spatial resolutions and patch sizes, limiting their ability to fully exploit the heterogeneous spatial characteristics inherent in satellite imagery. To address these challenges, we propose FlexiMo, a flexible remote sensing foundation model that endows the pre-trained model with the flexibility to adapt to arbitrary spatial resolutions. Central to FlexiMo is a spatial resolution-aware module that employs a parameter-free alignment embedding mechanism to dynamically recalibrate patch embeddings based on the resolution and dimensions of the input images. This design not only preserves the geometric fidelity of tokenization under varying image sizes, resolutions, and patch granularities, but also enables efficient feature extraction without requiring modifications to the underlying network architecture. In addition, FlexiMo incorporates a lightweight channel adaptation module that leverages prior spectral information from sensors. This mechanism allows the model to process images with varying numbers of channels while maintaining the data’s intrinsic physical properties. Extensive experiments on diverse multimodal, multi-resolution, and multi-scale datasets demonstrate that FlexiMo significantly enhances model generalization and robustness. In particular, the proposed method achieves outstanding performance across a range of downstream tasks, including scene classification, land cover classification, urban building segmentation, and cloud detection. We also explicitly validate physical consistency through wavelength-channel permutation and wavelength-perturbation tests, showing that FlexiMo is sensitive to physically incorrect spectral metadata while remaining robust to small wavelength deviations. By enabling parameter-efficient and physically consistent adaptation, FlexiMo improves the practical deployability of RSFMs under real-world sensor heterogeneity and scale variations.