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arXiv · 2603.12008

CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation

Ziqi Ye·Ziyang Gong·Ning Liao·Xiaoxing Hu·Di Wang·Hongruixuan Chen·Chen Huang·Yiguo He·Yuru Jia·Xiaoxing Wang·Haipeng Wang·Xue Yang·Junchi Yan
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Abstract

Synthetic Aperture Radar (SAR) enables global, all-weather earth observation. However, owing to diverse imaging mechanisms, domain shifts across sensors and regions severely hinder its semantic generalization. To address this, we present CrossEarth-SAR, the first billion-scale SAR vision foundation model built upon a novel physics-guided sparse mixture-of-experts (MoE) architecture incorporating physical descriptors, explicitly designed for cross-domain semantic segmentation. To facilitate large-scale pre-training, we develop CrossEarth-SAR-200K, a weakly and fully supervised dataset that unifies public and private SAR imagery. We also introduce a benchmark suite comprising 22 sub-benchmarks across 8 distinct domain gaps, establishing the first unified standard for domain generalization semantic segmentation on SAR imagery. Extensive experiments demonstrate that CrossEarth-SAR achieves state-of-the-art results on 20 benchmarks, surpassing previous methods by over 10\% mIoU on some benchmarks under multi-gap transfer. All code, benchmark and datasets will be publicly available.

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Ziqi Ye, Ziyang Gong, Ning Liao, Xiaoxing Hu, Di Wang, Hongruixuan Chen, Chen Huang, Yiguo He, Yuru Jia, Xiaoxing Wang, Haipeng Wang, Xue Yang, Junchi Yan. 2026-03-12. CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation. https://arxiv.org/abs/2603.12008

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