CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation

Ziyang Gong; Zhixiang Wei; Di Wang; Xiaoxing Hu; Xianzheng Ma; Hongruixuan Chen; Yuru Jia; Yupeng Deng; Zhenming Ji; Xiangwei Zhu; Xue Yang; Naoto Yokoya; Jing Zhang; Bo Du; Junchi Yan; Liangpei Zhang

arXiv · 2410.22629

CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation

Ziyang Gong·Zhixiang Wei·Di Wang·Xiaoxing Hu·Xianzheng Ma·Hongruixuan Chen·Yuru Jia·Yupeng Deng·Zhenming Ji·Xiangwei Zhu·Xue Yang·Naoto Yokoya·Jing Zhang·Bo Du·Junchi Yan·Liangpei Zhang

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Abstract

The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios. Despite the substantial domain gaps in RS images that are characterized by variabilities such as location, wavelength, and sensor type, research in this area remains underexplored: (1) Current cross-domain methods primarily focus on Domain Adaptation (DA), which adapts models to predefined domains rather than to unseen ones; (2) Few studies targeting the RSDG issue, especially for semantic segmentation tasks, where existing models are developed for specific unknown domains, struggling with issues of underfitting on other unknown scenarios; (3) Existing RS foundation models tend to prioritize in-domain performance over cross-domain generalization. To this end, we introduce the first vision foundation model for RSDG semantic segmentation, CrossEarth. CrossEarth demonstrates strong cross-domain generalization through a specially designed data-level Earth-Style Injection pipeline and a model-level Multi-Task Training pipeline. In addition, for the semantic segmentation task, we have curated an RSDG benchmark comprising 32 cross-domain settings across various regions, spectral bands, platforms, and climates, providing a comprehensive framework for testing the generalizability of future RSDG models. Extensive experiments on this benchmark demonstrate the superiority of CrossEarth over existing state-of-the-art methods.

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Ziyang Gong, Zhixiang Wei, Di Wang, Xiaoxing Hu, Xianzheng Ma, Hongruixuan Chen, Yuru Jia, Yupeng Deng, Zhenming Ji, Xiangwei Zhu, Xue Yang, Naoto Yokoya, Jing Zhang, Bo Du, Junchi Yan, Liangpei Zhang. 2025-09-23. CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation. https://arxiv.org/abs/2410.22629

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