CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913810466471936 |
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| author | Kuriyal, Abhishek Vincent, Elliot Aubry, Mathieu Landrieu, Loic |
| author_facet | Kuriyal, Abhishek Vincent, Elliot Aubry, Mathieu Landrieu, Loic |
| contents | Global variations in terrain appearance raise a major challenge for satellite image analysis, leading to poor model performance when training on locations that differ from those encountered at test time. This remains true even with recent large global datasets. To address this challenge, we propose a novel domain-generalization framework for satellite images. Instead of trying to learn a single generalizable model, we train one expert model per training domain, while learning experts' similarity and encouraging similar experts to be consistent. A model selection module then identifies the most suitable experts for a given test sample and aggregates their predictions. Experiments on four datasets (DynamicEarthNet, MUDS, OSCD, and FMoW) demonstrate consistent gains over existing domain generalization and adaptation methods. Our code is publicly available at https://github.com/Abhishek19009/CoDEx. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysis Kuriyal, Abhishek Vincent, Elliot Aubry, Mathieu Landrieu, Loic Computer Vision and Pattern Recognition Global variations in terrain appearance raise a major challenge for satellite image analysis, leading to poor model performance when training on locations that differ from those encountered at test time. This remains true even with recent large global datasets. To address this challenge, we propose a novel domain-generalization framework for satellite images. Instead of trying to learn a single generalizable model, we train one expert model per training domain, while learning experts' similarity and encouraging similar experts to be consistent. A model selection module then identifies the most suitable experts for a given test sample and aggregates their predictions. Experiments on four datasets (DynamicEarthNet, MUDS, OSCD, and FMoW) demonstrate consistent gains over existing domain generalization and adaptation methods. Our code is publicly available at https://github.com/Abhishek19009/CoDEx. |
| title | CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.19737 |