TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912974469332992 |
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| author | Shu, Yan Ren, Bin Xiong, Zhitong Zhu, Xiao Xiang Demir, Begüm Sebe, Nicu Rota, Paolo |
| author_facet | Shu, Yan Ren, Bin Xiong, Zhitong Zhu, Xiao Xiang Demir, Begüm Sebe, Nicu Rota, Paolo |
| contents | Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level visual representations. To address this problem, we introduce TerraScope, a unified VLM that delivers pixel-grounded geospatial reasoning with two key capabilities: (1) modality-flexible reasoning: it handles single-modality inputs (optical or SAR) and adaptively fuses different modalities into the reasoning process when both are available; (2) multi-temporal reasoning: it integrates temporal sequences for change analysis across multiple time points. In addition, we curate Terra-CoT, a large-scale dataset containing 1 million samples with pixel-level masks embedded in reasoning chains across multiple sources. We also propose TerraScope-Bench, the first benchmark for pixel-grounded geospatial reasoning with six sub-tasks that evaluates both answer accuracy and mask quality to ensure authentic pixel-grounded reasoning. Experiments show that TerraScope significantly outperforms existing VLMs on pixel-grounded geospatial reasoning while providing interpretable visual evidence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19039 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation Shu, Yan Ren, Bin Xiong, Zhitong Zhu, Xiao Xiang Demir, Begüm Sebe, Nicu Rota, Paolo Computer Vision and Pattern Recognition Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level visual representations. To address this problem, we introduce TerraScope, a unified VLM that delivers pixel-grounded geospatial reasoning with two key capabilities: (1) modality-flexible reasoning: it handles single-modality inputs (optical or SAR) and adaptively fuses different modalities into the reasoning process when both are available; (2) multi-temporal reasoning: it integrates temporal sequences for change analysis across multiple time points. In addition, we curate Terra-CoT, a large-scale dataset containing 1 million samples with pixel-level masks embedded in reasoning chains across multiple sources. We also propose TerraScope-Bench, the first benchmark for pixel-grounded geospatial reasoning with six sub-tasks that evaluates both answer accuracy and mask quality to ensure authentic pixel-grounded reasoning. Experiments show that TerraScope significantly outperforms existing VLMs on pixel-grounded geospatial reasoning while providing interpretable visual evidence. |
| title | TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.19039 |