GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917313814462464 |
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| author | Jiang, Lifan Pei, Yuhang Wu, oxi Zhao, Yan Wu, Tianrun Yu, Shulong Zhang, Lihui Cai, Deng |
| author_facet | Jiang, Lifan Pei, Yuhang Wu, oxi Zhao, Yan Wu, Tianrun Yu, Shulong Zhang, Lihui Cai, Deng |
| contents | Recent advances in MLLMs are reframing segmentation from fixed-category prediction to instruction-grounded localization. While reasoning based segmentation has progressed rapidly in natural scenes, remote sensing lacks a generalizable solution due to the prohibitive cost of reasoning-oriented data and domain-specific challenges like overhead viewpoints. We present GeoSeg, a zero-shot, training-free framework that bypasses the supervision bottleneck for reasoning-driven remote sensing segmentation. GeoSeg couples MLLM reasoning with precise localization via: (i) bias-aware coordinate refinement to correct systematic grounding shifts and (ii) a dual-route prompting mechanism to fuse semantic intent with fine-grained spatial cues. We also introduce GeoSeg-Bench, a diagnostic benchmark of 810 image--query pairs with hierarchical difficulty levels. Experiments show that GeoSeg consistently outperforms all baselines, with extensive ablations confirming the effectiveness and necessity of each component. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03983 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery Jiang, Lifan Pei, Yuhang Wu, oxi Zhao, Yan Wu, Tianrun Yu, Shulong Zhang, Lihui Cai, Deng Computer Vision and Pattern Recognition Artificial Intelligence Recent advances in MLLMs are reframing segmentation from fixed-category prediction to instruction-grounded localization. While reasoning based segmentation has progressed rapidly in natural scenes, remote sensing lacks a generalizable solution due to the prohibitive cost of reasoning-oriented data and domain-specific challenges like overhead viewpoints. We present GeoSeg, a zero-shot, training-free framework that bypasses the supervision bottleneck for reasoning-driven remote sensing segmentation. GeoSeg couples MLLM reasoning with precise localization via: (i) bias-aware coordinate refinement to correct systematic grounding shifts and (ii) a dual-route prompting mechanism to fuse semantic intent with fine-grained spatial cues. We also introduce GeoSeg-Bench, a diagnostic benchmark of 810 image--query pairs with hierarchical difficulty levels. Experiments show that GeoSeg consistently outperforms all baselines, with extensive ablations confirming the effectiveness and necessity of each component. |
| title | GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.03983 |