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arXiv · 2603.03983

GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery

Lifan Jiang·Yuhang Pei·oxi Wu·Yan Zhao·Tianrun Wu·Shulong Yu·Lihui Zhang·Deng Cai
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Abstract

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.

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Lifan Jiang, Yuhang Pei, oxi Wu, Yan Zhao, Tianrun Wu, Shulong Yu, Lihui Zhang, Deng Cai. 2026-03-04. GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery. https://arxiv.org/abs/2603.03983

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