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.
@article{arxiv.2603.03983,
title = {GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery},
author = {Lifan Jiang and Yuhang Pei and oxi Wu and Yan Zhao and Tianrun Wu and Shulong Yu and Lihui Zhang and Deng Cai},
journal= {arXiv preprint arXiv:2603.03983},
year = {2026}
}