AI 中文总结
该研究针对现有农田遥感图像分割范式的不足，提出动态分割智能体FarmSeeker，构建支持推理查询的全球高分辨率基准GSFS-Bench，其分割性能比现有方法更稳定。
AI 中文摘要
现有农田遥感图像（FRSI）分割遵循“借助图像内信息思考”的范式，假设当前图像包含可靠分割所需的充足视觉证据。然而农田外观随物候和空间背景变化，常与其他土地覆盖类型混淆，瞬时局部观测不足。因此分割歧义不仅源于模型表示有限，更根本在于所需的时空信息超出当前图像。基于此洞见，我们从信息瓶颈视角将FRSI分割重新定义为由任务相关的额外时空信息增益驱动的动态决策过程。我们进一步提出FarmSeeker，这是一种动态FRSI分割智能体，可识别歧义区域、推理其成因，并按需查询额外时空信息以实现准确分割。为评估FarmSeeker，我们构建了GSFS-Bench，这是首个支持推理查询的全球规模高分辨率FRSI分割基准。实验表明，FarmSeeker的分割性能比现有方法更稳定。该项目公开可用，网址为this https URL。
英文摘要
Existing farmland remote sensing image (FRSI) segmentation follows a "Think with Intra-Image" paradigm, assuming that the current image contains sufficient visual evidence for reliable segmentation. Yet farmland appearance varies with phenology and spatial context and is often confused with other land-cover, making instantaneous, local observations inadequate. Thus, segmentation ambiguity stems not only from limited model representation, but more fundamentally from the required spatio-temporal information lying beyond the current image. Based on this insight, we redefine FRSI segmentation from an information bottleneck perspective as a dynamic decision process driven by task-relevant extra spatio-temporal information gain. We further propose FarmSeeker, a dynamic FRSI segmentation agent that identifies ambiguous regions, reasons about their causes, and queries extra spatio-temporal information on demand for accurate segmentation. To evaluate FarmSeeker, we construct GSFS-Bench, the first global-scale, high-resolution FRSI segmentation benchmark that supports reasoning-querying. Experiments show that FarmSeeker achieves more stable segmentation performance than existing methods. The project is publicly available at: https://withoutocean.github.io/FarmSeeker/
