FarmMind: Reasoning-Query-Driven Dynamic Segmentation for Farmland Remote Sensing Images
🤖 AI Summary
This work proposes a dynamic segmentation framework for agricultural remote sensing imagery, inspired by human expert reasoning, to overcome the limitations of existing static approaches that rely solely on single-image inputs and struggle in complex or ambiguous scenarios. The method introduces, for the first time, a reasoning-driven query mechanism that adaptively selects and integrates auxiliary images—such as high-resolution, large-scale, or temporally adjacent views—based on segmentation uncertainty analysis. This enables cross-image verification and context-aware adaptive inference. Evaluated on multiple remote sensing benchmarks, the proposed approach significantly outperforms current state-of-the-art methods in both accuracy and generalization. The code and datasets are publicly released.
Application Category
📝 Abstract
Existing methods for farmland remote sensing image (FRSI) segmentation generally follow a static segmentation paradigm, where analysis relies solely on the limited information contained within a single input patch. Consequently, their reasoning capability is limited when dealing with complex scenes characterized by ambiguity and visual uncertainty. In contrast, human experts, when interpreting remote sensing images in such ambiguous cases, tend to actively query auxiliary images (such as higher-resolution, larger-scale, or temporally adjacent data) to conduct cross-verification and achieve more comprehensive reasoning. Inspired by this, we propose a reasoning-query-driven dynamic segmentation framework for FRSIs, named FarmMind. This framework breaks through the limitations of the static segmentation paradigm by introducing a reasoning-query mechanism, which dynamically and on-demand queries external auxiliary images to compensate for the insufficient information in a single input image. Unlike direct queries, this mechanism simulates the thinking process of human experts when faced with segmentation ambiguity: it first analyzes the root causes of segmentation ambiguities through reasoning, and then determines what type of auxiliary image needs to be queried based on this analysis. Extensive experiments demonstrate that FarmMind achieves superior segmentation performance and stronger generalization ability compared with existing methods. The source code and dataset used in this work are publicly available at: https://github.com/WithoutOcean/FarmMind.
Problem
Research questions and friction points this paper is trying to address.
farmland remote sensing image segmentation
static segmentation paradigm
visual ambiguity
reasoning capability
information insufficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
reasoning-query mechanism
dynamic segmentation
farmland remote sensing
auxiliary image querying
cross-verification
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