Geospatial-Reasoning-Driven Vocabulary-Agnostic Remote Sensing Semantic Segmentation

• URL: http://arxiv.org/abs/2602.08206v1
• Date: Mon, 09 Feb 2026 02:09:21 GMT
• Title: Geospatial-Reasoning-Driven Vocabulary-Agnostic Remote Sensing Semantic Segmentation
• Authors: Chufeng Zhou, Jian Wang, Xinyuan Liu, Xiaokang Zhang,
• Abstract summary: Open-vocabulary semantic segmentation has emerged as a promising research direction in remote sensing.<n>We propose a Geospatial Reasoning Chain-of-Thought (GR-CoT) framework to guide open-vocabulary segmentation models toward precise mapping.
• Score: 13.743073097114461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary semantic segmentation has emerged as a promising research direction in remote sensing, enabling the recognition of diverse land-cover types beyond pre-defined category sets. However, existing methods predominantly rely on the passive mapping of visual features and textual embeddings. This ``appearance-based" paradigm lacks geospatial contextual awareness, leading to severe semantic ambiguity and misclassification when encountering land-cover classes with similar spectral features but distinct semantic attributes. To address this, we propose a Geospatial Reasoning Chain-of-Thought (GR-CoT) framework designed to enhance the scene understanding capabilities of Multimodal Large Language Models (MLLMs), thereby guiding open-vocabulary segmentation models toward precise mapping. The framework comprises two collaborative components: an offline knowledge distillation stream and an online instance reasoning stream. The offline stream establishes fine-grained category interpretation standards to resolve semantic conflicts between similar land-cover types. During online inference, the framework executes a sequential reasoning process involving macro-scenario anchoring, visual feature decoupling, and knowledge-driven decision synthesis. This process generates an image-adaptive vocabulary that guides downstream models to achieve pixel-level alignment with correct geographical semantics. Extensive experiments on the LoveDA and GID5 benchmarks demonstrate the superiority of our approach.

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