AI RESEARCH
SATGround: A Spatially-Aware Approach for Visual Grounding in Remote Sensing
arXiv CS.CV
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ArXi:2512.08881v2 Announce Type: replace Vision-language models (VLMs) are emerging as powerful generalist tools for remote sensing, capable of integrating information across diverse tasks and enabling flexible, instruction-based interactions via a chat interface. In this work, we enhance VLM-based visual grounding in satellite imagery by proposing a novel structured localization mechanism. Our approach involves finetuning a pretrained VLM on a diverse set of instruction-following tasks, while interfacing a dedicated grounding module through specialized control tokens for localization.