AI RESEARCH
GeoVista: Visually Grounded Active Perception for Ultra-High-Resolution Remote Sensing Understanding
arXiv CS.CV
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ArXi:2605.14475v1 Announce Type: new Interpreting ultra-high-resolution (UHR) remote sensing images requires models to search for sparse and tiny visual evidence across large-scale scenes. Existing remote sensing vision-language models can inspect local regions with zooming and cropping tools, but most exploration strategies follow either a one-shot focus or a single sequential trajectory. Such single-path exploration can lose global context, leave scattered regions unvisited, and revisit or count the same evidence multiple times.