{"id":1141151,"date":"2025-06-03T17:40:32","date_gmt":"2025-06-04T00:40:32","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/?post_type=msr-research-item&p=1141151"},"modified":"2025-06-05T08:03:49","modified_gmt":"2025-06-05T15:03:49","slug":"geovision-labeler-zero-shot-geospatial-classification-with-vision-and-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/geovision-labeler-zero-shot-geospatial-classification-with-vision-and-language-models\/","title":{"rendered":"GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models"},"content":{"rendered":"

Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS-CLIP) that claim zero-shot classification capabilities for satellite imagery nonetheless rely on task-specific pretraining and adaptation to reach competitive performance. We introduce GeoVision Labeler (GVL), a strictly zero-shot classification framework: a vision Large Language Model (vLLM) generates rich, human-readable image descriptions, which are then mapped to user-defined classes by a conventional Large Language Model (LLM). This modular, and interpretable pipeline enables flexible image classification for a large range of use cases. We evaluated GVL across three benchmarks-SpaceNet v7, UC Merced, and RESISC45. It achieves up to 93.2% zero-shot accuracy on the binary Buildings vs. No Buildings task on SpaceNet v7. For complex multi-class classification tasks (UC Merced, RESISC45), we implemented a recursive LLM-driven clustering to form meta-classes at successive depths, followed by hierarchical classification first resolving coarse groups, then finer distinctions to deliver competitive zero-shot performance. GVL is open-sourced at this https URL<\/a>\u00a0to catalyze adoption in real-world geospatial workflows.<\/p>\n","protected":false},"excerpt":{"rendered":"

Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS-CLIP) that claim zero-shot classification capabilities for satellite imagery nonetheless rely on task-specific pretraining and adaptation to reach competitive performance. We introduce GeoVision Labeler (GVL), a strictly […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Gilles Quentin Hacheme","user_id":"42654"},{"type":"user_nicename","value":"Girmaw Abebe Tadesse","user_id":"42657"},{"type":"user_nicename","value":"Caleb Robinson","user_id":"39606"},{"type":"user_nicename","value":"Akram Zaytar","user_id":"42666"},{"type":"user_nicename","value":"Rahul Dodhia","user_id":"41401"},{"type":"user_nicename","value":"Juan M. 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