GeoX is a self-play RL framework in which a single multimodal policy proposes and solves spatial problems as executable programs over image primitives, using verifiable rewards to improve base VLMs by up to 5.5 points without large curated data.
RSVQA: Visual question answer- ing for remote sensing data.IEEE Transactions on Geoscience and Remote Sensing, 58(12): 8555–8566
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GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.
citing papers explorer
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GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards
GeoX is a self-play RL framework in which a single multimodal policy proposes and solves spatial problems as executable programs over image primitives, using verifiable rewards to improve base VLMs by up to 5.5 points without large curated data.
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GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis
GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
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Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition
A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.