FlameVQA is a new VQA benchmark with 34 questions per image across six operational groups for UAV wildfire intelligence, using RGB-thermal pairs and providing MLLM baselines that highlight failures in smoke detection and coverage estimation.
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The paradigm of Earth Observation analysis is shifting from static deep learning models to autonomous agentic AI. Although recent vision foundation models and multimodal large language models advance representation learning, they often lack the sequential planning and active tool orchestration required for complex geospatial workflows. This survey presents the first comprehensive review of agentic AI in remote sensing. We introduce a unified taxonomy distinguishing between single-agent copilots and multi-agent systems while analyzing architectural foundations such as planning mechanisms, retrieval-augmented generation, and memory structures. Furthermore, we review emerging benchmarks that move the evaluation from pixel-level accuracy to trajectory-aware reasoning correctness. By critically examining limitations in grounding, safety, and orchestration, this work outlines a strategic roadmap for the development of robust, autonomous geospatial intelligence.
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2026 2verdicts
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Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
citing papers explorer
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FlameVQA: A Physically-Grounded UAV Wildfire VQA Benchmark with Radiometric Thermal Supervision
FlameVQA is a new VQA benchmark with 34 questions per image across six operational groups for UAV wildfire intelligence, using RGB-thermal pairs and providing MLLM baselines that highlight failures in smoke detection and coverage estimation.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.