Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
arXiv:2606.15077v1 Announce Type: cross Abstract: We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into structured API calls, enabling efficient access to satellite imagery and environmental datasets. The architecture integrates three agents: Guardrail for safety and policy enforcement, General-QA for intent interpretation, and Recommender-Analyst for schema-aware API call generation. This coordinated design ensures reliable, semantically aligned interaction with external
The rapid advancement of large language models and the increasing availability of geospatial data necessitate more efficient and autonomous retrieval methods, pushing the development of specialized AI agents.
This development signifies a leap in how complex, real-world data (like satellite imagery) can be accessed and utilized, making it accessible via natural language, which democratizes access and accelerates analysis in critical domains.
The paradigm for interacting with vast geospatial datasets shifts from requiring specialized technical skills and complex API calls to intuitive natural language queries, driven by coordinated AI agents.
- · Geospatial intelligence companies
- · Environmental monitoring agencies
- · AI agent developers
- · Cloud-based geospatial catalogue providers
- · Manual geospatial data analysts
- · Legacy GIS software requiring expert users
More efficient and rapid extraction of insights from satellite imagery and environmental data.
Accelerated development of AI applications that rely on real-time and historical geospatial information, influencing sectors like disaster response and urban planning.
Enhanced AI-driven real-time situational awareness for governmental and commercial entities, potentially impacting national security and economic planning decisions.
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