The paper introduces ANASSA, an agentic AI orchestration framework designed for spatial intelligence in geographic information systems. It addresses gaps in current systems by integrating structured spatial reasoning, multi‑agent workflow orchestration, execution feedback, authoritative validation, provenance, uncertainty handling, and human decision authority. The architecture is detailed with eleven components across four layers, a six‑step Geospatial AI Cognitive Loop, cross‑component contracts, and governance mechanisms to ensure traceability, reproducibility, and accountability.
By Constantinos Papantoniou, Brian Hilton
arXiv:2610.15403v1 Announce Type: new
Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geographi...
By Hao Li, Wenping Yin, Fabian Deuser, Jia Jia, Ziqi Liu, Levente Juh\'asz, Fan Zhang, Filip Biljecki
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
GeoFitBench-LLM is a benchmark designed to evaluate large language models (LLMs) on their ability to elicit geospatial data requirements for geo‑analytic tasks. It focuses on two reasoning dimensions—spatio‑temporal relational and semantic grounding—and uses a provider‑independent schema to encode ground‑truth requirements. Experiments on seven models show that while many can identify necessary resources, accurately specifying fitness‑for‑use remains challenging, with GPT‑5.4 Thinking recovering all resources in 66.67% of cases.
By Amirhossein Vahdat, Thierry Badard, Luc Lamontagne
arXiv:2610.14382v1 Announce Type: new
Abstract: Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, s...
By Constantinos Papantoniou, Brian Hilton
arXiv:2608. 00877v1 Announce Type: new Abstract: Remote-sensing multimodal large language models (MLLMs) often assert facts that imagery cannot establish, such as a facility's identity or function.
By Xuechen Li