Papers for

geospatial intelligence developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Earth observation question answering improved with observation-support graph reasoning

OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering

Abstract: Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-derivation problem under strict spatiotemporal constraints. Existing LLM agents fail to ground observation items or suffer from search-space explosion. We propose OSGR, a two-stage framework that factorizes this search into offline symbolic concept deduction and online Relation-Conditioned Grounding. OSGR leverages an offline Observation-Support Graph to deduce valid target-to-result support paths at the concept level, and applies spatiotemporal compatibility kernels to instantiate grounded observation-item sets. On Earth-Bench and DisasterM3-Reason, OSGR improves GPT-4o accuracy from 49.3\% to 63.7\% while reducing the execution-step ratio from 2.9 to 2.5.

Tue 3 NovArtificial IntelligenceInformation Retrieval

The gist

Answering questions about Earth from satellite images is hard because it needs searching many places and times carefully. The authors propose a new method called OSGR that breaks the problem into two steps: first figuring out concepts and their relationships, then matching those to actual data. This makes the search smarter and faster. Their method works better than existing approaches on test problems, improving accuracy and using fewer steps.

Open → 2611.04301v1