GS-QA2: A Benchmark for Question Answering over Raster–Vector Data [Experiment]

A Systematic Comparison of RAG Architectures for Geographic POI Question Answering Using OpenStreetMap Data

It is suggested that, in dense-urban POI settings where coordinates are reliable, the marginal benefit of explicit graph edges shrinks for coordinate-computable relationships, while structured spatial processing complements vector retrieval.

When RAG Meets Query Planning: Logical Query Trees for Exploratory Reasoning Problems

Retrieval-Augmented Generation (RAG) effectively grounds large language models (LLMs) in external knowledge but struggles with exploratory reasoning problems (ERPs) that are the complex queries involving high uncertainty and ambiguity. Resolving ERPs requires complex reasoning with unclear paths, tending to result...

GeoBenchmark: Probing Large Language Models for Geo-Spatial Knowledge

CGD-QCSF: A Code Generation-Driven Query–Computation Separation Framework for Natural Language Geospatial Analysis

Geospatial data provide an important basis for urban governance, resource management, disaster assessment, and public health analysis by linking spatial locations, attribute information, and dynamic processes. However, complex geospatial analysis still requires substantial expertise in spatial databases, spatial SQL, a...

Network-based Spatial Context Retrieval for Open-weight LLMs: A Faithfulness Benchmark for Grounded Geographic Reasoning

Large language models (LLMs) encode substantial latent geographic knowledge, yet they reason poorly over space and are unreliable when queried from coordinates alone. Useful behaviour emerges only when structured spatial context is supplied in the prompt. This raises a question geographic evaluation has left unexamined...

NaVQA: Mitigating Silent Failures in Question Answering over Virtual Knowledge Graph

This paper studies the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries, and pro-poses NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models.