Geospatial QA

QA: Question Answering

Latest papers

17 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models
Oct 13, 2026Rong Li, Shijie Li, Jiangyong Huang +7Geospatial QA Visual Spatial Reasoning
The Hong Kong University of Science and Technology (Guangzhou) · Beijing Institute for General Artificial Intelligence · Agency for Science, A*STAR +3
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation
Oct 11, 2026Noam Slonim, Aviv Slobodkin, Gil Fidel +34Geospatial QA Geospatial Reasoning
Google Research · Google Geo · Spatial Bytes
Network-based Spatial Context Retrieval for Open-weight LLMs: A Faithfulness Benchmark for Grounded Geographic Reasoning
Sep 30, 2026Joan PerezSpatial Reasoning Benchmarks LLM Grounding
Urban Geo Analytics
GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering
Aug 23, 2026Meenu Ravi, Shailik Sarkar, Lulwah AlKulaib +2Geospatial QA Question Answering
Virginia Tech Alexandria, Virginia, U.S. · Florida Polytechnic University Lakeland, Florida, U.S. · Kuwait University Kuwait City, Kuwait
MultiGlobeQA: A Multilingual and Globally Diverse Benchmark for Geospatial Reasoning
Aug 4, 2026Martin Böckling, Elizaveta Nosova, Heiko Paulheim +1Spatial Reasoning Multilingual QA
Data and Web Science Group, University of Mannheim, Germany
CityLLM: A framework for natural-language querying of semantic 3D city models
Jul 16, 2026Rabindra Lamsal, Sisi Zlatanova, Johnson Xuesong ShenGeospatial QA Question Answering
GRID Lab, School of Built Environment, UNSW Sydney, NSW 2052, Australia · School of Civil and Environmental Engineering, UNSW Sydney, NSW 2052, Australia
DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery
Jul 14, 2026Xinyue Xu, Zheng Zhang, Kunyang Ma +5Visual Question Answering Geospatial QA
Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China · State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models
Jun 15, 2026Dongbin Na, Chanwoo Kim, Soonbin Rho +3Geospatial QA Robot Navigation
RGA Inc.
GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards
May 19, 2026Kyeongjin Ahn, Seungeon Lee, Krishna P. Gummadi +1Spatial Reasoning Self-Play RL
KAIST · MPI-SWS · MPI-SP
SPATIOROUTE: Dynamic Prompt Routing for Zero-Shot Spatial Reasoning
May 18, 2026Pawat Chunhachatrachai, Gueter Josmy Faure, Hung-Ting Su +1Geospatial QA Visual Spatial Reasoning
National Taiwan University · Delta Robotics Innovation Center
SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
May 12, 2026Zishan Liu, Ruoxi Zang, Yanglin Zhang +5Geospatial QA Visual Spatial Reasoning
1Lingnan University · 2XPENG Robotics
Tatarstan Toponyms: A Bilingual Dataset and Hybrid RAG System for Geospatial Question Answering
May 7, 2026Mullosharaf K. ArabovGeospatial QA Question Answering
Department of Data Analysis and Programming Technologies Institute of Computational Mathematics and Information Technologies Kazan (Volga Region) Federal University Kazan, Russia
Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
Apr 13, 2026Arun SharmaGeospatial QA Spatial Reasoning Benchmarks
University of Minnesota, Twin Cities
DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness
Apr 13, 2026Javad M Alizadeh, Genhui Zheng, Chiu C Tan +7Geospatial QA Question Answering
Temple University, Philadelphia, USA · University of Texas at Austin, Austin, USA · University of California Riverside, Riverside, USA +1
EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration
Apr 8, 2026Jianfei Wu, Zhichun Wang, Zhensheng Wang +1Geospatial QA Geospatial Reasoning Benchmarks
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China · Beijing Key Laboratory of Artificial Intelligence for Education, Beijing 100875, China · Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing 100875, China +1
MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering
Jan 21, 2026Anjishnu Mukherjee, Ziwei Zhu, Antonios AnastasopoulosLanguage Model Pretraining LLM Evaluation
George Mason University
OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
Nov 30, 2025Michael Siebenmann, Javier Argota Sánchez-Vaquerizo, Stefan Arisona +3Geospatial QA Question Answering
Professorship of Computational Social Science, ETH Zurich, 8092 Zürich, Switzerland · Esri R&D Center Zurich, 8005 Zürich, Switzerland · Complexity Science Hub, Vienna, 1030, Austria

Oct 13, 2026J/K move · Enter open · S save

GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models

Rong Li, Shijie Li, Jiangyong Huang, Lingdong Kong+6

The Hong Kong University of Science and Technology (Guangzhou) · Beijing Institute for General Artificial Intelligence · Agency for Science, A*STAR+3

Geospatial QA Visual Spatial Reasoning VLM Reasoning Representation Geometry in VLMs Geometric Representation Learning

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Vision-language models (VLMs) struggle with metric and multi-view relationships that remain implicit in RGB observations. Geometry foundation models recover this structure from RGB, but existing methods either fuse the estimated features with visual tokens and learn to use them from question answering alone, or align VLM latents with geometry features. Capturing geometry, however, does not ensure its use: zeroing the aligned latents of a released feature-alignment model leaves its accuracy unchanged. We introduce GeoReader, a framework that learns geometric representations for the VLM that reads them. First, we train lightweight Geometric Readers to produce continuous input tokens, supervising them with geometry descriptions decoded by a frozen VLM, so that the Readers express geometry in a form the target model can already read. We then freeze the Readers to preserve this interface while fine-tuning the VLM on spatial QA, with geometry injection conditioned on the question. Geometry descriptions serve only as training targets: at inference, the VLM answers directly from the continuous geometric tokens without generating a geometry chain of thought. With geometry predicted from RGB images, our approach reaches 94.3 on MindCube-Tiny with Qwen3-VL-4B and, with a multi-source QA mixture, an average of 72.5 across six additional spatial benchmarks with Qwen3.5-9B. These results suggest that a useful geometric representation is one the receiving model can use, not merely one from which geometry can be reconstructed.