Geospatial Reasoning

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Geospatial reasoning asks AI to locate places, measure distances and directions, understand map relationships, and analyze changes or patterns across Earth. Methods range from vision-language models for image geolocation to language-model agents that use GIS tools, and systems that represent space explicitly with coordinates, graphs, vectors, or executable spatial programs. Benchmarks test these abilities on satellite and street-view imagery, maps, geographic question answering, and multi-step environmental or disaster tasks. Models can often identify broad geographic context, but precise geometric computation, fine-grained localization, and reliable long tool-use workflows remain difficult; a key challenge is evaluating answers for spatial validity, not just plausibility.

Latest papers

77 papers13 papers in the last four weeks, up 225% on the four weeks before. 0.1% of all new papers.

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
GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces
Apr 5, 2026Xinyu Geng, Yanjing Xiao, Yuyang Zhang +5Geospatial Reasoning Benchmarks Tool-Using Vision-Language Agents
Hong Kong University of Science and Technology
GISclaw: A Comprehensive Open-Source LLM Agent System for Realistic Multi-Step Geospatial Analysis
Mar 27, 2026Jinzhen Han, JinByeong Lee, Yuri Shim +2Geospatial Reasoning Benchmarks Geospatial Reasoning
Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University, Suwon, South Korea · Ministry of the Interior and Safety, South Korea · School of Geography, University of Leeds, United Kingdom +1
Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming
Mar 26, 2026Yunus Talha Erzurumlu, Jiyong Kwag, Alper YilmazCross-View Geo-Localization Map-Based Localization
Photogrammetric Computer Vision Lab, The Ohio State University
GeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing
Mar 26, 2026Xuran Hu, Zhitong Xiong, Zhongcheng Hong +3Remote Sensing Image Understanding Large Vision-Language Models
KTH Royal Institute of Technology · Technical University of Munich · The Hong Kong University of Science and Technology (Guangzhou)
TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
Mar 19, 2026Yan Shu, Bin Ren, Zhitong Xiong +4Remote Sensing VLMs Geospatial Reasoning
University of Trento · MBZUAI · Technical University of Munich +1
Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography
Mar 19, 2026Krzysztof Janowicz, Gengchen Mai, Rui Zhu +4Geospatial Reasoning Factual Knowledge in Language Models
University of Vienna, Austria · University of Texas at Austin, USA · University of Bristol, UK +4
GroundSet: A Cadastral-Grounded Dataset for Spatial Understanding with Vector Data
Mar 15, 2026Roger Ferrod, Maël Lecene, Krishna Sapkota +4VLM Evaluation Remote Sensing Image Understanding
Université Paris Cité, LIPADE, F-75006 Paris, France · Google Research
TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings
Mar 4, 2026Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Koushik Ahamed Tonmoy +5Spatiotemporal Reasoning Geospatial Reasoning Benchmarks
Computational Intelligence and Operations Laboratory (CIOL), Bangladesh · Shahjalal University of Science and Technology (SUST), Sylhet, Bangladesh · North South University (NSU), Dhaka, Bangladesh +1
City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
Feb 22, 2026Rui Liu, Steven Jige Quan, Zhong-Ren Peng +6Hierarchical Planning Geospatial Reasoning
University of Kansas · Seoul National University · University of Florida +3
QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration
Feb 19, 2026Meng Ye, Xiao Lin, Georgina Lukoczki +2Information Retrieval Geospatial Reasoning
SRI International, Princeton, NJ, USA · University of Kentucky, Lexington, KY, USA · U.S. Geological Survey, Reston, VA, USA
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Feb 19, 2026Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8Geospatial Reasoning Benchmarks Remote Sensing
Mohamed bin Zayed University of AI, United Arab Emirates · IBM Research, Europe · Linköping University, Sweden +2
Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Feb 19, 2026Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik +1Adaptive Sampling Active Learning
University of Michigan, Ann Arbor, MI, USA · Washington University in St. Louis, St. Louis, MO, USA
GPSBench: Do Large Language Models Understand GPS Coordinates?
Feb 18, 2026Thinh Hung Truong, Jey Han Lau, Jianzhong QiGeospatial Reasoning Geospatial Reasoning Benchmarks
University of Melbourne.
EarthSpatialBench: Benchmarking Spatial Reasoning Capabilities of Multimodal LLMs on Earth Imagery
Feb 17, 2026Zelin Xu, Yupu Zhang, Saugat Adhikari +6Spatial Reasoning Benchmarks Geospatial Reasoning Benchmarks
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, USA · Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA.
GeoGR^2:Zero-Shot Geospatial Inference via Geostatistically-Guided Iterative Refinement with LLMs
Aug 6, 2025Jinfan Tang, Kunming Wu, Xieruifeng Gong +4Zero-Shot Learning Geospatial Reasoning
College of Computer Science, Sichuan University · National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University · College of Life Science, Sichuan University
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
May 19, 2025Pengyue Jia, Seongheon Park, Song Gao +2Geospatial Representation Learning Learning to Rank
Department of Data Science, City University of Hong Kong · Department of Computer Sciences, University of Wisconsin-Madison · Department of Geography, University of Wisconsin-Madison

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EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration

Jianfei Wu, Zhichun Wang, Zhensheng Wang, Zhiyu He

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

Geospatial QA Geospatial Reasoning Benchmarks Geospatial Reasoning LLM Evaluation Question Answering Tool-Augmented Language Model Agents

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While Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, their potential for purpose-driven exploration in dynamic geo-spatial environments remains under-investigated. Existing Geo-Spatial Question Answering (GSQA) benchmarks predominantly focus on static retrieval, failing to capture the complexity of real-world planning that involves dynamic user locations and compound constraints. To bridge this gap, we introduce EVGeoQA, a novel benchmark built upon Electric Vehicle (EV) charging scenarios that features a distinct location-anchored and dual-objective design. Specifically, each query in EVGeoQA is explicitly bound to a user's real-time coordinate and integrates the dual objectives of a charging necessity and a co-located activity preference. To systematically assess models in such complex settings, we further propose GeoRover, a general evaluation framework based on a tool-augmented agent architecture to evaluate the LLMs' capacity for dynamic, multi-objective exploration. Our experiments reveal that while LLMs successfully utilize tools to address sub-tasks, they struggle with long-range spatial exploration. Notably, we observe an emergent capability: LLMs can summarize historical exploration trajectories to enhance exploration efficiency. These findings establish EVGeoQA as a challenging testbed for future geo-spatial intelligence. The dataset and prompts are available at https://github.com/kg-bnu/EVGeoQA.