# MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models

arXiv: https://arxiv.org/abs/2501.00316 · HTML: https://mlsanity.com/paper/2501.00316/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 31 Dec 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding](https://mlsanity.com/paper/2403.12895/) (2024)
- [TraveLLM: Could you plan my new public transit route in face of a network disruption?](https://mlsanity.com/paper/2407.14926/) (2024)
- [MapQaTor: An Extensible Framework for Efficient Annotation of Map-Based QA Datasets](https://mlsanity.com/paper/2412.21015/) (2024)

## Built on by (most cited first, 21 of 21)

- [ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps](https://mlsanity.com/paper/2505.18675/) (2025)
- [MLLM-4D: Towards Visual-based Spatial-Temporal Intelligence](https://mlsanity.com/paper/2603.00515/) (2026)
- [Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2508.21365/) (2025)
- [Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts](https://mlsanity.com/paper/2601.16965/) (2026)
- [GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2509.04334/) (2025)
- [GPSBench: Do Large Language Models Understand GPS Coordinates?](https://mlsanity.com/paper/2602.16105/) (2026)
- [MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs](https://mlsanity.com/paper/2602.18600/) (2026)
- [GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models](https://mlsanity.com/paper/2606.12821/) (2026)
- [Understanding the Geospatial Reasoning Capabilities of LLMs: A Trajectory Recovery Perspective](https://mlsanity.com/paper/2510.01639/) (2025)
- [CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding](https://mlsanity.com/paper/2512.03558/) (2025)
- [Mil-SCORE: Benchmarking Long-Context Geospatial Reasoning and Planning in Large Language Models](https://mlsanity.com/paper/2601.21826/) (2026)
- [Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?](https://mlsanity.com/paper/2602.05023/) (2026)
- [TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings](https://mlsanity.com/paper/2603.06687/) (2026)
- [The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?](https://mlsanity.com/paper/2603.20305/) (2026)
- [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026)
- [Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning](https://mlsanity.com/paper/2606.12830/) (2026)
- [Running the Gauntlet: Challenging Agentic Tasks](https://mlsanity.com/paper/2606.14397/) (2026)
- [Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding](https://mlsanity.com/paper/2608.07353/) (2026)
- [Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing](https://mlsanity.com/paper/2610.12881/) (2026)
- [Geospatial Vector Geometry as a Native Spatial Modality for Vision-Language Models](https://mlsanity.com/paper/2610.16769/) (2026)
- [Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response](https://mlsanity.com/paper/2610.18789/) (2026)

## Abstract (arXiv)

Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we introduce MapEval, a benchmark designed to assess foundation models across three distinct tasks - textual, API-based, and visual reasoning - through 700 multiple-choice questions spanning 180 cities and 54 countries, covering spatial relationships, navigation, travel planning, and real-world map interactions. Unlike prior benchmarks that focus on simple location queries, MapEval requires models to handle long-context reasoning, API interactions, and visual map analysis, making it the most comprehensive evaluation framework for geospatial AI. On evaluation of 30 foundation models, including Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro, none surpass 67% accuracy, with open-source models performing significantly worse and all models lagging over 20% behind human performance. These results expose critical gaps in spatial inference, as models struggle with distances, directions, route planning, and place-specific reasoning, highlighting the need for better geospatial AI to bridge the gap between foundation models and real-world navigation. All the resources are available at: https://mapeval.github.io/.