Datasets:

joey234
/

GPSBench

license: mit
task_categories:
  - question-answering
language:
  - en
tags:
  - geospatial
  - gps
  - benchmark
  - geography
  - spatial-reasoning
  - coordinates
size_categories:
  - 10K<n<100K
pretty_name: 'GPSBench: GPS Reasoning Benchmark for LLMs'
dataset_info:
  - config_name: pure_gps
    description: 'Pure GPS track: coordinate manipulation tasks'
  - config_name: applied
    description: 'Applied track: geographic reasoning tasks'

GPSBench: Do Large Language Models Understand GPS Coordinates?

GPSBench is a benchmark dataset of 57,800 samples across 17 tasks for evaluating geospatial reasoning in Large Language Models (LLMs).

Paper: arXiv:2602.16105
Code: github.com/joey234/gpsbench
Leaderboard: gpsbench.github.io

Benchmark Structure

GPSBench is organized into two complementary evaluation tracks:

Pure GPS Track (9 tasks)

Coordinate manipulation without geographic knowledge:

Representation: Format Conversion, Coordinate System Transformation
Measurement: Distance Calculation, Bearing Computation, Area & Perimeter
Spatial Operations: Coordinate Interpolation, Bounding Box, Route Geometry, Relative Position

Applied Track (9 tasks)

Real-world geographic reasoning requiring world knowledge:

Knowledge Retrieval: Place Association, Name Disambiguation, Terrain Classification
Spatial Reasoning: Relative Position, Proximity & Nearest Neighbor, Boundary Analysis
Pattern Analysis: Route Analysis, Spatial Patterns, Missing Data Inference

Dataset Splits

Split	Ratio	Samples
Train	60%	~34,680
Dev	10%	~5,780
Test	30%	~17,340

Each task contains approximately 3,400 samples (1,020 test).

Data Format

Each sample is a JSON object containing:

task: Task identifier
question: Human-readable question/prompt
ground_truth: Expected answer with evaluation metrics
coordinate(s): GPS coordinate data
metadata: Track, task description, and other context

Citation

@article{gpsbench2025,
  title   = {GPSBench: Do Large Language Models Understand GPS Coordinates?},
  author  = {Truong, Thinh Hung and Lau, Jey Han and Qi, Jianzhong},
  journal = {arXiv preprint arXiv:2602.16105},
  year    = {2025}
}