README.md

---
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](https://arxiv.org/abs/2602.16105)
- **Code**: [github.com/joey234/gpsbench](https://github.com/joey234/gpsbench)
- **Leaderboard**: [gpsbench.github.io](https://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

```bibtex
@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}
}
```