README.md

---
license: cc-by-4.0
task_categories:
  - other
language:
  - ko
  - en
tags:
  - GIS
  - benchmark
  - geospatial
  - LLM
  - agent
  - QGIS
  - MCP
  - spatial-analysis
pretty_name: GIS-Bench
size_categories:
  - n<100
---

# GIS-Bench: A Benchmark for Evaluating LLM-based GIS Automation Agents

## Dataset Description

**GIS-Bench** is a benchmark framework designed to evaluate the capability of Large Language Model (LLM)-based agents in automating GIS (Geographic Information System) tasks using the Model Context Protocol (MCP).

This benchmark was developed as part of a master's thesis research and targets real-world spatial analysis workflows executable in QGIS Desktop via Claude + MCP integration.

- **Paper:** *GIS-Bench: LLM 기반 GIS 자동화 에이전트 평가 벤치마크* (KCC 2026, submitted)
- **Repository:** [yiseo0/GIS-Bench](https://huggingface.co/datasets/yiseo0/GIS-Bench)
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

---

## Benchmark Overview

| Item | Details |
|------|---------|
| Total Tasks | 45 |
| Difficulty Levels | 3 (Level 1 / Level 2 / Level 3) |
| Task Type | GIS spatial analysis & visualization |
| Output Format | HTML (Leaflet.js map), PNG, JPG |
| Evaluation Method | VLM-based automated scoring (Claude Sonnet) |
| Scoring | 130-point raw → 100-point normalized |
| Pass Threshold | 60 points (normalized) |

---

## Evaluated Models

| Model | Condition | Pass Rate (Overall) |
|-------|-----------|-------------------|
| GPT-5.4-mini | Few-shot | 10.0% |
| GPT-5.2 | Few-shot | 36.0% |
| Claude (Sonnet) | Zero-shot | 60.0% |
| Claude (Sonnet) | Few-shot | 78.0% |
| Claude (Sonnet) + QGIS MCP | Agentic | **86.0%** |

> Statistical significance confirmed via McNemar's test (p < 0.05)

---

## Task Structure

Tasks are organized across 3 difficulty levels:

| Level | Description | Example |
|-------|-------------|---------|
| Level 1 | Basic data loading & visualization | Load a shapefile and display on map |
| Level 2 | Spatial filtering & styling | Filter buildings by area > 500㎡ with color coding |
| Level 3 | Multi-step spatial analysis (MCP-driven, server data access) | Load Landsat NDWI data via MCP and generate a landslide density heatmap using `native:heatmapkerneldensity` |

Each task entry includes:
- `task_no`: Task ID (1–45)
- `prompt`: Natural language instruction (Korean)
- `level`: Difficulty level (1, 2, or 3)

---

## Data Sources

All spatial datasets used in this benchmark are sourced from Korean public data portals under open licenses.

| 데이터명 | 실제 파일 | 형식 | 공간 유형 | 좌표계 | 출처 | 라이선스 |
|---------|---------|------|---------|--------|------|---------|
| 서울시 CCTV 설치 현황 | `few_shot_test_서울시 안심이 CCTV 연계 현황` | CSV | Point | EPSG:4326 (WGS84) | [서울 열린데이터광장](https://data.seoul.go.kr) | 공공누리 1유형 |
| 서울시 고도지구 경계 | `UQ123_용도지구(고도지구)_20250805` | SHP | Polygon | EPSG:5186 | [국토정보플랫폼](https://www.nsdi.go.kr) | 공공누리 1유형 |
| 서울시 행정경계 | `서울시_SIG.shp` | SHP | Polygon | EPSG:5186 | [공공데이터포털](https://www.data.go.kr) | 공공누리 1유형 |
| 수치표고모델 (DEM) | `(B080)공개DEM_34602_img_2025` | GeoTIFF | Raster | EPSG:5186 | [국토정보플랫폼](https://www.nsdi.go.kr) | 공공누리 1유형 |
| 고해상도 위성영상 | `Landsat_위성영상의_정규물지수_2024` | GeoTIFF | Raster | EPSG:5186 | [NASA EarthData](https://earthdata.nasa.gov) | Public Domain |

> All datasets are publicly available and free to use with attribution.

---

## Evaluation Pipeline

Evaluation is performed using **VLM-based automated scoring** with Claude Sonnet as the judge model.

### Scoring Criteria (130-point raw scale)

#### Basic Quality (100 pts)
| Criterion | Max Score | Description |
|-----------|-----------|-------------|
| `exists` | 20 | Meaningful visualization present |
| `accuracy` | 30 | Correct region and dataset used |
| `requirement` | 30 | Color, filter, buffer conditions met |
| `completeness` | 20 | Legend, title, labels complete |

#### Spatial Accuracy (30 pts)
| Criterion | Max Score | Description |
|-----------|-----------|-------------|
| `spatial_location` | 15 | Data displayed in correct geographic area |
| `geometry_validity` | 5 | No polygon/buffer distortion |
| `numeric_match` | 10 | Numeric conditions reflected in output |

**Normalization:** `score_normalized = round(total_raw / 130 * 100)`

Each task is evaluated **3 times** (N=3) and the average score is used for robustness.

See [`evaluation/eval_criteria.md`](evaluation/eval_criteria.md) for the full evaluation prompt and criteria details.

---

## Reference Outputs

Since GIS tasks do not always have a single deterministic "ground truth," this benchmark provides **reference outputs** — screenshots generated by the benchmark author under each task condition. These serve as visual references for what a correct result should look like.

Reference screenshots are provided in `assets/screenshots/` organized by model and task ID.

---

## MCP Tool Dependency

This benchmark was conducted using **QGIS MCP** to connect Claude to QGIS Desktop.

- **Tool:** [jjsantos01/qgis_mcp](https://github.com/jjsantos01/qgis_mcp)
- **Description:** MCP server + QGIS plugin that allows LLMs to control QGIS Desktop
- **Note:** The MCP tool code is not included in this repository. Please refer to the original repository for installation.

---

## Repository Structure

```
GIS-Bench/
├── README.md                        # This file (Dataset Card)
├── data/
│   └── tasks.json                   # 45 task prompts with level labels
├── evaluation/
│   ├── run_evaluation_vlm.py        # VLM evaluation pipeline (5 models)
│   └── eval_criteria.md             # Scoring criteria & judge prompt
├── results/
│   └── leaderboard.json             # Evaluation results per model
└── assets/
    └── screenshots/                 # Reference output screenshots
```

---

## Citation

If you use GIS-Bench in your research, please cite:

```bibtex
@inproceedings{gisbench2026,
  title     = {GIS-Bench: LLM 기반 GIS 자동화 에이전트 평가 벤치마크},
  author    = {김이소 and 장두성},
  booktitle = {한국정보과학회 학술발표논문집 (KCC 2026)},
  year      = {2026}
}
```

---

## License

- **Benchmark tasks, evaluation code, and results:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Spatial datasets:** 공공누리 1유형 (출처 표시 조건, 각 데이터 출처 참조)
- **QGIS MCP tool:** Not included. See [jjsantos01/qgis_mcp](https://github.com/jjsantos01/qgis_mcp)