Datasets:

yiseo0
/

GIS-Bench

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
License: CC BY 4.0

Benchmark Overview

Item	Details
Total Tasks	50
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	공공누리 1유형
서울시 고도지구 경계	`UQ123_용도지구(고도지구)_20250805`	SHP	Polygon	EPSG:5186	국토정보플랫폼	공공누리 1유형
서울시 행정경계	`서울시_SIG.shp`	SHP	Polygon	EPSG:5186	공공데이터포털	공공누리 1유형
수치표고모델 (DEM)	`(B080)공개DEM_34602_img_2025`	GeoTIFF	Raster	EPSG:5186	국토정보플랫폼	공공누리 1유형
고해상도 위성영상	`Landsat_위성영상의_정규물지수_2024`	GeoTIFF	Raster	EPSG:5186	NASA EarthData	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 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
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                   # 50 task prompts with level labels
├── evaluation/
│   ├── run_evaluation_vlm.py        # VLM evaluation pipeline (5 models)
│   └── eval_criteria.md             # Scoring criteria & judge prompt
└── assets/
    └── screenshots/                 # Reference output screenshots

Citation

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

@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
Spatial datasets: 공공누리 1유형 (출처 표시 조건, 각 데이터 출처 참조)
QGIS MCP tool: Not included. See jjsantos01/qgis_mcp