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	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)
Claude (Sonnet) + QGIS MCP	Agentic	100%
Claude (Sonnet) Baseline	No MCP	~67%
GPT-5.2 Baseline	No MCP	~69%

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	Buffer analysis + intersection + styled output

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.

Dataset	Source	License
행정경계 (Administrative Boundaries)	공공데이터포털	공공누리 1유형 (CC BY)
건물 데이터 (Building Data)	국토정보플랫폼	공공누리 1유형 (CC BY)
도로망 (Road Network)	공공데이터포털	공공누리 1유형 (CC BY)
CCTV 위치정보	서울 열린데이터광장	공공누리 1유형 (CC BY)

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                   # 45 task prompts with level labels
├── evaluation/
│   ├── run_evaluation_vlm_v3.py     # VLM evaluation pipeline
│   └── 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:

@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