| # GIS-Bench Evaluation Criteria |
|
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| ## Overview |
|
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| GIS-Bench uses a **VLM-based automated evaluation** pipeline where Claude Sonnet acts as a judge model to score GIS task outputs (HTML maps, PNG, JPG screenshots). |
|
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| - **Judge Model:** `claude-sonnet-4-6` |
| - **Evaluation Repetitions:** N = 3 per task (averaged for robustness) |
| - **Raw Score Scale:** 130 points |
| - **Normalized Score Scale:** 100 points |
| - **Pass Threshold:** 60 points (normalized) |
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| --- |
|
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| ## Scoring Criteria |
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| ### Category 1: Basic Quality (100 pts) |
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| | Criterion | Max Score | Scoring Rule | |
| |-----------|-----------|-------------| |
| | `exists` | 20 | **0 or 20** — Meaningful visualization exists (not blank/error) | |
| | `accuracy` | 30 | **0–30** — Requested region and dataset are correctly used | |
| | `requirement` | 30 | **0–30** — All conditions met (color, filter, buffer, etc.) | |
| | `completeness` | 20 | **0–20** — Visual completeness: legend, title, labels present | |
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| ### Category 2: Spatial Accuracy (30 pts) |
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| Visual judgment from screenshot. |
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| | Criterion | Max Score | Scoring Rule | |
| |-----------|-----------|-------------| |
| | `spatial_location` | 15 | **15** = correct location / **8** = minor offset / **0** = coordinate error or wrong area | |
| | `geometry_validity` | 5 | **5** = normal / **2** = minor distortion / **0** = obvious distortion | |
| | `numeric_match` | 10 | **10** = fully reflected / **5** = partially reflected / **0** = not reflected | |
|
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| --- |
|
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| ## Score Normalization |
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| ``` |
| total_raw = exists + accuracy + requirement + completeness |
| + spatial_location + geometry_validity + numeric_match |
| |
| total_normalized = round(total_raw / 130 * 100) |
| |
| pass = (total_normalized >= 60) |
| ``` |
|
|
| --- |
|
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| ## VLM Judge Prompt |
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| The following prompt is passed to the judge model along with the task screenshot (base64 image): |
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| ``` |
| GIS 결과물 평가 전문가로서 아래 태스크의 출력 이미지를 채점하세요. |
| |
| [태스크 Level {level}] |
| {prompt} |
| |
| [채점 기준 — 130점 만점] |
| 기본(100점): |
| exists(0/20): 의미있는 시각화 존재 여부 |
| accuracy(0-30): 요청 지역·데이터셋 정확성 |
| requirement(0-30): 색상·필터·버퍼 등 조건 충족 |
| completeness(0-20): 범례·제목·라벨 완성도 |
| |
| 공간정확성(30점, 스크린샷 시각 판단): |
| spatial_location(0/8/15): 데이터가 올바른 지역에 표시되는지 |
| 15=정상, 8=경미한 이상, 0=좌표오류·엉뚱한위치 |
| geometry_validity(0/2/5): 폴리곤·버퍼 형태 이상 여부 |
| 5=정상, 2=경미한이상, 0=명백한왜곡 |
| numeric_match(0/5/10): 수치·조건이 결과에 반영되었는지 |
| 10=일치, 5=부분반영, 0=미반영 |
| |
| total_raw = 7개 항목 합계 |
| total_normalized = round(total_raw / 130 * 100) |
| |
| [응답] JSON만 출력, 다른 텍스트 금지: |
| {"exists":정수,"accuracy":정수,"requirement":정수,"completeness":정수, |
| "spatial_location":정수,"geometry_validity":정수,"numeric_match":정수, |
| "total_raw":정수,"total_normalized":정수,"pass":불리언,"reason":"한줄이유"} |
| ``` |
|
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| --- |
|
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| ## Evaluation Flow |
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| ``` |
| Output File |
| │ |
| ├── .html / .htm → Playwright rendering (3s wait) → Screenshot (PNG) |
| │ │ |
| └── .png / .jpg ────────────────────────────────────────────┘ |
| │ |
| Base64 encode |
| │ |
| Claude VLM Judge × 3 |
| │ |
| Average scores |
| │ |
| Pass / Fail determination |
| ``` |
|
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| --- |
|
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| ## Output Fields |
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| Each evaluated task produces the following fields: |
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| | Field | Type | Description | |
| |-------|------|-------------| |
| | `task_no` | int | Task ID | |
| | `level` | str | `Level1` / `Level2` / `Level3` | |
| | `model` | str | Model identifier | |
| | `file_type` | str | `html_screenshot` / `png` / `jpg` | |
| | `score` | float | Average normalized score (0–100) | |
| | `score_raw` | float | Average raw score (0–130) | |
| | `spatial_location` | float | Average spatial location score | |
| | `geometry_validity` | float | Average geometry validity score | |
| | `numeric_match` | float | Average numeric match score | |
| | `pass` | bool | `True` if score ≥ 60 | |
| | `reason` | str | VLM reason summary | |
| | `scores_3x` | str | Individual normalized scores from 3 runs | |
| | `scores_3x_raw` | str | Individual raw scores from 3 runs | |
|
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| --- |
|
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| ## Evaluation Script |
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| The full evaluation pipeline is available at: |
| [`evaluation/run_evaluation_vlm_v3.py`](run_evaluation_vlm_v3.py) |
|
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| ### Requirements |
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| ``` |
| anthropic |
| playwright |
| openpyxl |
| pandas |
| ``` |
|
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| ### Usage |
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| ```bash |
| # Set API key |
| set ANTHROPIC_API_KEY=your_key_here # Windows |
| export ANTHROPIC_API_KEY=your_key_here # Linux/Mac |
| |
| # Install Playwright browser |
| playwright install chromium |
| |
| # Run evaluation |
| python run_evaluation_vlm_v3.py |
| ``` |
|
|
| ### Directory Structure Expected |
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| ``` |
| C:\Users\A\Desktop\대학원 졸업논문\ |
| ├── GIS_Bench_문항.xlsx # Task prompts (Excel) |
| ├── claude_mcp\ # Model output files |
| │ ├── 1.html |
| │ ├── 2.png |
| │ └── ... |
| ├── claude_baseline\ |
| └── gpt_5.2\ |
| ``` |
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| > **Note:** Update `BASE_DIR` in the script to match your local environment. |
| |
| --- |
| |
| ## Reliability & Validity |
| |
| - **Inter-run consistency:** Each task evaluated 3 times; average used to reduce VLM variance |
| - **Prompt robustness:** JSON-only output enforced; fallback parsing with regex extraction |
| - **Rate limit handling:** 1.5s sleep between API calls |
| - **Token safety:** `max_tokens=1024` to prevent JSON truncation |
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|
