Documents
Multi-criteria suitability analysis for infrastructure site selection. Use when the user wants to analyze changes, compare multi-temporal rasters, compute indices, or generate assessment reports.
Multi-criteria suitability analysis for infrastructure site selection. Use when the user wants to analyze changes, compare multi-temporal rasters, compute indices, or generate assessment reports.
Multi-criteria suitability analysis for infrastructure site selection.
python scripts/infrastructure_site_selection.py --slope slope.tif
python scripts/infrastructure_site_selection.py --slope slope.tif --landcover lc.tif --roads-proximity roads.tif
python scripts/infrastructure_site_selection.py --slope slope.tif --output-dir my_output
| Argument | Required | Default | Description |
|---|---|---|---|
--slope | Yes | — | Path to slope raster (degrees) |
--landcover | No | — | Optional land cover raster (categorical) |
--roads-proximity | No | — | Optional distance-to-roads raster (m) |
--elevation | No | — | Optional elevation raster (m) |
--output-dir, -o | No | site-selection-output | Directory to write outputs |
| File | Description |
|---|---|
suitability-report.json | Machine-readable suitability stats (mean, area by class) |
report.html | Human-readable HTML report |
output-manifest.json | Run metadata + result summary |
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Argument error (e.g. missing input file) |
| 7 | Processing failure |
本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):
python infrastructure_site_selection.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir
--bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)--date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)--aoi-file : 替代 --bbox 的 GeoJSON 多边形--cache-dir : 缓存目录 (默认 ~/.geoskill_cache)当用户只给 --bbox + --date-range (没有 --elevation) 时，skill 自动下载数据。
当用户给 --elevation 时，走原文件路径 (向后兼容)。
Multi-criteria site selection for solar PV and wind projects. Applies hard constraints (slope, water, protected areas) and weighted suitability analysis. Use when screening for renewable energy development zones or comparing site alternatives.
Analyze land subsidence from InSAR displacement data. Use when the user wants to analyze changes, detect hazards, or generate assessment reports.
Detect and quantify urban expansion from multi-temporal built-up rasters. Use when the user wants to analyze changes, compare multi-temporal rasters, compute indices, or generate assessment reports.
由多变量环境栅格训练物种分布模型，输出 0-1 栖息地适宜性概率与变量贡献度。Models habitat suitability probability from environmental rasters with RF or logistic regression. 输出适宜性 GeoTIFF + 模型参数 JSON（含交叉验证 AUC）。
Estimate impervious surface fraction from multi-band satellite imagery (Sentinel-2) using spectral indices (NDBI, NDVI, MNDWI). Supports binary classification and continuous fraction estimation, with zone-level aggregation and change detection. Use when mapping urban impervious surfaces, computing impervious ratios by watershed/admin unit, or analyzing temporal changes in built-up areas.
Detect informal settlements by fusing texture irregularity, building morphology and spectral mixing into a classification score.