Install
openclaw skills install @ruiduobao/geoskill-urban-microclimateAnalyze urban heat island intensity and ventilation index from land surface temperature, NDVI, impervious surface and building morphology.
openclaw skills install @ruiduobao/geoskill-urban-microclimateAnalyzes urban microclimate from land surface temperature, vegetation, impervious surface and building morphology, supporting heat island mitigation and ventilation planning.
Core algorithm: LST modeling = baseline temperature + α×ISA − β×NDVI (impervious surfaces heat, vegetation cools through evapotranspiration); heat island intensity UHII = LST − rural reference temperature, positively correlated with ISA; ventilation index VI = SVF×(1−building density) ∈ [0,1] — high density combined with a low sky view factor indicates poor ventilation.
pip install 'numpy' 'rasterio'
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 [other parameters]
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
python geoskill-urban-microclimate.py --input features.tif --output-dir ./out
python geoskill-urban-microclimate.py --bbox 121.0 31.0 122.0 32.0 --rural-temp 23 --output-dir ./out --quiet
python geoskill-urban-microclimate.py --input features.tif --alpha 12 --beta 5 --output-dir ./out
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 --synthetic --base-temp 26 --output-dir ./out --quiet
| File | Format | Description |
|---|---|---|
microclimate.tif | GeoTIFF | Three bands: band1=LST, band2=heat island intensity UHII, band3=ventilation index |
microclimate_stats.json | JSON | Mean LST/UHII, max UHII, ventilation index, UHII-ISA correlation coefficient |
output-manifest.json | JSON | Run manifest |
Local four-band GeoTIFF (ISA, NDVI, building density, SVF); --synthetic mode simulates a center-to-suburb heat island gradient scenario.
--synthetic mode requires no network at all.MIT
从地表温度、植被、不透水面与建筑形态分析城市微气候,服务于热岛缓解与通风规划。
核心算法:LST 建模 = 基准温度 + α×ISA − β×NDVI(不透水面加热、植被蒸散降温);热岛强度 UHII = LST − 郊区参考温度,与 ISA 正相关;通风指数 VI = SVF×(1−建筑密度) ∈ [0,1],密度高且天空可视因子低 → 通风差。
pip install 'numpy' 'rasterio'
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
python geoskill-urban-microclimate.py --input features.tif --output-dir ./out
python geoskill-urban-microclimate.py --bbox 121.0 31.0 122.0 32.0 --rural-temp 23 --output-dir ./out --quiet
python geoskill-urban-microclimate.py --input features.tif --alpha 12 --beta 5 --output-dir ./out
python geoskill-urban-microclimate.py --bbox 116.0 39.0 117.0 40.0 --synthetic --base-temp 26 --output-dir ./out --quiet
| 文件 | 格式 | 说明 |
|---|---|---|
microclimate.tif | GeoTIFF | 三波段:band1=LST,band2=热岛强度 UHII,band3=通风指数 |
microclimate_stats.json | JSON | 平均 LST/UHII、最大 UHII、通风指数、UHII-ISA 相关系数 |
output-manifest.json | JSON | 运行清单 |
本地四波段 GeoTIFF(ISA, NDVI, 建筑密度, SVF);--synthetic 模式模拟中心-郊区热岛梯度场景。
--synthetic 模式完全无网络。MIT