Estimate population density from building volume, residential ratio, night-light correction and land-cover weights with total conservation.
1 次安装
编程
geoskill-building-density-mapping
试用Estimate building footprint density and floor area ratio (FAR) from building footprints and heights using kernel density estimation.
它能做什么
Estimate building footprint density and floor area ratio (FAR) from building footprints and heights using kernel density estimation.
技能文档
建筑密度制图 | Building Density Mapping
Estimates building density (building coverage ratio) and floor area ratio (FAR) from building footprint rasters, for urban form analysis, development intensity assessment, and planning management.
Core algorithm: takes a binary building footprint raster as input and applies local mean convolution with a square kernel to obtain a continuous density field in [0, 1]; FAR is then computed as FAR = building density × (building height / standard floor height). The density kernel is conservative, density equals 1 in purely built-up areas and 0 in purely vacant land, and FAR satisfies an analytical relationship with height/floor height.
Dependencies / 依赖
pip install 'numpy' 'rasterio' 'scipy'
Usage / 使用方法
Basic Usage
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 [other parameters]
Examples
Example 1 (Synthetic Data (Offline))
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2 (Usage 2)
python geoskill-building-density-mapping.py --input footprints.tif --heights heights.tif --output-dir ./out
Example 3 (Usage 3)
python geoskill-building-density-mapping.py --bbox 121.0 31.0 122.0 32.0 --kernel-size 7 --output-dir ./out --quiet
Example 4 (Usage 4)
python geoskill-building-density-mapping.py --input fp.tif --floor-height 3.5 --output-dir ./out
Example 5 (Usage 5)
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --kernel-size 3 --output-dir ./out --quiet
Output / 输出
| File | Format | Description |
|---|---|---|
building_density.tif | GeoTIFF | Two bands: band1=building density, band2=floor area ratio (FAR) |
density_stats.json | JSON | Density/FAR statistics (mean, maximum, floor height) |
output-manifest.json | JSON | Run manifest |
Data Source / 数据源 / Source
Local building footprint + building height GeoTIFFs; --synthetic mode generates an offline simulated scene containing random building blocks.
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network access at all. - All processing is performed locally; no user data is uploaded.
License / License
MIT
name: geoskill-building-density-mapping description: 'Estimate building footprint density and floor area ratio (FAR) from building footprints and heights using kernel density estimation.'
建筑密度制图 | Building Density Mapping
从建筑足迹栅格估计建筑密度（建筑覆盖率）与容积率（FAR），用于城市形态分析、开发强度评估与规划管理。
核心算法：以建筑足迹二值栅格为输入，用方形核做局部均值卷积得到连续的密度场 [0,1]；再由 FAR = 建筑密度 × (建筑高度 / 标准层高) 计算容积率。密度核守恒、纯建筑区密度为 1、纯空地为 0，FAR 与高度/层高满足解析关系。
依赖
pip install 'numpy' 'rasterio' 'scipy'
使用方法
基本用法
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
示例
示例 1（合成数据（离线））
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2（用法 2）
python geoskill-building-density-mapping.py --input footprints.tif --heights heights.tif --output-dir ./out
示例 3（用法 3）
python geoskill-building-density-mapping.py --bbox 121.0 31.0 122.0 32.0 --kernel-size 7 --output-dir ./out --quiet
示例 4（用法 4）
python geoskill-building-density-mapping.py --input fp.tif --floor-height 3.5 --output-dir ./out
示例 5（用法 5）
python geoskill-building-density-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --kernel-size 3 --output-dir ./out --quiet
输出
| 文件 | 格式 | 说明 |
|---|---|---|
building_density.tif | GeoTIFF | 双波段：band1=建筑密度，band2=容积率 FAR |
density_stats.json | JSON | 密度/FAR 统计（均值、最大值、层高） |
output-manifest.json | JSON | 运行清单 |
数据源 / Source
本地建筑足迹 + 建筑高度 GeoTIFF；--synthetic 模式生成含随机建筑块的离线模拟场景。
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传用户数据。
License
MIT
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