野火蔓延模拟 | Wildfire Spread Modeling
Cellular automaton (CA) based wildfire spread simulation: at each time step, burning pixels ignite unburned pixels in their 8-neighborhood with a given probability; the ignition probability integrates fuel flammability, moisture (wetter fuel is harder to ignite), slope (faster uphill), and wind speed (faster downwind, including the angle relative to wind direction). Once ignited, a pixel never goes out, so the burned area grows monotonically with time; under a fixed random sequence, stronger wind burns more and higher moisture burns less.
Dependencies / 依赖
pip install numpy rasterio scipy
Usage / 使用方法
Basic Usage (Synthetic Data, Offline)
python geoskill-wildfire-spread-modeling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
More Examples
python geoskill-wildfire-spread-modeling.py --bbox 116 39 117 40 --synthetic --output-dir ./out
python geoskill-wildfire-spread-modeling.py --input fuel.tif --steps 25 --output-dir ./out
python geoskill-wildfire-spread-modeling.py --bbox 116 39 117 40 --wind-speed 4 --wind-dir 90 --steps 20 --synthetic --output-dir ./out
python geoskill-wildfire-spread-modeling.py --bbox 110 35 111 36 --synthetic --quiet --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
burned_area.tif |
GeoTIFF | Final burned extent |
arrival_time.tif |
GeoTIFF | Arrival time step (unburned=-1) |
fire_params.json |
JSON | Per-time-step burned area series and parameters |
Each run also produces output-manifest.json (run manifest, including inputs/outputs/QA summary).
Data Source / 数据源 / Source
Real mode reads a multi-band GeoTIFF (band1=fuel 0-1, band2=moisture 0-1, band3=slope 0-1); synthetic mode generates fire scenarios offline.
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network at all. - All processing is done locally; no user data is uploaded.
License / License
MIT
name: geoskill-wildfire-spread-modeling description: '元胞自动机叠加坡度风速燃料湿度修正的过火面积模拟'
野火蔓延模拟 | Wildfire Spread Modeling
基于元胞自动机（CA）的野火蔓延模拟：每个时间步，燃烧像元向 8 邻域未燃像元以概率点火，点火概率综合燃料可燃性、湿度（越湿越难点燃）、坡度（上坡更快）与风速（顺风更快，含风向夹角）。一旦点燃永不熄灭，过火面积随时间单调不减；固定随机序列下，风速越大过火越多、湿度越大过火越少。
依赖
pip install numpy rasterio scipy
使用方法
基本用法（合成数据，离线）
python geoskill-wildfire-spread-modeling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
更多示例
python geoskill-wildfire-spread-modeling.py --bbox 116 39 117 40 --synthetic --output-dir ./out
python geoskill-wildfire-spread-modeling.py --input fuel.tif --steps 25 --output-dir ./out
python geoskill-wildfire-spread-modeling.py --bbox 116 39 117 40 --wind-speed 4 --wind-dir 90 --steps 20 --synthetic --output-dir ./out
python geoskill-wildfire-spread-modeling.py --bbox 110 35 111 36 --synthetic --quiet --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
burned_area.tif |
GeoTIFF | 最终过火范围 |
arrival_time.tif |
GeoTIFF | 到达时间步（未燃=-1） |
fire_params.json |
JSON | 逐时间步过火面积序列与参数 |
每次运行还会产出 output-manifest.json（运行清单，含输入/产物/QA 摘要）。
数据源 / Source
真实模式读取多波段 GeoTIFF（band1=燃料0-1、band2=湿度0-1、band3=坡度0-1）；合成模式离线生成火场。
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，不上传任何用户数据。
License
MIT