geoskill-wildfire-spread-modeling

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

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

bash

pip install numpy rasterio scipy

Usage / 使用方法

Basic Usage (Synthetic Data, Offline)

bash

python geoskill-wildfire-spread-modeling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

More Examples

bash

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; --synthetic mode 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

bash

pip install numpy rasterio scipy

使用方法

基本用法（合成数据，离线）

bash

python geoskill-wildfire-spread-modeling.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

更多示例

bash

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