geoskill-bare-soil-mapping

裸土/裸地制图 | Bare Soil Mapping

Fuses three complementary features to extract the distribution of bare soil/bare land, distinguishing bare soil from vegetation, urban built-up areas, and water bodies. Suitable for soil erosion baseline surveys, early identification of desertification, verification of exposed land at construction sites, and land cover mapping in arid regions.

Core algorithm:

• BSI (Bare Soil Index): BSI = ((SWIR+Red) − (NIR+Blue)) / ((SWIR+Red) + (NIR+Blue)). Bare soil exhibits high reflectance in the red and shortwave infrared bands and relatively low reflectance in the near-infrared, yielding high BSI values; vegetation produces negative BSI values due to its high NIR reflectance.
• Brightness: mean multi-band reflectance, used to exclude dark water bodies.
• Texture: local standard deviation. Bare soil surfaces are homogeneous with low contrast, whereas urban areas are heterogeneous with high texture.
• Thresholding: the membership values of the three features are multiplied to obtain a score in [0, 1]; --threshold auto applies the Otsu method for automatic thresholding, or an explicit float in [0, 1] can be given.

Supports --synthetic mode to generate physically consistent scenes containing bare soil, vegetation, urban areas, and water bodies (offline).

Dependencies / 依赖

pip install numpy rasterio scipy

Usage / 使用方法

Basic Usage (Synthetic Data + Automatic Threshold, Offline)

python geoskill-bare-soil-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold auto --output-dir ./output

Example 1: Explicit Threshold

python geoskill-bare-soil-mapping.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --threshold 0.4 \
    --output-dir ./thr

Example 2: Adjusting the Texture Window

python geoskill-bare-soil-mapping.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --texture-size 7 \
    --output-dir ./tex

Example 3: Real Multi-band Imagery

python geoskill-bare-soil-mapping.py \
    --input scene.tif \
    --threshold auto \
    --output-dir ./real

Input band order: blue / green / red / nir / swir (at least 5 bands).

Output / 输出

Data Source / 数据源 / Source

• Synthetic mode: generated locally, no external data source
• Real mode: user-provided multi-band surface reflectance GeoTIFF (e.g., Landsat / Sentinel-2)

Privacy / 隐私声明 / Privacy

• Runs fully offline by default; makes no network requests
• All computation is performed locally; no user data is uploaded

License / License

MIT

---

---

name: geoskill-bare-soil-mapping description: '融合裸土指数 BSI、亮度与局部纹理（裸土低对比度）阈值提取裸土/裸地分布，支持 Otsu 自动阈值，输出裸土 GeoTIFF、BSI 栅格与面积统计。Maps bare soil by fusing BSI, brightness and local texture.'

裸土/裸地制图 | Bare Soil Mapping

融合三个互补特征提取裸土/裸地分布，把裸土与植被、城镇建筑、水体区分开。 适用于土壤侵蚀本底调查、荒漠化早期识别、建设用地裸地核查与干旱区地表覆盖制图。

核心算法：

• BSI（裸土指数）：BSI = ((SWIR+Red) − (NIR+Blue)) / ((SWIR+Red) + (NIR+Blue))。 裸土红光与短波红外高反射、近红外相对低，BSI 偏高；植被因 NIR 高而 BSI 为负。
• 亮度（brightness）：多波段反射率均值，用于排除暗色水体。
• 纹理（texture）：局部标准差。裸土表面均一、对比度低；城镇异质、纹理高。
• 阈值化：三者隶属度相乘得到得分 [0,1]；--threshold auto 用大津法（Otsu） 自动阈值，或显式给定 [0,1] 浮点数。

支持 --synthetic 模式生成含裸土/植被/城镇/水体的物理一致场景（离线）。

依赖

pip install numpy rasterio scipy

使用方法

基本用法（合成数据 + 自动阈值，离线）

python geoskill-bare-soil-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold auto --output-dir ./output

示例 1：显式阈值

python geoskill-bare-soil-mapping.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --threshold 0.4 \
    --output-dir ./thr

示例 2：调整纹理窗口

python geoskill-bare-soil-mapping.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --texture-size 7 \
    --output-dir ./tex

示例 3：真实多波段影像

python geoskill-bare-soil-mapping.py \
    --input scene.tif \
    --threshold auto \
    --output-dir ./real

输入波段顺序：blue / green / red / nir / swir（至少 5 波段）。

输出

数据源 / Source

• 合成模式：本地生成，无外部数据源
• 真实模式：用户提供多波段地表反射率 GeoTIFF（如 Landsat / Sentinel-2）

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求
• 所有计算在本地完成，不上传用户数据

License

MIT