geoskill-sar-landslide-detection

SAR 滑坡检测 | SAR Landslide Detection

Identifies suspected landslide bodies by fusing multi-source SAR-derived factors:

• InSAR deformation rate (mm/yr): landslide bodies exhibit high deformation along the line of sight (absolute value taken).
• Backscatter change: sliding / churning alters surface roughness, producing large σ⁰ differences between before and after.
• Slope (derived from a DEM via Horn's gradient method): landslides mostly occur on steep slopes, making slope a key constraint.

Method: the three factors are normalized using robust percentile scaling (--normalize robust|minmax), then weighted to compute an integrated risk score; suspected areas are extracted with the dual thresholds score ≥ --score-threshold and slope ≥ --slope-threshold, cleaned morphologically, vectorized into polygons by connected components, and classified into risk levels.

Dependencies / 依赖

pip install numpy rasterio geopandas shapely scipy

Usage / 使用方法

Basic usage (bbox only, synthetic data auto-generated)

python geoskill-sar-landslide-detection.py --bbox 116.0 39.0 117.0 40.0 --slope-threshold 15 --output-dir ./out

Example 1: synthetic data (offline)

python geoskill-sar-landslide-detection.py --bbox 116 39 117 40 --synthetic --output-dir ./syn

Example 2: real deformation rate + DEM

python geoskill-sar-landslide-detection.py --input deform_rate.tif --dem dem.tif --output-dir ./real

Example 3: with before/after σ⁰ imagery

python geoskill-sar-landslide-detection.py --input deform.tif --dem dem.tif --sigma-before s0_before.tif --sigma-after s0_after.tif --output-dir ./full

Example 4: lower thresholds for higher sensitivity

python geoskill-sar-landslide-detection.py --bbox 103 30 104 31 --slope-threshold 10 --score-threshold 0.4 --output-dir ./sensitive --quiet

Output / 输出

Data Source / 数据源 / Source

• Real mode: local InSAR deformation-rate GeoTIFF, with optional DEM and before/after σ⁰ imagery.
• Synthetic mode: locally generated DEM slopes + localized high-deformation patches + σ⁰ anomalies.

Privacy / 隐私声明 / Privacy

• Fully offline by default; --synthetic mode makes no network calls.
• All processing is done locally; no user data is uploaded.

License / License

MIT

---

---

name: geoskill-sar-landslide-detection description: '融合InSAR形变速率、后向散射变化与DEM坡度综合加权评分，双门限提取疑似滑坡连通域并矢量化分级（high/medium/low），输出滑坡GeoJSON、形变速率/风险评分GeoTIFF与风险汇总JSON。SAR landslide detection fusing InSAR deformation, backscatter change and slope.'

SAR 滑坡检测 | SAR Landslide Detection

融合多源 SAR 派生因子识别疑似滑坡体：

• InSAR 形变速率（mm/yr）：滑坡体在视线向上表现为高形变（取绝对值）。
• 后向散射变化：滑动 / 翻搅使地表粗糙度改变，σ⁰ 前后差异大。
• 坡度（由 DEM 经 Horn 梯度法求）：滑坡多发生在陡坡，是关键约束。

方法：三因子稳健百分位归一化（--normalize robust|minmax）后加权求综合风险 评分，再用 score ≥ --score-threshold 且 slope ≥ --slope-threshold 双门限 提取疑似区，形态学清理后按连通域矢量化为多边形并分级。

依赖

pip install numpy rasterio geopandas shapely scipy

使用方法

基本用法（仅给 bbox，自动合成）

python geoskill-sar-landslide-detection.py --bbox 116.0 39.0 117.0 40.0 --slope-threshold 15 --output-dir ./out

示例 1：合成数据（离线）

python geoskill-sar-landslide-detection.py --bbox 116 39 117 40 --synthetic --output-dir ./syn

示例 2：真实形变速率 + DEM

python geoskill-sar-landslide-detection.py --input deform_rate.tif --dem dem.tif --output-dir ./real

示例 3：含 σ⁰ 前后影像

python geoskill-sar-landslide-detection.py --input deform.tif --dem dem.tif --sigma-before s0_before.tif --sigma-after s0_after.tif --output-dir ./full

示例 4：降低门限提高灵敏度

python geoskill-sar-landslide-detection.py --bbox 103 30 104 31 --slope-threshold 10 --score-threshold 0.4 --output-dir ./sensitive --quiet

输出

数据源 / Source

• 真实模式：本地 InSAR 形变速率 GeoTIFF，可选 DEM 与 σ⁰ 前后影像。
• 合成模式：本地生成 DEM 斜坡 + 局部高形变斑块 + σ⁰ 异常。

隐私声明 / Privacy

• 默认完全离线运行，--synthetic 无任何网络。
• 所有处理本地完成，不上传用户数据。

License

MIT