深度学习变化检测 | Deep Learning Change Detection
Detects land surface changes from bi-temporal imagery (vegetation degradation, urban expansion, water body growth/shrinkage), outputting a change probability map, a binary change map, and change-region polygons as GeoJSON.
This skill uses a genuine deep learning model: a Siamese fully convolutional change detection network (FC-Siam-diff style, Daudt et al. 2018). The two epochs (red/nir two-channel) pass through a shared-weight encoder to extract multi-scale features, and the decoder fuses layer-wise feature differences |f1 − f2| to reconstruct pixel-level change probabilities (sigmoid, [0, 1]), which are then thresholded via --prob-thresh, aggregated into change regions through 8-neighborhood connected-component analysis, and geocoded. Both training and inference run on CUDA GPUs (torch ≥ 2.x, requiring cuDNN or automatically falling back to native CUDA convolutions).
The skill ships with pretrained weights cd_siamese_weights.pt (about 0.6 MB, trained on synthetic bi-temporal change pairs, holdout recall ≈ 0.999 / false alarm ≈ 0). If the weight file is missing, the first run automatically trains on synthetic data on the GPU (about 15 seconds) and caches the weights to disk.
Dependencies / 依赖
pip install numpy rasterio scipy torch
Usage / 使用方法
Example 1 (Synthetic Data, Offline)
python geoskill-change-detection-dl.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2: Two Files for Bi-temporal Input (Each with 2 Bands [red, nir] Reflectance)
python geoskill-change-detection-dl.py --input t1.tif --input2 t2.tif --prob-thresh 0.6 --output-dir ./out
Example 3: Single File with 4 Bands [red1, nir1, red2, nir2]
python geoskill-change-detection-dl.py --input pair.tif --min-area 16 --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
change_probability.tif |
GeoTIFF | Change probability [0, 1] (network sigmoid output) |
change_binary.tif |
GeoTIFF | Binary change map |
change_regions.geojson |
GeoJSON | Change-region polygons + area attributes |
output-manifest.json |
JSON | Run manifest (inputs/outputs/QA/exit code/model metadata) |
In synthetic mode, QA writes synthetic_recall / synthetic_false_alarm (computed against the built-in ground truth).
Limitations / 局限（诚实声明）
- The bundled weights were trained on synthetic spectra (four reflectance classes: vegetation/bare soil/water/built-up); the probability outputs on real imagery have not been radiometrically calibrated or field-validated, so results on real data should be treated as screening-level rather than map-grade.
--scaleis the steepness parameter of the legacy classical baseline (1−exp(−scale·|dNDVI|)); it is kept only for CLI compatibility and is not used by the network probability path.
Data Source / 数据源 / Source
Local bi-temporal GeoTIFFs (each containing red/nir bands, or a single 4-band file), or a --synthetic pair (t1 fully vegetated, t2 degraded to bare soil at the center).
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-change-detection-dl description: 'Siamese 全卷积网络(FC-Siam-diff 风格)双时相变化检测：GPU 训练/推理，输出变化概率、二值变化图与变化图斑'
深度学习变化检测 | Deep Learning Change Detection
从双时相影像检测地表变化（植被退化、城市扩张、水体消长），输出变化概率图、二值变化图与变化图斑 GeoJSON。
本 skill 使用真正的深度学习模型：Siamese 全卷积变化检测网络（FC-Siam-diff 风格，
Daudt et al. 2018）。两个时相（red/nir 双通道）经共享权重编码器提取多尺度特征，
解码器融合逐层特征差 |f1 − f2| 重建像元级变化概率（sigmoid，[0,1]），
再经 --prob-thresh 阈值二值化、8 邻域连通域聚合为变化图斑并地理编码。
训练与推理均在 CUDA GPU 上执行（torch ≥ 2.x，需 cuDNN 或自动退回 CUDA 原生卷积）。
随 skill 附带预训练权重 cd_siamese_weights.pt（约 0.6 MB，在合成双时相变化对上
训练，holdout recall ≈ 0.999 / false-alarm ≈ 0）。若权重文件缺失，首次运行时
自动在 GPU 上用合成数据训练（约 15 秒）并落盘缓存。
依赖
pip install numpy rasterio scipy torch
使用方法
示例 1（合成数据，离线）
python geoskill-change-detection-dl.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2：双文件双时相（各 2 波段 [red, nir] 反射率）
python geoskill-change-detection-dl.py --input t1.tif --input2 t2.tif --prob-thresh 0.6 --output-dir ./out
示例 3：单文件 4 波段 [red1,nir1,red2,nir2]
python geoskill-change-detection-dl.py --input pair.tif --min-area 16 --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
change_probability.tif |
GeoTIFF | 变化概率 [0,1]（网络 sigmoid 输出） |
change_binary.tif |
GeoTIFF | 二值变化图 |
change_regions.geojson |
GeoJSON | 变化图斑多边形 + 面积属性 |
output-manifest.json |
JSON | 运行清单（输入/输出/QA/退出码/模型元信息） |
合成模式 QA 会写入 synthetic_recall / synthetic_false_alarm（对内置真值计算）。
局限（诚实声明）
- 随附权重在合成光谱（植被/裸土/水体/建成区四类反射率）上训练，对真实影像的 概率输出未做辐射定标/外场验证；真实数据结果应视为筛查级而非制图级。
--scale为旧版经典基线（1−exp(−scale·|dNDVI|)）的陡峭度参数，保留仅为 CLI 兼容，网络概率路径不使用它。
数据源 / Source
本地双时相 GeoTIFF（各含 red/nir 波段，或单文件 4 波段），或 --synthetic 合成对 （t1 全植被，t2 中心退化为裸土）。
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