geoskill-archaeology-site-detection

LiDAR micro-topography, multispectral anomaly and SAR fusion for suspected archaeological site detection with anomaly grading

考古遗址遥感探测 | Archaeological Site Detection

Fuses LiDAR micro-topography, multispectral vegetation anomalies and SAR backscatter to automatically screen suspected archaeological sites and assign anomaly grades, providing remote sensing leads for large-scale archaeological surveys.

The method works in three layers: a large-window detrending of the DEM extracts local relief (highlighting micro-landforms such as mounds and depressions); NDVI is computed and detrended to identify crop marks caused by buried remains; and SAR backscatter is z-scored to detect moisture/structural anomalies. The three anomaly layers are normalized, fused with weights (or by maximum), graded by thresholds (none/low/high), and local peaks are used to locate suspected site points.

Dependencies / 依赖

bash

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

Basic Usage

bash

python geoskill-archaeology-site-detection.py --bbox 116.0 39.0 117.0 40.0 [other parameters]

Example 1 (Synthetic Data, Offline)

bash

python geoskill-archaeology-site-detection.py --bbox 116 39 117 40 --synthetic --output-dir ./out

示例 2（真实多波段影像（DEM/Red/NIR/SAR））

bash

python geoskill-archaeology-site-detection.py --input scene.tif --output-dir ./out

Example 3 (Switch to Maximum-Value Fusion)

bash

python geoskill-archaeology-site-detection.py --input scene.tif --fusion max --output-dir ./out

Example 4 (Adjusting Weights and Classification Thresholds)

bash

python geoskill-archaeology-site-detection.py --input scene.tif --w-relief 0.5 --w-spectral 0.3 --w-sar 0.2 --high-threshold 0.8 --output-dir ./out

Example 5 (Larger Detrending Window (Regional Landforms))

bash

python geoskill-archaeology-site-detection.py --input scene.tif --window 25 --footprint 9 --output-dir ./out

Output / 输出

File	Format	Description
anomaly_score.tif	GeoTIFF	Fused anomaly score [0,1]
anomaly_level.tif	GeoTIFF	Anomaly grade (0 none / 1 low / 2 high)
suspected_sites.geojson	GeoJSON	Suspected site points (with score and grade)
detection_report.json	JSON	Summary statistics and top sites
output-manifest.json	JSON	Run manifest

Data Source / 数据源 / Source

Multi-band GeoTIFF with band order DEM / Red / NIR / SAR. Or use --synthetic to generate physically consistent simulated data (fully offline).

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is done locally; user data is never uploaded.

License / License

MIT

---

---

name: geoskill-archaeology-site-detection description: 'LiDAR micro-topography, multispectral anomaly and SAR fusion for suspected archaeological site detection with anomaly grading'

考古遗址遥感探测 | Archaeological Site Detection

融合 LiDAR 微地形、多光谱植被异常与 SAR 后向散射，自动筛查疑似考古遗址并给出异常等级，为大范围考古调查提供遥感线索。

方法分三层：对 DEM 做大窗口去趋势提取局部起伏（突出土丘/凹陷等微地貌）；计算 NDVI 并去趋势识别地下遗存导致的作物标志 (crop mark)；对 SAR 后向散射做 z-score 识别湿度/结构异常。三层异常归一化后按权重融合（或取最大），阈值分级（无/低/高）并用局部峰值定位疑似遗址点。

依赖

bash

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法

bash

python geoskill-archaeology-site-detection.py --bbox 116.0 39.0 117.0 40.0 [其他参数]

示例 1（合成数据，离线）

bash

python geoskill-archaeology-site-detection.py --bbox 116 39 117 40 --synthetic --output-dir ./out

示例 2（真实多波段影像（DEM/Red/NIR/SAR））

bash

python geoskill-archaeology-site-detection.py --input scene.tif --output-dir ./out

示例 3（改用最大值融合）

bash

python geoskill-archaeology-site-detection.py --input scene.tif --fusion max --output-dir ./out

示例 4（调整权重与分级阈值）

bash

python geoskill-archaeology-site-detection.py --input scene.tif --w-relief 0.5 --w-spectral 0.3 --w-sar 0.2 --high-threshold 0.8 --output-dir ./out

示例 5（更大去趋势窗口（区域地貌））

bash

python geoskill-archaeology-site-detection.py --input scene.tif --window 25 --footprint 9 --output-dir ./out

输出

文件	格式	说明
anomaly_score.tif	GeoTIFF	融合异常评分 [0,1]
anomaly_level.tif	GeoTIFF	异常分级（0 无 / 1 低 / 2 高）
suspected_sites.geojson	GeoJSON	疑似遗址点（含评分与等级）
detection_report.json	JSON	统计摘要与 Top 站点
output-manifest.json	JSON	运行清单

数据源 / Source

多波段 GeoTIFF，波段顺序 DEM / Red / NIR / SAR。 或使用 --synthetic 生成物理一致的模拟数据（完全离线）。

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