geoskill-lidar-ground-classification

渐进形态学滤波PMF/坡度滤波分离地面点并生成DTM

Install

openclaw skills install @ruiduobao/geoskill-lidar-ground-classification

LiDAR 地面点分类 | LiDAR Ground Point Classification

Separates a LiDAR point cloud (N×3 xyz) into ground points (ASPRS class 2) and non-ground points (class 1), then interpolates a bare-earth Digital Terrain Model (DTM). Two filtering methods are implemented:

PMF (Progressive Morphological Filter, a simplified version of Zhang et al. 2003): rasterizes the point cloud into a minimum-elevation surface, then applies morphological opening with progressively larger windows; protruding pixels whose elevation difference exceeds an adaptive threshold (dh = dh0 + slope × window width) are trimmed to yield the ground surface;
slope (slope filtering): estimates the terrain from a large-window minimum-elevation surface, then suppresses steeply varying areas based on local slope anomalies.

Per-point classification rule: a point is classified as ground when the difference between its elevation and the gridded ground surface is ≤ z_tolerance. Synthetic mode generates a simulated point cloud of smooth terrain + buildings (rectangular boxes) + trees (Gaussian crowns), enabling offline validation of classification accuracy. Suited to DEM/DTM production, terrain analysis, and point-cloud preprocessing pipelines.

Dependencies / 依赖

bash

pip install numpy rasterio scipy

Usage / 使用方法

Basic usage (synthetic data, offline)

bash

python geoskill-lidar-ground-classification.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 1: PMF with default parameters

bash

python geoskill-lidar-ground-classification.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --method pmf --cell-size 1.0 \
    --output-dir ./pmf

Example 2: slope filtering with a relaxed tolerance

bash

python geoskill-lidar-ground-classification.py \
    --bbox 121.0 31.0 122.0 32.0 \
    --synthetic --method slope --z-tolerance 0.8 \
    --output-dir ./slope

Example 3: real point cloud input (.npy / .csv / .txt xyz)

bash

python geoskill-lidar-ground-classification.py \
    --input tile_001.npy --method pmf --cell-size 2.0 \
    --output-dir ./real_pmf

Example 4: coarse-resolution quick preview

bash

python geoskill-lidar-ground-classification.py \
    --input tile_001.csv --cell-size 5.0 --quiet \
    --output-dir ./coarse

Output / 输出

File	Format	Description
`classified_points.npy`	NumPy (N×4)	xyz + class code (2=ground, 1=non-ground)
`dtm.tif`	GeoTIFF (float32)	DTM interpolated from ground points, EPSG:4326
`density.tif`	GeoTIFF (float32)	Point density map (points per grid cell)
`stats.json`	JSON	Ground point ratio, accuracy statistics (synthetic mode includes comparison against ground truth)
`output-manifest.json`	JSON	Run manifest (input/output/QA/software versions)

Data Source / 数据源 / Source

Real mode: local point cloud files (.npy / .csv / .txt, at least 3 columns of xyz, projected or local metric coordinates recommended)
Synthetic mode: locally generated point cloud of smooth terrain + buildings + trees; no external data source

Privacy / 隐私声明 / Privacy

Runs fully offline; accesses no network services
All processing is done locally; no user data is uploaded

License / License

MIT

name: geoskill-lidar-ground-classification description: '渐进形态学滤波PMF/坡度滤波分离地面点并生成DTM'

LiDAR 地面点分类 | LiDAR Ground Point Classification

把 LiDAR 点云（N×3 xyz）分离为地面点（ASPRS class 2）与非地面点（class 1），并内插生成裸地数字地形模型 DTM。实现两种滤波方法：

PMF（Progressive Morphological Filter，Zhang et al. 2003 简化版）：把点云栅格化为最低高程面，逐级增大窗口做形态学开运算，高程差超过自适应阈值（dh = dh0 + slope × 窗宽）的突起像元被削平，得到地面面；
slope（坡度滤波）：大窗口最低值面估计地形，再按局部坡度异常抑制陡变区域。

逐点分类规则：点高程与格网地面面之差 ≤ z_tolerance → 地面点。合成模式生成平滑地形 + 建筑（长方体）+ 树木（高斯冠）的模拟点云，可离线验证分类精度。适用于 DEM/DTM 生产、地形分析、点云预处理流水线。

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-lidar-ground-classification.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 1：PMF 默认参数

bash

python geoskill-lidar-ground-classification.py \
    --bbox 116.0 39.0 117.0 40.0 \
    --synthetic --method pmf --cell-size 1.0 \
    --output-dir ./pmf

示例 2：坡度滤波 + 放宽容差

bash

python geoskill-lidar-ground-classification.py \
    --bbox 121.0 31.0 122.0 32.0 \
    --synthetic --method slope --z-tolerance 0.8 \
    --output-dir ./slope

示例 3：真实点云输入（.npy / .csv / .txt xyz）

bash

python geoskill-lidar-ground-classification.py \
    --input tile_001.npy --method pmf --cell-size 2.0 \
    --output-dir ./real_pmf

示例 4：粗分辨率快速预览

bash

python geoskill-lidar-ground-classification.py \
    --input tile_001.csv --cell-size 5.0 --quiet \
    --output-dir ./coarse

输出

文件	格式	说明
`classified_points.npy`	NumPy (N×4)	xyz + 分类码（2=地面，1=非地面）
`dtm.tif`	GeoTIFF (float32)	地面点插值 DTM，EPSG:4326
`density.tif`	GeoTIFF (float32)	点密度图（点/格网）
`stats.json`	JSON	地面点比例、精度统计（合成模式含真值对比）
`output-manifest.json`	JSON	运行清单（输入/输出/QA/软件版本）

数据源 / Source

真实模式：本地点云文件（.npy / .csv / .txt，至少 3 列 xyz，建议投影或局部米制坐标）
合成模式：本地生成平滑地形 + 建筑 + 树木点云，无外部数据源

隐私声明 / Privacy

完全离线运行，不访问任何网络服务
所有处理在本地完成，不上传任何用户数据

License

MIT