Geoskill: LiDAR Point Cloud Analysis

Read LAS/LAZ/COPC point clouds, compute statistics, classification QA, DEM/DSM/CHM rasters, cross-sections, density maps, and quality reports. Use when analyzing LiDAR point cloud data, generating terrain models, checking point cloud quality, or producing canopy height models.

Prerequisites / 先准备 X 文件

⚠️ 必读 — 本 skill 不属于即用型，需要先准备特定文件才能跑。

本 skill 需要 LAS/LAZ 点云文件，不自动下载（数据太大且按需购买）。

👉 完整教程见仓库根目录 PREREQUISITES.md 2.5 节。

先准备 X 文件：不传 --input 直接跑，用合成点云验证工作流。

快速试跑命令：

bash

python lidar_point_cloud_analysis.py --output-dir ./test

LiDAR Point Cloud Analysis

Reads LAS/LAZ/COPC point clouds, computes statistics, classification QA, DEM/DSM/CHM rasters, cross-sections, density maps, and quality reports.

Trigger

Use when the user wants to:

• Generate DEM/DSM/CHM from LiDAR point cloud data
• Check point cloud density, classification, and quality
• Extract cross-section profiles from point clouds
• Compute multi-temporal DEM differences for change detection
• Produce canopy height models for forestry analysis
• Assess point cloud data quality and coverage

CLI Usage

bash

# Synthetic demo mode (no input files needed)
python scripts/lidar_point_cloud_analysis.py --output-dir ./lpca-output

# With custom resolution and all products
python scripts/lidar_point_cloud_analysis.py \
  --resolution 0.5 \
  --products dem,dsm,chm,density,qa \
  --output-dir ./lpca-output

# DEM only with specific bounding box (legacy 4-floats form)
python scripts/lidar_point_cloud_analysis.py \
  --bbox-bounds 0 0 500 500 \
  --products dem \
  --resolution 1.0 \
  --output-dir ./lpca-output

Data Download (interface reserved)

This skill works on local LAS/LAZ point clouds and does not auto-download from any data source. The standard --bbox / --date-range / --aoi-file CLI flags are exposed (from the shared _geoskill_data_fetcher library) so that a future integration with the USGS 3DEP, OpenTopography, or ESA Copernicus DEM endpoints can be added without changing the CLI surface.

bash

# Reserved: --bbox is parsed to drive the synthetic-data bounds.
# (no actual download happens; this is the interface contract.)
python scripts/lidar_point_cloud_analysis.py \
  --bbox 116,39,117,40 \
  --date-range 2024-06-01,2024-06-30 \
  --output-dir ./lpca-output

Parameters

Parameter	Default	Description
--input	None	Input LAS/LAZ file path (optional, uses synthetic data if omitted)
--output-dir	./lpca-output	Output directory
--resolution	1.0	Raster resolution in meters
--ground-method	grid_min	Ground classification: grid_min, pmf
--products	dem,dsm,chm,density,qa	Comma-separated products to generate
--tile-size	100.0	Tile size in meters for QA
--bbox-bounds	None	(legacy) Bounding box for synthetic data: xmin ymin xmax ymax
--bbox	None	Bounding box W,S,E,N (shared flag, drives synthetic-data bounds)
--date-range	None	Date range START,END in ISO-8601 (reserved for future DEM endpoint)
--aoi-file	None	Optional GeoJSON AOI polygon (reserved)

Output

File	Description
dem.tif	Digital Elevation Model (bare earth)
dsm.tif	Digital Surface Model (first return)
chm.tif	Canopy Height Model (DSM - DEM)
density.tif	Point density map (points/m²)
profiles.geojson	Cross-section profiles as GeoJSON
pointcloud_qa.json	Point cloud statistics and QA results
request.json	Analysis request metadata
dataset-manifest.json	Dataset inventory
output-manifest.json	Output file inventory and raster info
qa.json	Quality assurance checks

Products

Code	Name	Description
dem	Digital Elevation Model	Ground surface (minimum Z per cell)
dsm	Digital Surface Model	Surface including objects (maximum Z per cell)
chm	Canopy Height Model	Height above ground (DSM - DEM)
density	Point Density	Points per square meter
qa	Quality Report	Statistics and quality checks

Ground Classification Methods

Method	Description
grid_min	Grid-based lowest point classification with slope threshold
pmf	Progressive Morphological Filter (requires scipy)

ASPRS Classification Codes

Code	Name
0	Created, Never Classified
1	Unassigned
2	Ground
3	Low Vegetation
4	Medium Vegetation
5	High Vegetation
6	Building
7	Low Point (Noise)
8	Model Key-point
9	Water
10-18	Reserved (Rail, Road, Wire, Bridge, etc.)

Exit Codes

Code	Meaning
0	Success
2	Argument error
3	Dependency missing
6	Data validation failure
7	Processing failure

Limitations

• LAS/LAZ reading requires laspy (falls back to synthetic data if not installed)
• Ground classification is simplified; production workflows should use PDAL
• Large files may require streaming/chunking (not yet implemented for LAS input)
• Vertical datum is assumed consistent; no datum transformation
• Multi-temporal difference requires co-registered point clouds

References

• ASPRS LAS Specification (ASPRS 1.4)
• IPCC Good Practice Guidance for Land Use
• OpenTopography Point Cloud Processing Guidelines