point-cloud-lidar
LiDAR and point cloud processing: PDAL pipelines, LAS/LAZ/COPC handling, ground classification, DTM/DSM/CHM generation, canopy and building metrics, and photogrammetric (SfM) point clouds. Use when the primary input is LAS, LAZ, COPC, LiDAR, or an unstructured 3D point cloud. This skill owns vertical datum agreement, co-registration and the vertical-accuracy budget when two acquisitions are differenced with comparability not yet established; once datum, geoid and accuracy are documented, a subsidence or elevation-change question is change-detection's. Route a derived DEM, DTM, DSM or CHM to terrain-hydrology unless point-level classification, comparability, or metrics remain in scope.
Install
With your agent
Install OMGSkills skill "muend/geoai-skills:skills/point-cloud-lidar" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/muend/geoai-skills /tmp/geoai-skills && ln -s /tmp/geoai-skills/skills/point-cloud-lidar ~/.claude/skills/point-cloud-lidarFrom README
Point Clouds & LiDAR Purpose: from raw returns to defensible elevation and structure products. The recurring failure modes: trusting vendor classification blindly, mixing return types in surfaces (DSM from last returns, DTM with vegetation), and ignoring point density when choosing output resolution. First contact with any cloud Report before touching anything: point count, density (pts/m² — decides achievable raster resolution), CRS (horizontal AND vertical datum — ellipsoidal vs orthometric heights differ by the geoid undulation, tens of meters in places), classification present?, return numbers present?, flight-line overlap artifacts. A cloud without CRS metadata: resolve from the provider, never assume.
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