point-cloud-lidar

# 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 ```bash pdal info input.laz --summary # counts, bounds, CRS, classes, returns ``` 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. ## Format and scale | Format | Use | |---|---| | **LAZ** | Compressed interchange/archive — default | | **COPC** (cloud-optimized LAZ) | Streaming/HTTP range access, web viewers | | LAS | Only when a tool can't read LAZ | | Entwine/EPT | Massive multi-tile collections, indexed | Tile large collections; process per-tile with buffered edges (~2× search radius) to avoid seam artifacts in filters and surfaces; drop the buffer on write. ## PDAL pipeline pattern ```json { "pipeline": [ "input.laz", {"type": "filters.reprojection", "out_srs": "EPSG:32636"}, {"type": "filters.outlier", "method": "statistical", "mean_k": 8, "multiplier": 2.5}, {"...