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

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

{
  "pipeline": [
    "input.laz",
    {"type": "filters.reprojection", "out_srs": "EPSG:32636"},
    {"type": "filters.outlier", "method": "statistical",
     "mean_k": 8, "multiplier": 2.5},
    {"type": "filters.smrf", "slope": 0.15, "window": 18.0,
     "threshold": 0.5, "scalar": 1.2},
    {"type": "writers.las", "filename": "classified.laz",
     "extra_dims": "all"}
  ]
}

Run: pdal pipeline pipeline.json. Denoise BEFORE ground classification (low outliers below ground destroy SMRF/CSF); tune slope up for steep terrain, window to the largest non-ground object (big buildings need bigger windows).

Ground classification & DTM

• If vendor class 2 (ground) exists: audit it on 2-3 cross-sections (bridges, dense canopy, steep slopes) before trusting; reclassify where it fails.
• Algorithms: SMRF (PDAL default, robust), CSF (cloth simulation, good in steep forest). Parameters are terrain-dependent — show a cross-section plot as evidence, not just the parameter list.
• DTM from ground-only points; interpolation: TIN → raster (standard for DTM) or IDW for dense clouds. Output resolution ≥ ~1/√density; a 0.5 m DTM from 1 pt/m² data is invented detail.
• DSM from first returns / highest-point binning. CHM = DSM − DTM, clamp negatives to 0, and use a pit-free algorithm for forestry (naive CHMs are pocked by within-crown pits).