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).

Structure metrics

• Forestry: height percentiles (p95 ≈ canopy height), canopy cover

(first returns > 2 m / all first returns), density metrics per grid cell or plot; normalize heights against the DTM first (filters.hag_dem or filters.hag_nn). Individual tree detection: local maxima on pit-free CHM + watershed segmentation — validate count against field plots or manual photo-interpretation samples.

• Buildings: class 6 or planar-patch extraction; building height =

p90(roof points HAG); footprint fusion with cadastre/OSM polygons via zonal statistics on HAG.

• Downstream terrain analysis (slope, watersheds) → terrain-hydrology;

DL on point clouds or derived rasters → geo-deep-learning.

SfM/photogrammetric clouds — not LiDAR

Drone photogrammetry clouds have no returns, no canopy penetration (ground under vegetation is guessed), correlated noise, and possible doming from poor camera calibration. A "DTM" from SfM over forest is a canopy model. State the sensor type in every deliverable; use LiDAR-specific claims (penetration, return metrics) only for LiDAR.

Verification protocol

1. Cross-sections (2-3, including a building edge and a vegetated slope):

ground class hugs terrain, DSM caps surface.

1. DTM minus known control points / national DEM: report RMSE and check

for a constant offset = vertical datum mismatch.

1. Hillshade the DTM — classification artifacts (pits, pimples,

flight-line stripes) are instantly visible.

1. Report: density, CRS + vertical datum, classifier + parameters, output

resolution rationale.

Pitfalls checklist

• Ellipsoidal heights delivered as orthometric (whole product offset by

the geoid).

• DTM resolution finer than point density supports.
• Vendor ground class trusted under dense canopy.
• CHM with negative values or crown pits (no pit-free processing).
• Per-tile processing without buffers → seam lines in derivatives.
• Outlier filter run AFTER ground classification.
• SfM cloud treated as canopy-penetrating LiDAR.

Execution contract

• Workflow: inspect header, CRS, vertical datum, density, classes, and returns; tile with buffers; filter noise; classify; derive products; mosaic; validate in 3D and cross-section.
• Decision rules: use point-cloud workflows when return-level 3D evidence matters, terrain workflows after a validated DEM exists, and separate assumptions for LiDAR versus SfM clouds.
• Verification protocol: reconcile point counts and classes, inspect buffered seams and cross-sections, compare elevations to control, hillshade derived terrain, and report density-supported resolution.
• Failure modes: stop for unknown vertical datum, insufficient density, corrupt classification, tile seams, unbounded outliers, or product resolution finer than sampling supports.
• Deliverables: validated cloud or derived DTM/DSM/CHM, pipeline parameters, CRS and vertical datum, density and class report, QA graphics, accuracy metrics, and limitations.
• Source freshness: consult [the authoritative source registry](references/authoritative-sources.md) before applying format, quality, or processing rules and record the checked date.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: muend
• Source: muend/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.