LiDAR & Point Clouds

Millions of points, and the surfaces worth deriving from them.

A point cloud is the rawest spatial data in common use: no cells, no features, just returns with a class attached and a header that says where they really are. These guides cover what the returns and classes mean, how LAS, LAZ and COPC differ, reading them in Python with laspy, the scale-and-offset arithmetic that silently moves a cloud to the wrong continent, and the surfaces people actually want out of it — DTM, DSM, canopy height and building heights — including why the holes and spikes appear.

14 Steps in this path

The full path, in order

14 guides: the concepts underneath the work, then each task, then the errors that task produces. Work down, or jump to the step you need.

  1. LAS, LAZ and COPC Explained: How Point Clouds Are Stored las laz copc explained 🧭 Concept
  2. How to Read a LAS or LAZ File in Python with laspy read las laz python laspy ⚙️ How-To
  3. A LAZ File Will Not Open in Python laz file will not open 🔧 Fix
  4. laspy Returns the Wrong Coordinates: Scale, Offset and CRS laspy wrong coordinates 🔧 Fix
  5. Point Density Explained: What Resolution a Point Cloud Supports point density explained 🧭 Concept
  6. How to Filter a Point Cloud by Class and Return Number filter point cloud class return ⚙️ How-To
  7. How to Clip and Tile a Point Cloud in Python clip tile point cloud python ⚙️ How-To
  8. DSM, DTM and CHM from LiDAR: How Each Surface Is Derived lidar surfaces dsm dtm chm explained 🧭 Concept
  9. How to Create a DTM from LiDAR Ground Points in Python create dtm from lidar python ⚙️ How-To
  10. My LiDAR DTM Has Holes, Spikes or Terraces lidar dtm holes and spikes 🔧 Fix
  11. How to Create a Canopy Height Model from LiDAR canopy height model python ⚙️ How-To
  12. How to Rasterise a Point Cloud to a Grid in Python rasterise point cloud python ⚙️ How-To
  13. How to Measure Building Heights from LiDAR in Python building heights from lidar python ⚙️ How-To

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