Final Project

The final project brings together a number of PDAL processing workflow operations into a single effort It builds upon the exercises to enable you to use the capabilities of PDAL in a coherent processing strategy, and it will give you ideas about how to orchestrate PDAL in the context of larger data processing scenarios.

Given the following pipeline for fetching the data, complete the rest of the tasks:

{
    "pipeline": [
        {
            "type": "readers.ept",
            "filename":"https://s3-us-west-2.amazonaws.com/usgs-lidar-public/MA_CentralEastern_1_2021/ept.json",
            "bounds":"([-7911859.4, -7911077.0],[5213787.7, 5214543.3],[-40, 400])"
        },
        {
            "type": "filters.expression",
            "expression": "Classification < 20"
        },
        {
            "type": "writers.las",
            "compression": "true",
            "minor_version": "4",
            "dataformat_id": "0",
            "filename":"public-garden.laz"
        },
        {
            "type": "writers.copc",
            "filename": "public-garden.copc.laz",
            "forward": "all"
        }
    ]
}

• Read data from an EPT resource using readers.ept (See Entwine)

Note

The particular data we are pulling has some high classification values due to how it was processed. These aren’t useful to us, and we can use filters.expression in the pipeline to only write points with a classification value under 20.

• Thin it to 1.0 meter spacing using filters.sample (See Thinning)

• Filter out noise using filters.outlier (See Removing noise)

• Classify ground points using filters.smrf (See Identifying ground)

• Compute height above ground using filters.hag_nn

• Generate a digital terrain model (DTM) using writers.gdal (See Generating a DTM)

• Find the average vegetative height model using writers.gdal

Note

You should review specific exercises for specifics on how to achieve each task.