Entwine

Exercise

This exercise uses PDAL to fetch data from an Entwine index stored in an Amazon Web Services object store (bucket). Entwine is a point cloud indexing strategy, which rearranges points into a lossless octree structure known as EPT, for Entwine Point Tiles. The structure is described here: https://entwine.io/entwine-point-tile.html.

EPT indexes can be used for visualization as well as analysis and data manipulation at any scale.

Examples of Entwine usage can be found from very fine photogrammetric surveys to continental scale lidar management.

US Geological Survey (USGS) example data is here: https://usgs.entwine.io/

We will use a sample data set from Dublin, Ireland https://viewer.copc.io/?r=https://na-c.entwine.io/dublin/ept.json

1. View the entwine.json file in your editor.

   {
       "pipeline": [
           {
               "type": "readers.ept",
               "filename":"https://na-c.entwine.io/dublin/ept.json",
               "resolution": 5
           },
           {
               "type": "writers.las",
               "compression": "true",
               "minor_version": "2",
               "dataformat_id": "0",
               "filename":"dublin.laz"
           }
       ]
   }
   

   Note

   If you use the Developer Console when visiting http://speck.ly or http://potree.entwine.io, you can see the browser making requests against the EPT resource at http://na-c.entwine.io/dublin/ept.json

2. Issue the following command in your Conda Shell.

   $ pdal pipeline ./exercises/translation/entwine.json -v 7
   (PDAL Debug) Debugging...
   (pdal pipeline readers.ept Debug) Root resolution: 21.3828
   Query resolution:  5
   Actual resolution: 2.67285
   Depth end: 4
   Query bounds: ()
   Threads: 15
   (pdal pipeline Debug) Executing pipeline in stream mode.
   (pdal pipeline writers.las Debug) Wrote 8034506 points to the LAS file
   

1. Verify that the data look ok:

   $ pdal info dublin.laz | jq .stats.bbox.native.bbox
   {
       "maxx": -694128.96,
       "maxy": 7049938.84,
       "maxz": 385.37,
       "minx": -699477.88,
       "miny": 7044490.98,
       "minz": -144.24
   }
   $ pdal info dublin.laz -p 0
   {
       "file_size": 56441298,
       "filename": "dublin.laz",
       "now": "2022-05-16T09:57:45-0700",
       "pdal_version": "2.3.0 (git-version: Release)",
       "points":
       {
           "point":
           {
               "Classification": 4,
               "EdgeOfFlightLine": 0,
               "Intensity": 265,
               "NumberOfReturns": 2,
               "PointId": 0,
               "PointSourceId": 16,
               "ReturnNumber": 1,
               "ScanAngleRank": 2,
               "ScanDirectionFlag": 1,
               "UserData": 0,
               "X": -695945.82,
               "Y": 7046284.13,
               "Z": 122.01
           }
       },
       "reader": "readers.las"
   }
   

2. Visualize the data in http://plas.io

   

Notes

1. readers.ept contains more detailed documentation about how to use PDAL’s EPT reader .