SilviMetric

SilviMetric#

SilviMetric is an open source library and set of utilities from Hobu, Inc. that are useful for summarizing point cloud data into raster and raster-like products.

Find out more about SilviMetric by visiting About. A slide deck about SilviMetric is available on Google Slides, and tutorials can be found on the Tutorial page as well as in Google Colab.

QuickStart#

SilviMetric depends upon Conda for packaging support. You must first install all of SilviMetric’s dependencies using Conda:

This tutorial shows you how to initialize, shatter, and extract data in SilviMetric using the command line.

We are going to use the Autzen Stadium as our test example.

Note

The Autzen Stadium has units in feet, and this can sometimes be a source of confusion for tile settings and such.

Installation#

The best way to install SilviMetric for usage is through Conda.

conda install -c conda-forge silvimetric

Initialization#

Initialize a SilviMetric database. To initialize a SilviMetric database, we need a bounds and a coordinate reference system.

  1. We first need to determine a bounds for our database. In our case, we are going to use PDAL and jq to grab our bounds

    pc_url="https://s3.amazonaws.com/hobu-lidar/autzen-classified.copc.laz"
bbox=$(pdal info $pc_url --readers.copc.resolution=800 | jq -c '.stats.bbox.native.bbox')
echo $bbox

    Our boundary is emitted in expanded form.

    {"maxx":639003.73,"maxy":853536.21,"maxz":615.26,"minx":635579.2,"miny":848884.83,"minz":406.46}

    Note

    You can express bounds in two additional formats for SilviMetric:

    • [635579.2, 848884.83, 639003.73, 853536.21] – [minx, miny, maxx, maxy]

    • ([635579.2,848884.83],[639003.73,853536.2]) – ([minx, maxx], [miny, maxy])

    Note

    You can install jq by issuing conda install -c conda-forge jq -y in your environment if you are on Linux or Mac. On Windows, you will need to download jq from the website and put it in your path. https://jqlang.github.io/jq/download/

  2. We need a coordinate reference system for the database. We will grab it from

    the PDAL metadata just like we did for the bounds.

    epsg=$(pdal info --metadata $pc_url | jq -c '.metadata.srs.json.components[0].id.code')
echo $epsg

    Our EPSG code is in the pdal info --metadata output, and after extracted by jq, we can use it.

    2992

    Note

    Both a bounds and CRS must be set to initialize a database. We can set them to whatever we want, but any data we are inserting into the database must match the coordinate system of the SilviMetric database.

  3. With bounds and CRS in hand, we can now initialize the database. We’ll add in a resolution we want (30 feet for ~10 meter), as well as a selection of what metrics we want computed. You can select from a group of pre-defined metrics, select individual metrics, or create your in a python file and pass a path to that file. For now we’ll stick to the metrics defined in FUSION.

    db_name="autzen-smdb.tdb"
crs="EPSG:$epsg"
res=30
silvimetric -d "${db_name}" initialize --bounds "${bbox}" --crs "${crs}" --resolution="${res}" -m grid_metrics

Scan#

The scan command will tell us information about the pointcloud with respect to the database we already created, including a best guess at the correct number of cells per tile, or tile size.

silvimetric -d ${db_name} scan ${pc_url}

We should see output like the output below, recommending we use a tile size of 185.

2024-12-06 09:08:07,124 - silvimetric - INFO - info:155 - {
    "pc_info": {
        "storage_bounds": [
            635550.0,
            848880.0,
            639030.0,
            853560.0
        ],
        "data_bounds": [
            635550.0,
            848880.0,
            639030.0,
            853560.0
        ],
        "count": [
            10653336
        ]
    },
    "tile_info": {
        "num_cells": 18096,
        "num_tiles": 220,
        "mean": 82.25454545454545,
        "std_dev": 94.76295175221901,
        "recommended": 177
    }
}

Shatter#

We can now insert data into the SMDB.

If we run this command without the argument --tilesize, Silvimetric will determine a tile size for you. The method will be the same as the Scan method, but will filter out the tiles that have no data in them.

silvimetric -d ${db_name} \
   shatter \
   --date 2008-12-01 \
   ${pc_url}

If we grab the tile size from the scan that we ran earlier, we can skip the filtering step alltogether.

silvimetric -d ${db_name} \
   shatter \
   --tilesize 177 \
   --date 2008-12-01 \
   ${pc_url}

2024-12-06 10:36:59,605 - silvimetric - INFO - info:155 - Beginning shatter process...
[########################################] | 100% Completed | 62.31 s
2024-12-06 10:38:05,076 - silvimetric - INFO - info:155 - Consolidated time slot 1.

Info#

We can query past shatter processes and the schema for the database with the Info call.

silvimetric -d ${db_name} info --history

This will print out a JSON object containing information about the current state of the database. We can find the name key here, which is necessary for Delete, Restart, and Resume. For the following commands we will have copied the value of the name key in the variable uuid.

2024-12-06 17:34:48,965 - silvimetric - INFO - info:155 - {
  "history": [
    {
      "filename": "https://s3.amazonaws.com/hobu-lidar/autzen-classified.copc.laz",
      "name": "34f0d5de-00e6-4800-aac6-0ffb51398396",
      "time_slot": 1,
      "bounds": [
        635550.0,
        848880.0,
        639030.0,
        853560.0
      ],
      "date": [
        "2008-12-01T00:00:00Z"
      ]
    }
  ]
}

Extract#

After data is inserted, we can extract it into different rasters. When we created the database we gave it a list of Attributes and Metrics. When we ran Shatter, we filled in the values for those in each cell. If we have a database with the Attributes Intensity and Z, in combination with the Metrics min and max, each cell will contain values for min_Intensity, max_Intensity, min_Z, and max_Z. This is also the list of available rasters we can extract.

silvimetric -d ${db_name} extract -o output-directory

2024-12-06 10:42:21,767 - silvimetric - INFO - info:155 - Collecting database information...
2024-12-06 10:42:23,399 - silvimetric - INFO - info:155 - Writing rasters to output-directory

Delete#

We can also remove a shatter process by using the delete command. This will remove all data associated with that shatter process from the database.

silvimetric -d autzen-smdb.tdb delete --id $uuid

Restart#

If you would like to rerun a Shatter process, whether or not it was previously finished, you can use the restart command. This will call the delete method and use the config from that to re-run the shatter process.

silvimetric -d autzen-smdb.tdb restart --id $uuid

Resume#

If a Shatter process is cancelled partway through, we can pick up where we left off with the Resume command.

silvimetric -d autzen-smdb.tdb resume --id $uuid