Summarizing and filtering point cloud data into useful information for modeling is challenging. In forestry applications in particular, the FUSION software toolkit is often used to extract information in preparation for modeling. FUSION, however, has a few missing features that make it

Working with Bob McGaughey and the USFS GTAC team, Kyle Mann and Howard Butler from Hobu, Inc., developed the initial prototype of SilviMetric to implement an alternative approach to computing the “GridMetrics” component of typical FUSION processing pipelines.

SilviMetric does this by breaking apart the computation of metrics into three distinct steps – info, shatter, and extract. SilviMetric takes an infrastructure computing approach to the challenge by applying emerging open source technologies that speak cloud, are nimble with data formats, and compute in a more friendly language - Python.


SilviMetric stands on the shoulders of giants to provide an integrated solution to computing rasterized point cloud metrics. These technologies include:

  • PDAL reads point cloud content and allows users to filter or process data as it ingested.

  • Dask processes tasks for :ref`shatter` and extract in a highly parallel, cloud-friendly distributed computing environment.

  • TileDB stores metrics in cloud object stores such as S3 in addition to typical filesystems.

  • Python computes metrics and provides a diverse and convenient computing capability for users to easily add and extract their own metrics to the database.