Howard Butler is a geospatial software entrepreneur and engineer with 22 years of experience building and stewarding open-source point cloud and GIS tooling as President of Hobu, Inc. He is a principal author and maintainer of foundational projects like PDAL, libLAS and libspatialindex and has contributed to high-profile libraries such as GDAL, GEOS, Entwine and MapServer. His work spans low-level C/C++ libraries, build and Windows support, DevOps packaging for conda-forge, and front-end UX improvements (e.g., plas.io), showing rare fluency across the full stack of geospatial infrastructure. Based in Iowa City, he combines academic training in agricultural engineering with pragmatic production engineering to solve large-scale LiDAR and spatial data problems. An often-unspoken strength is his ability to translate complex point-cloud analysis into interoperable catalog and packaging standards, evident in STAC and conda-forge integrations.
22 years of coding experience
Bachelor of Science - BS, Agricultural Systems Technology, Bachelor of Science - BS, Agricultural Systems Technology at Iowa State University
C++ implementation of R*-tree, an MVR-tree and a TPR-tree with C API
Role in this project:
Back-end Developer
Contributions:5 releases, 3 reviews, 422 commits in 14 years 9 months
Contributions summary:Howard contributed to the development and maintenance of the libspatialindex C++ library, focusing on code related to the build system and the integration of the tools library. The user added features such as the ability to build a C API and made several modifications to the build configurations, including the addition of an autogen script to simplify auto-stuff. These changes also included updates to support MSVC compiler.
C++ library and programs for reading and writing ASPRS LAS format with LiDAR data
Role in this project:
Back-end Developer
Contributions:2353 commits, 35 PRs, 64 pushes in 14 years 10 months
Contributions summary:Howard primarily contributed to the enhancement of the C++ library for reading and writing LAS format data by implementing support for LAS 1.3 version, adding new point format types, and modifying and cleaning up code within the header, and source files. The user refactored existing code and created new methods for increased efficiency. They also made changes to a few Python files.
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