Andrew Sanchez
Director of Engineering at Ecological Restoration Inst. and School of Forestry, Northern Arizona Univ.
Flagstaff, Arizona, United States
Summary
👤
Senior🎓
Top SchoolAndrew Sanchez is a Director of Engineering with 11 years of experience who blends civil engineering education and hands-on software development to solve spatial and ecological problems. Based in Flagstaff, he pairs academic work at Northern Arizona University with practical skills in LiDAR algorithm implementation—most notably contributing the lassnags snag-classification routine and an efficient quadtree search to the widely used lidR repository. He is completing a Civil Engineering degree while preparing for the FE exam and FAA Part 107 certification, signaling a deliberate bridge between surveying aviation and computational geospatial work. Andrew focuses on learning from diverse perspectives to synthesize better approaches to complex problems and consistently refines algorithms and documentation for reproducible, production-ready tools. His background reflects a rare mix of domain knowledge in forestry/ecological restoration and deep technical fluency in back-end algorithm design for airborne LiDAR.
11 years of coding experience
Bachelor's degree, Civil Engineering, Bachelor's degree, Civil Engineering at Northern Arizona UniversityPikes Peak Community College
Github Skills (6)
rcpp10
algorithms10
sens10
lidar10
point-cloud10
r10
Programming languages (3)
Github contributions (5)
r-lidar/lidR
Oct 2017 - Nov 2018
Airborne LiDAR data manipulation and visualisation for forestry application
Role in this project:
Back-end Developer & Algorithm Implementer
Back-end Developer & Algorithm ImplementerContributions:15 commits, 2 PRs, 102 comments in 1 year 1 month
Contributions summary:Andrew's contributions center on implementing and refining the `lassnags` function, which appears to be a key algorithm for snag classification within the `lidr` R package. They added the `lassnags` function, implemented the "wing2015" algorithm, and incorporated quadtree search methods for efficiency. Furthermore, the user fixed typos, refined the example code within the package documentation and adjusted the code to reflect the needs of pull requests. These changes point towards the user's expertise in developing and improving algorithms for the processing of LiDAR data.
manipulationairbornelazvisualisationals
bi0m3trics/spanner
Mar 2020 - Mar 2022
Utilities to support landscape-, forest-, and tree-related data collection, manipulation, analysis, modelling, and visualization.
Contributions:2 releases, 65 commits, 4 PRs in 2 years
visualizationmanipulationdata-analysisvisual-analyticsdata-viz
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