Joe Mcglinchy
Principal Remote Sensing Scientist at AiDASH
Arvada, Colorado, United States
Summary
👤
Senior🎓
Top SchoolJoe Mcglinchy is a Principal Remote Sensing Scientist with 11 years of experience translating multi- and hyperspectral imagery, lidar, and radar into operational geospatial products. He has led end-to-end efforts to fuse diverse satellite sources (VIIRS, MODIS, ECOSTRESS, Landsat, Sentinel-2) into daily global Land Surface Temperature and Surface Reflectance products, combining algorithm development, cloud orchestration (AWS), validation, and georegistration. With an MS in Imaging Science and a BSEE, he blends physics-based retrieval expertise, atmospheric correction (Py6S), and statistical/machine learning techniques for classification, unmixing, and target detection. Joe contributes to open-source geospatial tooling—enhancing the well-used earthpy library—and has hands-on experience with UAV sensor integration, field campaigns, and lidar-derived 2D/3D reconstruction. He routinely spans roles from technical lead and developer to mentor and QA, demonstrating a rare mix of research rigor and production-focused engineering.
11 years of coding experience
14 years of employment as a software developer
Masters of Science Imaging Science (Remote Sensing), Masters of Science Imaging Science (Remote Sensing) at Rochester Institute of TechnologyBSEE Electrical Engineering, BSEE Electrical Engineering at The University of Akron
Stackoverflow
1reputation
352reached
1answer
1question
Github Skills (12)
spatial-data-analysis10
rasterio10
geospatial10
pytest10
spatial-analysis10
python10
raster10
geospatial-analysis10
numpy10
testing10
google-earth-engine6
ed255194
Programming languages (8)
Github contributions (5)
earthlab/earthpy
Nov 2018 - Jun 2019
A package built to support working with spatial data using open source python
Role in this project:
Data Scientist
Data ScientistContributions:31 commits, 18 PRs, 1 push in 6 months
Contributions summary:Joe primarily contributed to enhancing the `earthpy` library for geospatial data analysis. Their work focused on implementing and testing new functionalities related to raster data processing, including stacking, masking, and handling NoData values. They also added documentation, test cases and addressed code quality, and bug fixes. Additionally, they contributed to the EPSG coordinate system handling within the library.
pythonspatial-datarastervector
earthlab/NSS-Satellite-Imagery-with-NEON-AOP-Data
Oct 2019 - Apr 2021
Code and instructions for "Satellite Imagery with NEON AOP Data" Working Group at NEON Science Summit
Contributions:27 commits, 1 PR, 22 pushes in 1 year 6 months
aop
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