Joe McGlinchy is a remote sensing scientist at University of Colorado - Boulder with the Earth Lab’s Analytics Hub. He has a BS in Electrical Engineering from the University of Akron and a MS in Imaging Science from the Rochester Institute of Technology. Before joining Earth Lab, Joe was an imagery scientist in Esri’s Professional Services group where he focused on data fusion using active and passive remotely sensed data with applications in feature extraction and change detection. Joe’s current research interests are in the use and application of multi-modal, multi-scale, and multi-temporal remote sensing data, along with computer vision and deep learning techniques, as applied in the fields of urban and natural change, feature detection, disaster response, and environmental monitoring.
- McGlinchy, J; Muller, B; Johnson, B; Joseph, M; Diaz, J (2021), Fully Convolutional Neural Network for Impervious Surface Segmentation in Mixed Urban Environment. Version: 1 PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 87 (2) 117-123, issn: 0099-1112, doi: 10.14358/PERS.87.2.117
- Wasser, L., MB Joseph, J McGlinchy, J Palomino, N Korinek, C Holdgraf, T Head (2019), EarthPy: A Python package that makes it easier to explore and plot raster and vector data using open source Python tools.. Journal of Open Source Software 4(43), 1886, Version: 1 4 (43) , doi: 10.21105/joss.01886
- McGlinchy, J; Johnson, B; Muller, B; Joseph, M; Diaz, J (2019), APPLICATION OF UNET FULLY CONVOLUTIONAL NEURAL NETWORK TO IMPERVIOUS SURFACE SEGMENTATION IN URBAN ENVIRONMENT FROM HIGH RESOLUTION SATELLITE IMAGERY. Version: 1 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 3915-3918, Yokohama, JAPAN, JUL 28-AUG 02, 2019, issn: 2153-6996, isbn: 978-1-5386-9154-0