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.
- Hunter LM; Talbot C; Twine W; McGlinchy J; Kabudula CW; Ohene-Kwofie D. (Mar 2021). Working toward effective anonymization for surveillance data: innovation at South Africa's Agincourt Health and Socio-Demographic Surveillance Site. Population and Environment , 445-476. 10.1007/s11111-020-00372-4
- McGlinchy J; Muller B; Johnson B; Joseph M; Diaz J. (Feb 2021). Fully Convolutional Neural Network for Impervious Surface Segmentation in Mixed Urban Environment. Photogrammetric Engineering and Remote Sensing , 87(2), 117-123. 10.14358/PERS.87.2.117
- Balch JK; St. Denis LA; Mahood AL; Mietkiewicz NP; Williams TM; McGlinchy J; Cook MC. (Nov 2020). FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001-2019). Remote Sensing , 12(21). 10.3390/rs12213498
- Hunter L; Talbot C; Twine W; McGlinchy J; Kabudula C. (Jan 2020). Working toward effective anonymization for population-environment-health research: Innovation at South Africa's Agincourt Health and Demographic Surveillance Site. Population and Environment , Being revised for resubmission. 10.1007/s11111-020-00372-4