A Deep Learning Approach to Train a Bi-temporal Change Detector For Optical Satellite Imagery
Date
Mar 6, 2020
Event
Performance Review Meeting
Location
MDA, Richmond, BC, Canada
We discuss a deep-learning approach to develop a change detector for optical satellite imagery. The detector forms part of the persistent multi-sensor land surveillance and change monitoring project funded by the Defence Innovation Research Program (DIRP) and MDA.
Luz Angélica Caudillo Mata
Computational Scientist & Community Builder
Passionate about driving innovation and developing cutting-edge technology at the intersection of GeoAI and computational geosciences, while fostering collaborative communities.