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« Webinaire de la Section régionale de Waterloo de la CRSS-SCT »
Disaster Damage Assessment Using Deep Transfer Learning Dr. Ghasem Abdi, Post-Doctoral Fellow, University of New Brunswick, Canada Thursday, April 29, 2021
1:00 PM | (UTC-04:00) Eastern Time (US & Canada)
With the recent tremendous improvements in the spatial, spectral, and temporal resolutions of remote sensing imaging systems, there has been a dramatic increase in the applications of remote sensing images. Amongst different applications of very high-resolution remote sensing images, damage assessment for rapid emergency response is one of the most challenging ones. Recently, deep learning frameworks have enhanced the performance of damage assessment by automatic extraction of strong deep features. However, most of the existing studies in this area focus on using nadir satellite images or orthophotos which limits the available data sources. This limitation decreases the temporal resolution of the practical images, which is a serious issue considering the emergency nature of damage assessment applications. The objective of this webinar is to present a multi-modal integrated structure to combine orthophoto and off-nadir images for damage assessment.