Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN...

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Bibliographic Details
Main Authors: Saman Ghaffarian, Norman Kerle, Edoardo Pasolli, Jamal Jokar Arsanjani
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Remote Sensing
Subjects:
post-disaster
building database update
damage assessment
recovery assessment
openstreetmap
deep learning
convolutional neural network
multi-temporal satellite imagery
u-net
super typhoon haiyan
Online Access:https://www.mdpi.com/2072-4292/11/20/2427

Internet

https://www.mdpi.com/2072-4292/11/20/2427

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