Assessment of convolutional neural network architectures for earthquake-induced building damage detection based on pre- and post-event orthophoto images
In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are consid...
Main Authors: | Kalantar, Bahareh, Ueda, Naonori, Al-Najjar, Husam Abdulrasool H., Abdul Halin, Alfian |
---|---|
Format: | Article |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2020
|
Online Access: | http://psasir.upm.edu.my/id/eprint/87948/1/ABSTRACT.pdf |
Similar Items
-
BDD-Net: an end-to-end multiscale residual CNN for earthquake-induced building damage detection
by: Seydi, Seyd Teymoor, et al.
Published: (2022) -
Conditioning factors determination for landslide susceptibility mapping using support vector machine learning
by: Kalantar, Bahareh, et al.
Published: (2019) -
Land cover classification from fused DSM and UAV images using convolutional neural networks
by: Al-Najjar, Husam A. H., et al.
Published: (2019) -
MSBDA-Net: Multi-scale Siamese Building Damage Assessment Network
by: Zaryabi, Erfan Hasanpour, et al.
Published: (2022) -
Optimized conditioning factors using machine learning techniques for groundwater potential mapping
by: Kalantar, Bahareh, et al.
Published: (2019)