Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve th...
Main Authors: | Min Ji, Lanfa Liu, Rongchun Zhang, Manfred F. Buchroithner |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/2/602 |
Similar Items
-
Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake
by: Min Ji, et al.
Published: (2018-10-01) -
Research Progress on Vision–Language Multimodal Pretraining Model Technology
by: Huansha Wang, et al.
Published: (2022-10-01) -
Pretrained Configuration of Power-Quality Grayscale-Image Dataset for Sensor Improvement in Smart-Grid Transmission
by: Yeong-Chin Chen, et al.
Published: (2022-09-01) -
Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images
by: Bahareh Kalantar, et al.
Published: (2020-10-01) -
Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection
by: Falah Amer Abdulazeez, et al.
Published: (2024-03-01)