Uncertainty‐aware convolutional neural network for explainable artificial intelligence‐assisted disaster damage assessment-Reference-Cited by-同舟云学术

Uncertainty‐aware convolutional neural network for explainable artificial intelligence‐assisted disaster damage assessment

Published:2022-06-06 Issue:10 Volume:29 Page:
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Structural Contr & Hlth

Author:

Cheng Chih‐Shen¹,Behzadan Amir H.²,Noshadravan Arash¹^ORCID

Affiliation:

1. Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA

2. Department of Construction Science Texas A&M University College Station TX USA

Publisher

Hindawi Limited

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.3019

Reference71 articles.

1. FEMA.Fema preliminary damage assessment guide.Report FEMA;2020.https://www.fema.gov/

2. MarshallJ SmithD LydaD et al.Steer ‐ hurricane dorian: Field assessment structural team (fast‐1) early access reconnaissance report (earr). DesignSafe‐CI;2019.

3. Big data and disaster management: a systematic review and agenda for future research

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

5. AdamsS FriedlandC LevitanM.Unmanned aerial vehicle data acquisition for damage assessment in hurricane events. In: Proceedings of the 8th international workshop on remote sensing for disaster management tokyo japan Vol. 30;2010.

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