Semantic Detection of Potential Wind-Borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety-Reference-Cited by-同舟云学术

Semantic Detection of Potential Wind-Borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:Computing in Civil Engineering 2021
language:
Short-container-title:

Author:

Kamari Mirsalar¹²,Ham Youngjib¹²

Affiliation:

1. Ph.D. Student, Dept. of Construction Science, Texas A&M Univ., College Station, TX.

2. Assistant Professor, Dept. of Construction Science, Texas A&M Univ., College Station, TX.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483893.111

Reference14 articles.

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2. Ghorbani Z. and Behzadan A. H. (2020). “Identification and Instance Segmentation of Oil Spills Using Deep Neural Networks.” CSEE 2020.

3. Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones.;Ham Y.;AutoCon,2019

4. Kamari M. and Ham Y. “Automated Filtering Big Visual Data from Drones for Enhanced Visual Analytics in Construction.” Proc. CRC 2018.

5. Kamari M. and Ham Y. (2020). “Analyzing Potential Risk of Wind-induced Damage in Construction Sites and Neighboring Communities using Large-scale Visual Data from Drones.” Proc. CRC 2020.

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