基于特征融合和任务关系编码的基于深度学习的Google街景图像洪水风险评估建筑物属性估计

IF 4.7 2区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing in Civil Engineering Pub Date : 2022-11-01 DOI:10.1061/(asce)cp.1943-5487.0001025

Fu-Chen Chen, Abhishek Subedi, M. Jahanshahi, David R. Johnson, E. Delp

引用次数: 4

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Learning-Based Building Attribute Estimation from Google Street View Images for Flood Risk Assessment Using Feature Fusion and Task Relation Encoding

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来源期刊

Journal of Computing in Civil Engineering 工程技术-工程：土木

CiteScore

11.90

自引率

7.20%

发文量

审稿时长

6 months

期刊介绍： The Journal of Computing in Civil Engineering serves as a resource to researchers, practitioners, and students on advances and innovative ideas in computing as applicable to the engineering profession. Many such ideas emerge from recent developments in computer science, information science, computer engineering, knowledge engineering, and other technical fields. Some examples are innovations in artificial intelligence, parallel processing, distributed computing, graphics and imaging, and information technology. The journal publishes research, implementation, and applications in cross-disciplinary areas including software, such as new programming languages, database-management systems, computer-aided design systems, and expert systems; hardware for robotics, bar coding, remote sensing, data mining, and knowledge acquisition; and strategic issues such as the management of computing resources, implementation strategies, and organizational impacts.