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Building an annotated damage image database is the first step to support AI-assisted hurricane impact analysis. Up to now, annotated datasets for model training are insufficient at a local level despite abundant raw data that have been collected for decades. This paper provides a systematic approach for establishing an annotated hurricane-damaged building image database to support AI-assisted damage assessment and analysis. Optimal rectilinear images were generated from panoramic images collected from Hurricane Harvey, Texas 2017. Then, deep learning models, including Amazon Web Service (AWS) Rekognition and Mask R-CNN (Region Based Convolutional Neural Networks), were retrained on the data to develop a pipeline for building detection and structural component extraction. A web-based dashboard was developed for building data management and processed image visualization along with detected structural components and their damage ratings. The proposed AI-assisted labeling tool and trained models can intelligently and rapidly assist potential users such as hazard researchers, practitioners, and government agencies on natural disaster damage management.
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A New Data-Driven Approach to Measuring Hurricane Risk
Improving disaster operations requires understanding and managing risk. This paper proposes a new data-driven approach for measuring the risk associated with a natural hazard, in support of developing more effective approaches for managing disaster operations. The paper focuses, in particular, on the issue of defining the inherent severity of a hazard event, independent of its impacts on human society, and concentrates on hurricanes as a specific type of natural hazard. After proposing a preliminary severity measure in the context of a hurricane, the paper discusses the issues associated with collecting empirical data to support its implementation. The approach is then illustrated by comparing the relative risk associated with two different locations in the state of North Carolina subject to the impacts of Hurricane Florence in 2018.
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- Award ID(s):
- 1735139
- PAR ID:
- 10110723
- Date Published:
- Journal Name:
- Proceedings of the 16th ISCRAM Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
