@inproceedings{16,
  author = {Hao Ou and Sun Ro and Jie Gong and Zhigang Zhu},
  title = {Building an Annotated Damage Image Database to Support AI-Assisted Hurricane Impact Analysis},
  abstract = {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.},
  year = {2021},
  journal = {2021 IEEE International Conference on Imaging Systems and Techniques (IST)},
  chapter = {1},
  pages = {6},
  month = {08},
  url = {https://par.nsf.gov/biblio/10346690},
  doi = {10.1109/IST50367.2021.9651432},
}