Post-Hurricanes Roadway Closure Detection using Satellite Imagery and Semi-Supervised Ensemble Learning

After hurricanes, roadway damage assessment is critical to emergency responders and city authorities. In this paper, the authors propose an automated semi-supervised approach to identify tree debris along roadways to improve the efficiency of damage assessment and allow faster debris cleaning operations. The solution uses two high-resolution satellite images taken before and after a hurricane. The proposed methodology is an ensemble learning machine using an unsupervised autoencoder-based feature extractor, an unsupervised vegetation coverage estimator, and a weakly-supervised tree segmentation. The authors show that such a combination can increase precision and accuracy. The authors' solution has been tested with a case study on Hurricane Michael, which hit Tallahassee, the capital of Florida, in October 2018.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; Photos; References;
  • Pagination: 18p

Subject/Index Terms

Filing Info

  • Accession Number: 01763675
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-00892
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:08AM