post-disaster

Abstract

Post-disaster building damage assessment is an important application of remote sensing. The increasing resolution of remote sensing imaging systems and state-of-the-art deep learning networks has facilitated damage assessment. However, most existing methods in the literature concentrate on damage/non-damage classification only in specific disaster types/areas using pre- and post-event images. Furthermore, site visits are inevitable to assess the level of damage to structures. Therefore, the main objective of this study was to utilize deep transfer learning over a pre-trained network and extend it to a damage assessment framework. The network is fine-tuned to identify four different damage levels: non-damage, minor damage, major damage, and collapsed, using only post-event images taken from different disaster types/areas. To evaluate the proposed framework, we carried out three experiments on Hurricane Irma in Sint Maarten, Hurricane Dorian in Abaco Islands, and Woolsey Fire using post-event orthophotos derived from unmanned aerial vehicle (UAV) images. The results of over 80% overall accuracy confirm that with a structured learning scenario, it is possible to use transfer learning on very high-resolution remote sensing images to classify the level of structural damage.

Full document can be requested here: https://cdnsciencepub.com/doi/10.1139/geomat-2021-0014

Abstract.

With the concept of "build back better", the United Nations emphasizes the importance of the recovery phase following a natural hazard as an opportunity to implement vulnerability reduction measures. This work here focuses on the ongoing recovery of the French part of island of St. Martin following hurricanes Irma in September 2017. The recovery of this semi-autonomous territory is a major challenge for the local authorities and for the French State. The current state of post-disaster recovery shows the difficulties of reconciling the two imperatives of "rebuild faster" and "rebuild better", in a context of social, political and media pressure. Therefore, what conditions would be necessary to take advantage of this key moment and make the small island more resilient to a new event? What do we learn from this experience for the management of the recovery?