Rapid identification of damaged buildings using incremental learning with transferred data from historical natural disaster cases

Abstract

The accurate extraction of building damage after destructive natural disasters is critical for disaster rescue and assessment. To achieve a rapid disaster response, training a model from scratch using enough ground-truth data collected in situ is not feasible. Often, in disaster situations, it is ineffective to directly apply an existing model due to the vast diversity among buildings worldwide, the limited number of label samples for training, and the different sources of remote sensing images between the pre- and post-disaster. To solve this problem, we present an incremental learning framework for the rapid identification of collapsed buildings triggered by sudden natural disasters. Specifically, end-to-end gradient boosting networks are improved into an incremental learning framework for an emergency response, where the historical natural disaster data are transferred into the same style of images that were captured shortly after a disaster event by using cycle-consistent generative adversarial networks. The proposed method is tested on two cases, i.e., the Haiti earthquake in January 2010 and the Nepal earthquake in April 2015, achieving Kappa accuracies of 0.70 and 0.68, respectively. The optimization of building damage extraction can be completed within 8 h after the disaster using the transferred data. The experimental results show that the proposed method is an effective way to evaluate the building damage triggered by natural disasters with different source remote sensing images. The code of this work and the data of the test cases are available at https://github.com/gjy-Ari/Incre-Trans.