On the Generalization Ability of a Global Model for Rapid Building Mapping from Heterogeneous Satellite Images of Multiple Natural Disaster Scenarios

Yijiang Hu,Hong Tang

doi:10.3390/rs13050984

Abstract

Post-classification comparison using pre- and post-event remote-sensing images is a common way to quickly assess the impacts of a natural disaster on buildings. Both the effectiveness and efficiency of post-classification comparison heavily depend on the classifier’s precision and generalization abilities. In practice, practitioners used to train a novel image classifier for an unexpected disaster from scratch in order to evaluate building damage. Recently, it has become feasible to train a deep learning model to recognize buildings from very high-resolution images from all over the world. In this paper, we first evaluate the generalization ability of a global model trained on aerial images using post-disaster satellite images. Then, we systemically analyse three kinds of method to promote its generalization ability for post-disaster satellite images, i.e., fine-tune the model using very few training samples randomly selected from each disaster, transfer the style of postdisaster satellite images using the CycleGAN, and perform feature transformation using domain adversarial training. The xBD satellite images used in our experiment consist of 14 different events from six kinds of frequently occurring disaster types around the world, i.e., hurricanes, tornadoes, earthquakes, tsunamis, floods and wildfires. The experimental results show that the three methods can significantly promote the accuracy of the global model in terms of building mapping, and it is promising to conduct post-classification comparison using an existing global model coupled with an advanced transfer-learning method to quickly extract the damage information of buildings.

Highlights

The frequent occurrence of various kinds of disaster around the world has caused unprecedented heavy losses to human life and property
We systemically analyse three types of methods, i.e., fine-tune the model using very few training samples randomly selected from each disaster, transfer the style of postdisaster satellite images using CycleGAN [15], and perform feature transformation using domain adversarial training [16], to promote the model’s generalization ability on postdisaster satellite images
We systemically analyse three kinds of methods, i.e., fine-tuning the model using very few training samples randomly selected from each disaster, transferring the style of post-disaster satellite images using the CycleGAN, and feature transformation using domain adversarial training, to promote the generalization ability using post-disaster satellite images

Summary

Introduction

The frequent occurrence of various kinds of disaster around the world has caused unprecedented heavy losses to human life and property. As one of the main disaster-bearing bodies, buildings might collapse and/or be damaged due to earthquakes, typhoons and other major natural disasters. Most human casualties from disasters occur in collapsed buildings. As an important indicator of the severity of a disaster, damage assessment of buildings plays an important role in disaster relief and government decision making. With increasing numbers of remote-sensing images and higher spatial resolutions, the issue of how to use remote-sensing technology for damage assessment has become the focus of researchers. A common method for collapsed building detection after disasters is to extract building damage information from high-resolution remote-sensing images

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Conclusion