Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks

TL;DR

This paper introduces a novel superpixel-based deep learning approach for precise building damage detection from high-resolution post-earthquake imagery, improving accuracy over existing methods.

Contribution

It combines a modified segmentation technique with a pre-trained DNN to enhance damage detection accuracy in VHR imagery after earthquakes.

Findings

Outperforms alternative classifiers in damage detection accuracy

Effective segmentation and merging improve building damage localization

Demonstrated on WorldView-2 imagery from Nepal 2015 earthquake

Abstract

Building damage detection after natural disasters like earthquakes is crucial for initiating effective emergency response actions. Remotely sensed very high spatial resolution (VHR) imagery can provide vital information due to their ability to map the affected buildings with high geometric precision. However, we suffer from suboptimal performances in detecting damaged buildings due to earthquakes. This paper presents a novel superpixel based approach incorporates Deep Neural Networks (DNN) with a modified segmentation method, for more precise building damage detection from VHR imagery. Firstly, a modified Fast Scanning and Adaptive Merging method is extended to create initial over-segmentation. Secondly, the segments are properly merged based on the Region Adjacent Graph (RAG). Thirdly, a pre-trained DNN using Stacked Denoising Auto-Encoders (SDAE-DNN) is presented, to exploit the rich semantic features for building damage detection. Experimental results on a WorldView-2 imagery from Nepal Earthquake of 2015 demonstrate the feasibility and effectiveness of our method, which could boost detection accuracy through learning more intrinsic and discriminative features, which outperforms other methods using alternative classifiers.