Multi-step feature fusion for natural disaster damage assessment on satellite images

TL;DR

This paper introduces a multi-step feature fusion network for classifying building damage in satellite images post-disaster, significantly improving accuracy by integrating pre- and post-disaster data with a novel CNN module.

Contribution

The paper proposes a new multi-step feature fusion CNN module and a Fuse Module for better damage assessment in satellite imagery, enhancing existing models' performance.

Findings

Over 3% accuracy improvement with Vision Transformer

Effective feature fusion at multiple network levels

Validated on large-scale open datasets

Abstract

Quick and accurate assessment of the damage state of buildings after natural disasters is crucial for undertaking properly targeted rescue and subsequent recovery operations, which can have a major impact on the safety of victims and the cost of disaster recovery. The quality of such a process can be significantly improved by harnessing the potential of machine learning methods in computer vision. This paper presents a novel damage assessment method using an original multi-step feature fusion network for the classification of the damage state of buildings based on pre- and post-disaster large-scale satellite images. We introduce a novel convolutional neural network (CNN) module that performs feature fusion at multiple network levels between pre- and post-disaster images in the horizontal and vertical directions of CNN network. An additional network element - Fuse Module - was proposed to adapt any CNN model to analyze image pairs in the issue of pair classification. We use, open, large-scale datasets (IDA-BD and xView2) to verify, that the proposed method is suitable to improve on existing state-of-the-art architectures. We report over a 3 percentage point increase in the accuracy of the Vision Transformer model.