Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment

TL;DR

This paper presents a deep learning framework utilizing open data and pretraining techniques for efficient aerial image segmentation to assess disaster impact, specifically identifying affected areas and roads post-disaster.

Contribution

It introduces a novel framework combining segmentation neural networks, open data from OpenStreetMap, and graph theory for rapid disaster impact assessment from satellite images.

Findings

Pretraining on ImageNet improves segmentation performance.

OpenStreetMap data enables training without manual annotation.

ENetSeparable achieves comparable results with fewer parameters.

Abstract

Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neural networks is proposed to identify impacted areas and accessible roads in post-disaster scenarios. The effectiveness of pretraining with ImageNet on the task of aerial image segmentation has been analysed and performances of popular segmentation models compared. Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models. Open data available from OpenStreetMap (OSM) is used for training, forgoing the need for time-consuming manual annotation. The method also makes use of graph theory to update road network data available from OSM and to detect the changes caused by a natural disaster. Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer parameters compared to ENet, achieved comparable segmentation results to that of the state-of-the-art networks.