CNN-Based Semantic Change Detection in Satellite Imagery

TL;DR

This paper introduces a CNN-based framework that detects semantic changes in satellite imagery to identify accessible roads after disasters, improving damage assessment accuracy for disaster risk management.

Contribution

It presents a novel CNN-based method combined with graph theory and OpenStreetMap data for detecting road network changes post-disaster, addressing limitations of previous approaches.

Findings

Validated on tsunami-affected region data from Palu, Indonesia

Effectively detects accessible roads in post-disaster imagery

Enhances damage assessment accuracy for disaster response

Abstract

Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting changes from pre-disaster imagery. Graph theory is combined with the CNN output for detecting semantic changes in road networks with OpenStreetMap data. Our results are validated with data of a tsunami-affected region in Palu, Indonesia acquired from DigitalGlobe.