CNN-Based Semantic Change Detection in Satellite Imagery
This addresses the need for timely and accurate road mapping in disaster-struck areas, where existing methods fail due to invalid assumptions about road topology, representing a domain-specific incremental improvement.
The paper tackles the problem of detecting accessible roads in post-disaster satellite imagery by proposing a CNN-based framework that identifies changes from pre-disaster imagery, achieving results validated with data from a tsunami-affected region in Palu, Indonesia.
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.