The time it takes to properly assess damage in the wake of a major event can be the difference between life and death. However, emergency responders must often navigate disruptions to local communication and transportation infrastructure, making accurate assessments dangerous, difficult, and slow. While satellite and aerial imagery offer a less risky alternative that covers more ground, analysts must still conduct manual, time-intensive assessments of images.
The xView2 Challenge used high-resolution RGB satellite imagery and competitors developed algorithms to identify buildings and score structural damage before and after a variety of natural disasters around the world.
We implemented a Siamese-based approach for semantic segmentation tasks focused on assessing building damage levels in pre- and post-disaster imagery. The core of the model architecture is based on UNet, with shared parameters between the upper and lower arms of the network for instance segmentation.
We used xBD dataset, a publicly available dataset, to train and evaluate our proposed network performance. Detailed information about this dataset is provided in "xBD: A Dataset for Assessing Building Damage from Satellite Imagery" by Ritwik Gupta et al.
On the Deployment of Post-Disaster Building Damage Assessment Tools using Satellite Imagery: A Deep Learning Approach by Shahrzad Gholami et al.