REAL-TIME EMBEDDED HPC BASED EARTHQUAKE DAMAGE MAPPING USING 3D LIDAR POINT CLOUDS

In the early hours following the earthquake, supporting humanitarian actions like rescue operations and relief distribution is the primary objective of the rescue managers. The damage mapping can be performed using reliable data that can be obtained from high-resolution satellite imagery but obtaining satellite imagery can be challenging for some days post disaster due to revisit time. Considering the disaster response timing, Unmanned Aerial Vehicles (UAV) are used because ground transportation systems are ineffective due to road blockage. In this work, we make use of Light Detection and Ranging (LiDAR) 3D point cloud data obtained for Haiti Earthquake. The focus of our work is to develop and implement an approach for LiDAR data classification to enable Earthquake damage mapping and detection. This is obtained by running our deep learning network on NVIDIA Jetson Nano embedded supercomputing platform. This approach takes the advantage of embedded High-Performance computing and low power consumption capabilities of Jetson Nano which enhances the classification and promotes rapid response which is the key to manage post-disaster activities. Jetson Nano is a feasible option which provides a GPU architecture that is optimized for running energy-aware deep learning models and which generates the results in real or near-real time. We envisage that our work could be extended to perform near real-time classification of lidar point clouds in a post earthquake scenario.