Efficient Large-Scale Damage Assessment After Natural Disasters With UAVS and Deep Learning

Frequent and increasingly severe natural disasters due to climate change threaten human health and infrastructure. The provision of accurate, timely, and understandable information has the potential to revolutionize disaster management. While traditional analyses provide some insights into the data, the complexity, scale, and multi-disciplinary nature of the data necessitate advanced, intelligent solutions. The main barrier is the lack of near real-time data analysis. Recently there has been a surge of practical applications of real-time semantic segmentation. Real-time semantic segmentation requires fast and high-quality predictions. As a result, lightweight architectures with low latency and computational costs are necessary for efficient semantic segmentation. In this article, we developed several encoder-decoder and two-pathway architectures and compared their performance on a novel RescuNet dataset.