Assessing Damage of Natural Disasters from Satellite Imagery Using a Deep Learning Model

Abstract

Natural disasters are events that arise anywhere on the planet. It causes enormous devastation and places entire cities in need of significant support. The capability to swiftly and precisely deploy rescue services in the affected regions is critical for reducing the impact and saving lives. A two-step model is developed in an attempt to resolve this problem by using satellite images as input. The model draws attention to the structures such as buildings, which are severely damaged. The current deep learning-based computer vision models use pre- and post-disaster satellite images to semantically infer the level of damage to individual buildings after natural disasters. This model alleviates an important roadblock in disaster managerial decisions by simplifying the evaluations of damages caused to the building. We used DeepLabv3+ for semantic segmentation and a custom CNN model for image classification to analyze disaster-related images. This paper describes how satellite data and proficient image analysis may effectively be used to conduct disaster and crisis management to assist jobs that require fast mappings. This model’s performance and accuracy are sub-optimal and are being studied to further improvisations. However, it surpasses the current cutting-edge model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.