Abstract
In the era of accelerating climate change and growing urban populations, the frequency and severity of natural disasters have increased significantly, posing substantial threats to infrastructure, economic stability, and human lives. Disasters, including the likes of earthquakes, floods, and hurricanes usually are the reasons for serious destruction of buildings, requiring rapid and accurate assessment to aid in emergency response and resource allocation. In light of this, the research aims to deliver a deep learning based building damage assessment model, which is a hybrid architecture consisting of Artificial Intelligence and IOT. In this paper we will examine the use of Internet of Things (IoT) and Artificial Intelligence in disaster management systems in order to improve the automation, transparency, and sustainability in smart intelligence systems. The system should collect and analyze pre-disaster and post-disaster aerial imagery to classify buildings into damage categories, i.e. from no damage to destroyed.. Also, we integrate our model into a wide disaster management system in order to make a visualization of damages on a geospatial interface, that helps the decision-makers to get a quick look at priority areas and streamline the response of disaster. This system’s plan is to assist public authorities, NGOs, and first responders with quick decision making in postdisaster response times.