Crisis Assessment Through Satellite Footage Using Deep Learning Techniques for Efficient Disaster Response

Natural disasters possess the capacity to cause substantial and extensive harm, resulting in noteworthy economic ramifications. Interestingly, there has been a noticeable increase in the amount of loss and damage brought on by these occurrences in recent years. As such, disaster management organizations have an even greater need to proactively protect communities through the development of efficient management plans. Artificial intelligence (AI) approaches have been used in a number of research projects to analyze catastrophe-related data, improving the caliber of decision-making related to disaster management. The volume and diversity of data from satellite photography make it difficult to comprehend, despite the large amount of data it offers for a variety of uses. Manual ground inspections are usually required for damage assessment, which is a time-consuming and ineffective procedure. To address these issues, this work presents a novel deep learning algorithm for classifying buildings in satellite photos as damaged or undamaged.