Automatic Analysis of Disasters and infer Damage Response using Deep Learning

Effective disaster management techniques are necessary because natural calamities represent serious dangers to both infrastructure and human life. In this study, we address the problem of improving disaster response and recovery by using cutting-edge deep learning methods. The main goals are to categorize damaged infrastructure using cutting-edge techniques and automate the identification of disasters and disaster victims. The methodology used for calamity assessment is described in detail in the paper, which also includes a thorough evaluation of the literature and the integration of deep learning algorithms with disaster photos. The study’s findings support the effectiveness of our suggested strategy for quick disaster response. Notably, the CNN model scored 81.02% accuracy, ResNet50 achieved 90.26%, and VGG16 outperformed with 94.64% accuracy. In summary, by offering creative approaches to lessen the effects of natural emergencies and speed up recovery efforts, our research advances the field of disaster management.