Transfer Learning Approach For Recognizing Natural Disasters Video

Natural disasters are uncontrolled phenomena that happen every year and cause severe loss of life, property, and environmental damage. Transfer Learning-based real-time catastrophe recognition, on the other hand, can help victims and emergency response organizations in the early phases of these devastating situations. There are still gaps in the research when it comes to real-time natural catastrophe identification. In this research, a dataset for collaborative natural disaster categorization in video is presented. There was also a proposal for a VGG-16 with four classification heads. The network was trained to detect natural catastrophes such as cyclones, hurricanes, earthquakes, floods, and wildfires in video using transfer learning. When evaluated in a controlled setting, the model performed well on all four categorization tasks. As a consequence of the study, it was discovered that by combining a lightweight model with transfer learning, effective natural disaster detection may be achieved. This research will hopefully lead to the development of monitoring or technology that could accurately identify natural catastrophes on the scene and in live time, enabling for speedier rescue missions and less lives lost, and homes damaged.