Smart Aerial Image Classification using Deep Learning for Drone-Based Emergency Monitoring

DL can provide cutting-edge accuracy for technologies that utilize remote sensing including unmanned aerial vehicles (UAVs), especially enhance the remote detection abilities in responding to emergencies along with catastrophe control activities. In particular, "UAVs" that have sensors for cameras are able to function online difficult accessing disaster-affected places, process images, also notify people in the event of various catastrophes like collapsed buildings, floods, or fire. The goal is to lessen the detrimental impact of disasters on the natural world and the human population as quickly as possible. DL integration introduces high processing demands that prevent the implementation of DNN for different cases where lowlatency restrictions on inference are required for making critical decisions in real-time. Consequently, our interest of this work is the efficient classification of aerial photos taken while a UAV is in flight for emergency response and surveillance purposes. Specifically, an Aerial Photos Collection for response to emergencies systems is proposed and a comparison between many current algorithms has been done. That processing is used to propose EmergencyRes, a CNN design. With lower than 1% accuracy decrease in comparison to the most advanced techniques, it can achieve up to 20x greater performance than currently available models and operate efficiently on low-power existing platforms. It can also handle multi-resolution features utilizing atrous convolutions.