a deep learning method for road extraction in disaster management to increase the efficiency of health services

Both man-made and natural disasters can cause significant damage to property and human lives. giving emergency medical services to the casualties as fast as possible after a disaster is critical. however, the destruction of some infrastructure such as roads, in the aftermath of a disaster, makes this process complicated. artificial intelligence is now more frequently used to solve a wide range of difficult problems. in this paper, a combination of a deep learning model and particle swarm optimization algorithm is proposed to extract roads from satellite images, which can be useful for emergency vehicle drivers to recognize the best available path to reach casualties in disaster zones and give medical services to them faster. the model is evaluated by the evaluation metrics. moreover, it is compared with other common models. the proposed model shows remarkable performance and 92% accuracy. also, some predictions based on the model will be presented.