Research On Road Extraction And Road Network Generation For Evacuation In Fire Disaster

The occurrence of fire disaster brings serious losses to humans.The research on fire prevention,monitoring and crowd evacuation has become a hot topic.Due to the limitation of space and perspective,when a large-scale fire occurs,rescuers are often unable to know the real-time environmental conditions around the fire site,which made the rescue work more difficult.In order to help people to do rescue work and crowd evacuation task,this thesis proposes a large-scale road extraction method based on aerial images and the generation of evacuation road networks in fire disaster,using deep learning networks and image processing techniques to compare the roads and fire site information in aerial images.Our work provides understandable semantic information to rescuers from a high-altitude perspective,assist in decision-making work such as emergency management and crowd evacuation.The main research work of this thesis is as follow:1)The research status of road network extraction are reviewed,the existing fire detection algorithms are analyzed and summarized in this thesis.2)A road extraction network D-CrossLinkNet is proposed,it use cross-resolution connection and double dilated convolution blocks to achieve the task of road networks extraction from aerial images.Experimental results on two benchmark datasets(Beijing and Shanghai dataset,Deep Globe dataset)demonstrate the effectiveness and superiority of the proposed network.3)A fire recognition algorithm based on RGB,YCbCr and HSI space rules is implemented,which can be used for images of various formats and resolutions to identify and locate fire site in the images.The results of road network and fire recognition is combined.We mark the fire sites on the road as obstacles,thereby generating a road network with fire site marks.