Abstract
Abstract. Road traffic infrastructure plays a key role in emergency management. It allows to evacuate people from the affected area in the shortest possible time, as well as to organize rapid emergency response. However, disasters often cause the destruction of roads, railways and pedestrian routes, which can significantly affect the evacuation plan and availability of facilities for emergency services, which increases the response time and thereby increases the losses. Therefore, it is very important to quickly provide emergency services with necessary post-disaster maps, created on the principles of rapid mapping. Change detection based on geospatial data before and after damage can make rapid and automatic assessment possible with reasonable accuracy and speed. This research proposes a new approach for detecting damage and detecting the state and availability of the road network based on the satellite imagery data, unmanned aerial vehicles (UAVs) and SAR using various methods of image analysis. We also provided an assessment of the resulting combined mathematical model based on neural networks and spatial analysis approaches.
Highlights
Natural disasters as well as major man made incidents are an increasingly serious threat for civil society
We proposed an approach designed for roads semantic segmentation using different remote sensing data: unmanned aerial vehicles (UAVs), satellite and synthetic aperture radar (SAR)
To evaluate the results of routing, we used Average Path Length Similarity (APLS) metric proposed by organizers of ‘SpaceNet Road Detection and Routing Challenge’ (Adam Van Etten, 2017), which is based on graph theory and estimates graph similarity matching, focusing on the logical topology of the graph
Summary
Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Earthquakes, landslides, collapse and debris flow, floods etc. Usually cause damage or inaccessibility to roads network. The roads network, considered as transportation lifeline, has a critical impact on rescue and reconstruction missions after earthquake. Effective, fast and coordinated disaster management crucially depends on the availability of real-time roads conditions information in the affected area. Emergency managers require timely and accurate information on areas affected by disasters to prioritize relief efforts and plan mitigation measures against damage (Ge L., et al 2015). Allows decision makers to obtain the transport accessibility and arrange relief routes. The extraction and assessment of road damage information is quite necessary
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