USE OF MACHINE LEARNING TECHNIQUES FOR RAPID DETECTION, ASSESSMENT AND MAPPING THE IMPACT OF DISASTERS ON TRANSPORT INFRASTRUCTURE

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.

• Publication year

  2019

• Venue

  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2019-08-21

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.5194/isprs-archives-xlii-3-w8-195-2019
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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