Use of machine learning techniques for rapid detection, assessment and mapping the impact of disasters on transport infrastructure

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Use of machine learning techniques for rapid detection, assessment and mapping the impact of disasters on transport infrastructure. / Kikin, P. M.; Kolesnikov, A. A.; Portnov, A. M.

In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 3/W8, 20.08.2019, p. 195-200.

Research output: Contribution to journal › Conference article › peer-review

Author

Kikin, P. M. ; Kolesnikov, A. A. ; Portnov, A. M. / Use of machine learning techniques for rapid detection, assessment and mapping the impact of disasters on transport infrastructure. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2019 ; Vol. 42, No. 3/W8. pp. 195-200.

BibTeX

@article{604a4e557e584f2da04b15acaed2140f,

title = "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.",

keywords = "Artificial Neural Networks, Machine Learning, Rapid Mapping, Road Network, UAV, Unet",

author = "Kikin, {P. M.} and Kolesnikov, {A. A.} and Portnov, {A. M.}",

note = "Publisher Copyright: {\textcopyright} 2019 International Society for Photogrammetry and Remote Sensing. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 2019 GeoInformation for Disaster Management, Gi4DM 2019 ; Conference date: 03-09-2019 Through 06-09-2019",

year = "2019",

month = aug,

day = "20",

doi = "10.5194/isprs-archives-XLII-3-W8-195-2019",

language = "English",

volume = "42",

pages = "195--200",

journal = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

issn = "1682-1750",

publisher = "International Society for Photogrammetry and Remote Sensing",

number = "3/W8",

}

RIS

TY - JOUR

T1 - Use of machine learning techniques for rapid detection, assessment and mapping the impact of disasters on transport infrastructure

AU - Kikin, P. M.

AU - Kolesnikov, A. A.

AU - Portnov, A. M.

PY - 2019/8/20

Y1 - 2019/8/20

N2 - 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.

AB - 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.

KW - Artificial Neural Networks

KW - Machine Learning

KW - Rapid Mapping

KW - Road Network

KW - UAV

KW - Unet

UR - http://www.scopus.com/inward/record.url?scp=85074247485&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLII-3-W8-195-2019

DO - 10.5194/isprs-archives-XLII-3-W8-195-2019

M3 - Conference article

AN - SCOPUS:85074247485

VL - 42

SP - 195

EP - 200

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

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

SN - 1682-1750

IS - 3/W8

T2 - 2019 GeoInformation for Disaster Management, Gi4DM 2019

Y2 - 3 September 2019 through 6 September 2019

ER -

ID: 76310386