An Evacuation Route Model for Disaster Affected Areas

Wagh, Vinaysheel K., Pathak, Pramod, Stynes, Paul and Nardin, Luis G. (2020) An Evacuation Route Model for Disaster Affected Areas. In: Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Republic of Ireland, December 7-8, 2020 (AICS 2020). CEUR Workshop Proceedings (2771). CEUR-WS.org, pp. 61-72.

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Abstract

Natural disasters such as earthquake severely damage buildings and introduce obstacles to people trying to evacuate an affected area. Detecting and analyzing the severity of damage to an affected area is a challenge. This paper proposes a novel model for classifying damaged buildings and supporting people's evacuation from natural disaster affected areas using satellite images. The model integrates image segmentation and classification with a shortest path algorithm. First, buildings are detected from pre-disaster satellite images using the proposed Segmentation model. Second, post-disaster images are classified based on the severity of the damage using the proposed Classification model. Finally, the shortest and safest evacuation route to a rescue shelter is detected using the Dijkstra's algorithm. Results show that the Route Detection model dynamically adapts to new and updated satellite images. The Segmentation model shows an F1 score 5% better than the Building Footprint Extraction model and the Classification model shows F1 scores 8% and 10% better than the VGG16 and VGG19 respectively. The Evacuation Route model is useful to disaster management teams and trapped people for planning safe evacuation routes out of the affected area.

Item Type:	Book Section
Uncontrolled Keywords:	Natural Disaster Management; Image Processing; Deep Learning; Shortest Path Algorithm
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	27 Jul 2021 15:18
Last Modified:	15 Mar 2022 13:28
URI:	https://norma.ncirl.ie/id/eprint/4901

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