Document Type

Conference Paper

Publication Date

2020

Publication Title

Proceedings of the 2020 IISE Annual Conference

Pages

1-6

Conference Name

IISE Annual Conference & Expo 2020, May 30-June 2, 2020, New Orleans, Louisiana

Abstract

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how to apply remote sensor data in decision-making process for disaster preparedness and response. To achieve this goal, the satellite imagery is used as a data source for the decision-makers. Since the real time satellite imagery are a kind of meta data, big data helps in reading the information. Machine learning algorithms is applied to classify and analyze the data.

Rights

© 2020 Institute of Industrial and Systems Engineers (IISE) 2020. All rights reserved.

Included with the kind written permission of the copyright holder.

ORCID

0000-0003-2830-675X (Gheorghe)

Original Publication Citation

Pour, F. S. A., & Gheorghe, A. (2020) Disaster damage categorization applying satellite images and machine learning algorithm. IIE Annual Conference. Proceedings, (2020), 1-6.

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