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Detecting Safe Routes During Floods Using Deep Learning

Detecting Safe Routes During Floods Using Deep Learning

Mayank Mathur, Yashi Agarwal, Shubham Pavitra Shah, Lavanya K.
Copyright: © 2020 |Volume: 1 |Issue: 1 |Pages: 13
ISSN: 2644-1675|EISSN: 2644-1683|EISBN13: 9781799809777|DOI: 10.4018/IJBDIA.2020010102
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MLA

Mathur, Mayank, et al. "Detecting Safe Routes During Floods Using Deep Learning." IJBDIA vol.1, no.1 2020: pp.23-35. http://doi.org/10.4018/IJBDIA.2020010102

APA

Mathur, M., Agarwal, Y., Shah, S. P., & K., L. (2020). Detecting Safe Routes During Floods Using Deep Learning. International Journal of Big Data Intelligence and Applications (IJBDIA), 1(1), 23-35. http://doi.org/10.4018/IJBDIA.2020010102

Chicago

Mathur, Mayank, et al. "Detecting Safe Routes During Floods Using Deep Learning," International Journal of Big Data Intelligence and Applications (IJBDIA) 1, no.1: 23-35. http://doi.org/10.4018/IJBDIA.2020010102

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Abstract

Floods are one of the most devastating and frequently occurring natural disasters throughout the world. Floods can cause blockage of roads and hence create trouble for civilians and authorities to navigate in the flooded area. This paper proposes an automated system that uses a road extraction algorithm to extract roads from satellite images to create a highlighted map of all the available roads during floods. The road extraction algorithm the authors developed uses U-net model architecture, a fully convolutional neural network, to extract roads from aerial images (satellite images and drone images). Convolutional Neural Network is robust to shadows and water streams, able to obtain the characteristics of roads adequately and most importantly, able to produce output quickly, which is necessary for flood evacuations and relief. The developed system can be deployed as an Application Programming Interface or stand-alone system, loaded on drones, which will provide the users with a map highlighting safe paths to traverse the flooded areas.

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