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Curating flood extent data and leveraging citizen science for benchmarking machine learning solutions
  • +3
  • Shubhankar Gahlot,
  • Muthukumaran Ramasubramanian,
  • Iksha Gurung,
  • Ronny Hansch,
  • Andrew Molthan,
  • Manil Maskey
Shubhankar Gahlot
University of Alabama in Huntsville

Corresponding Author:sgahlot@hawk.iit.edu

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Muthukumaran Ramasubramanian
University of Alabama in Huntsville
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Iksha Gurung
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Ronny Hansch
TU Berlin
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Andrew Molthan
NASA Marshall Space Flight Center
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Manil Maskey
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We present a labeled machine learning (ML) training dataset derived from Sentinel 1 C-band synthetic aperture radar (SAR) data for flood events. In this paper, we detail the steps to collect, pre-process, label, curate, and catalog the training dataset. Development of benchmark ML models and usage of the training datasets for a data science competition are also presented.