NASA Flood Extent Detection

External

Linked on 1/6/2023

This dataset contains synthetic aperture radar (SAR) raster imagery for various flood events acquired from the European Space Agencys Sentinel-1A and Sentinel-1B missions, providing C-Band dual-polarized imagery that spans geographical areas of interest in the United States and Bangladesh. The main emphasis was on the labeling of open water areas where specular reflection of the radar signal off of the relatively still, flat open water surface results in reduced backscatter, low amplitude, and an overall darkened appearance within the image. The labels for the water surface reflectance are also provided in GeoTiff rasterized file format in scenes aligned with the SAR source raster imagery.

Dataset Characteristics

Image

Subject Area

Physical Science

Associated Tasks

Classification

Feature Type

Real

# Instances

50000

# Features

-

Dataset Information

For what purpose was the dataset created?

This dataset contains synthetic aperture radar (SAR) raster imagery for various flood events acquired from the European Space Agency's Sentinel-1A and Sentinel-1B missions, providing C-Band dual-polarized imagery that spans geographical areas of interest in the United States and Bangladesh. The main emphasis was on the labeling of open water areas where specular reflection of the radar signal off of the relatively still, flat open water surface results in reduced backscatter, low amplitude, and an overall darkened appearance within the image. The labels for the water surface reflectance are also provided in GeoTiff rasterized file format in scenes aligned with the SAR source raster imagery.

Who funded the creation of the dataset?

NASA-IMPACT, University of Alabama in Huntsville

What do the instances in this dataset represent?

Photos

Has Missing Values?

No

Introductory Paper

Curating flood extent data and leveraging citizen science for benchmarking machine learning solutions

By Shubhankar Gahlot, Muthukumaran Ramasubramanian, Iksha Gurung, Ronny Hãnsch, Andrew Molthan, and Manil Maskey. 2022

Published in AGU Journals

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Citations/Acknowledgements

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Creators

Shubhankar Gahlot

sgahlot@hawk.iit.edu

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