Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska

Larry Stanislawski; Ethan J. Shavers; Alexander Duffy; Philip T. Thiem; Nattapon Jaroenchai; Shaowen Wang; Zhe Jiang; Barry J. Kronenfeld; Barbara P. Buttenfield

doi:10.5194/isprs-archives-xlviii-4-w1-2022-449-2022

USGS · 70238156

Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska

Larry Stanislawski·Ethan J. Shavers·Alexander Duffy·Philip T. Thiem·Nattapon Jaroenchai·Shaowen Wang·Zhe Jiang·Barry J. Kronenfeld·Barbara P. Buttenfield

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Abstract

The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska.

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Source-reported bounding extent: 66.00582490875226° to 67.79791394109961° latitude; -157.48017274535448° to -152.2946258703545° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Larry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield. 2022-08-06. Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska. https://doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-449-2022

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