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.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska |
| DOI | 10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022 |
| Authors | Larry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield |
| Publication Type | Conference Paper |
| Publication Subtype | Conference Paper |
| Index ID | 70238156 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Center for Geospatial Information Science (CEGIS) |
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Larry Stanislawski
Research Cartographer
Ethan Shavers, PhD
CEGIS Section Chief/ Supervisory Geographer
Philip Thiem
Computer Scientist
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Larry Stanislawski
Research CartographerEmailPhoneEthan Shavers, PhD
CEGIS Section Chief/ Supervisory GeographerEmailPhonePhilip Thiem
Computer ScientistEmailPhone