Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
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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.
Study Area
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska |
| DOI | 10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022 |
| Year Published | 2022 |
| Language | English |
| Publisher | International Society for Photogrammetry and Remote Sensing (ISPRS) |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 8 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| First page | 449 |
| Last page | 456 |
| Conference Location | Florence, Italy |
| Conference Date | August 22-28, 2022 |
| Country | United States |
| State | Alaska |
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