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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 449-456 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Issue number | 4/W1-2022 |
| DOIs | |
| State | Published - Aug 5 2022 |
| Event | 2022 Free and Open Source Software for Geospatial, FOSS4G 2022 - Florence, Italy Duration: Aug 22 2022 → Aug 28 2022 |
Keywords
- National Hydrography Dataset
- U-net
- elevation
- hydrography
- machine learning
- transfer learning
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development