High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such as roads and overpasses, can form barriers that incorrectly alter flow accumulation models, and hinder the extraction of accurate surface water drainage networks. This study tests a deep learning approach to identify the intersections of roads and stream valleys, whereby valley channels can be burned through road embankments in a HR DEM for subsequent flow accumulation modeling, and proper natural drainage network extraction.
Citation Information
| Publication Year | 2018 |
|---|---|
| Title | Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning |
| DOI | 10.5194/isprs-archives-XLII-4-597-2018 |
| Authors | Larry Stanislawski, Tyler Brockmeyer, Ethan J. Shavers |
| Publication Type | Conference Paper |
| Publication Subtype | Conference Paper |
| Index ID | 70200636 |
| 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
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Larry Stanislawski
Research CartographerEmailPhoneEthan Shavers, PhD
CEGIS Section Chief/ Supervisory GeographerEmailPhone