Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning

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Abstract

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.

Additional publication details

Publication type Conference Paper
Publication Subtype Conference Paper
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
Volume XLII-4
Year Published 2018
Language English
Publisher International Society for Photogrammetry and Remote Sensing
Contributing office(s) Center for Geospatial Information Science (CEGIS)
Description 5 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 597
Last page 601