Abstract
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.
Highlights
Aside from cartographic purposes, accurate hydrographic data is an important component of hydrologic modeling, ecosystem analysis and flood forecasting, among several other important tasks (Poppenga, Gesch, and Worstell, 2013)
The National Hydrography Dataset (NHD) is a comprehensive vector database of surface water features for the United States that is managed by the U.S Geological Survey (USGS) and partner organizations, including the Environmental Protection Agency and various state and private organizations
Within the conterminous United States, the high resolution (HR) NHD is a multi-scale data set of hydrographic features comprised from the best available data sources having scales of 1:24,000 or larger
Summary
Aside from cartographic purposes, accurate hydrographic data is an important component of hydrologic modeling, ecosystem analysis and flood forecasting, among several other important tasks (Poppenga, Gesch, and Worstell, 2013). Flow-direction and flow-accumulation modeling can furnish surface-water drainage lines from DEM data (O’Callaghan and Mark, 1984; Jenson and Dominigue, 1988; Tarboton, Bras, and Rodriguez-Iturbe, 1991; Montgomery and Foufoula-Georgiou, 1993; Maidment, 2002; Passalacqua, Tarolli, and FoufoulaGeorgiou, 2010; Passalacqua, Belmont, and FoufoulaGeorgiou, 2012) Given proper validation, such methods can help update HR NHD content (Poppenga, Gesch, and Worstell, 2013). It is possible to use existing vector transportation or other data to automatically create breaches in elevation models where embankments exist for features such as culverts or bridges, and thereby improve subsequently derived drainage models (Waller et al, 2015; Maderal et al, 2016) These methods rely on accurate and complete transportation data, which is not available in all places in the United States, for unpaved roads in rural areas. Extracted roads may be used to update the national transportation database
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