The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 597–601, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-597-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 597–601, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-597-2018

  19 Sep 2018

19 Sep 2018

AUTOMATED ROAD BREACHING TO ENHANCE EXTRACTION OF NATURAL DRAINAGE NETWORKS FROM ELEVATION MODELS THROUGH DEEP LEARNING

L. Stanislawski, T. Brockmeyer, and E. Shavers L. Stanislawski et al.
  • U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA

Keywords: Deep Learning, National Hydrography Dataset, Neural Network, Elevation-derived Drainage Network

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.