Extracting Topographic Data from Online Sources to Generate a Digital Elevation Model for Highway Preliminary Geometric Design

Two principal aspects of preliminary highway design are detailed characterization of the geographic and topographic features along potential candidate alignments and preliminary design of the highway along at least one of these candidates. The digital elevation model (DEM) of the existing terrain is essential for these tasks. However, generating a DEM requires excessive time and effort. This research paper proposes a quick and practical technique to extract geographic and topographic data from freely available online resources to generate a DEM for purposes of preliminary highway geometric design. A primary step in construction of the DEM was to quantify relationships between actual distance on the ground, computer screen resolution and size, and scale of the virtual earth, and then use these relationships to extract the data automatically. The area displaying position and elevation data were positioned and subsequently saved as images. Image recognition techniques were then used to interpret the position and elevation data. Data storage management was subsequently conducted to minimize the error generated due to decimal place omission. In doing so, the accuracy of the extracted geographic data were enhanced to meet the requirements for preliminary geometric design. The DEMs were then constructed using the extracted geographic data. To demonstrate the efficacy of the developed method and to verify its accuracy, the paper used data from a mountainous region located in the Sichuan Province of China. The results showed that the proposed method is practical and that the constructed DEM is reliable and reasonable. Further, compared with traditional methods, the proposed DEM construction method is less time-consuming and laborious, and more functionally consistent with available online resources.


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  • Accession Number: 01695709
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Feb 21 2019 9:55AM