Google Earth elevation data extraction and accuracy assessment for transportation applications

PLoS One. 2017 Apr 26;12(4):e0175756. doi: 10.1371/journal.pone.0175756. eCollection 2017.

Abstract

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.

MeSH terms

  • Algorithms
  • Databases, Factual*
  • Geographic Information Systems
  • Transportation*

Grants and funding

The authors appreciate the funding support from the Federal Highway Administration (FHWA), the Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10, the National Natural Science Foundation of China (grant no. 51608386), and Shanghai Sailing Program (grant no. 16YF1411900). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funder [Shanghai International Automobile City (Group) Co., Ltd] provided support in the form of salaries for author Yinsong Wang, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of the authors are articulated in the ‘author contributions’ section.