Google Earth elevation data extraction and accuracy assessment for transportation applications

• Published in: PloS one Vol. 12; no. 4; p. e0175756
• Main Authors: Wang, Yinsong, Zou, Yajie, Henrickson, Kristian, Wang, Yinhai, Tang, Jinjun, Park, Byung-Jung
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26-04-2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Archives & records; Benchmarking; Benchmarks; Computer and Information Sciences; Databases, Factual; Datasets; Earth Sciences; Elevation; Energy efficiency; Engineering and Technology; Errors; Extraction; Geographic Information Systems; Geological surveys; Ground truth; Measuring instruments; Memorials & monuments; Multilayers; Physical Sciences; Segmentation; Slopes; Transportation; Transportation applications; Transportation industry; Transportation planning; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0175756

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.