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Google Earth elevation data extraction and accuracy assessment for transportation applications

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) fo...

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Autores principales: Wang, Yinsong, Zou, Yajie, Henrickson, Kristian, Wang, Yinhai, Tang, Jinjun, Park, Byung-Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405931/
https://www.ncbi.nlm.nih.gov/pubmed/28445480
http://dx.doi.org/10.1371/journal.pone.0175756
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author Wang, Yinsong
Zou, Yajie
Henrickson, Kristian
Wang, Yinhai
Tang, Jinjun
Park, Byung-Jung
author_facet Wang, Yinsong
Zou, Yajie
Henrickson, Kristian
Wang, Yinhai
Tang, Jinjun
Park, Byung-Jung
author_sort Wang, Yinsong
collection PubMed
description 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.
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spelling pubmed-54059312017-05-14 Google Earth elevation data extraction and accuracy assessment for transportation applications Wang, Yinsong Zou, Yajie Henrickson, Kristian Wang, Yinhai Tang, Jinjun Park, Byung-Jung PLoS One Research Article 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. Public Library of Science 2017-04-26 /pmc/articles/PMC5405931/ /pubmed/28445480 http://dx.doi.org/10.1371/journal.pone.0175756 Text en © 2017 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Yinsong
Zou, Yajie
Henrickson, Kristian
Wang, Yinhai
Tang, Jinjun
Park, Byung-Jung
Google Earth elevation data extraction and accuracy assessment for transportation applications
title Google Earth elevation data extraction and accuracy assessment for transportation applications
title_full Google Earth elevation data extraction and accuracy assessment for transportation applications
title_fullStr Google Earth elevation data extraction and accuracy assessment for transportation applications
title_full_unstemmed Google Earth elevation data extraction and accuracy assessment for transportation applications
title_short Google Earth elevation data extraction and accuracy assessment for transportation applications
title_sort google earth elevation data extraction and accuracy assessment for transportation applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405931/
https://www.ncbi.nlm.nih.gov/pubmed/28445480
http://dx.doi.org/10.1371/journal.pone.0175756
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