2017
DOI: 10.1371/journal.pone.0175756
|View full text |Cite
|
Sign up to set email alerts
|

Google Earth elevation data extraction and accuracy assessment for transportation applications

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
30
0
2

Year Published

2017
2017
2023
2023

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 58 publications
(32 citation statements)
references
References 20 publications
0
30
0
2
Order By: Relevance
“…Transportation infrastructures as on-ramps are associated with grade changes (as from highway to minor roads), can indicate static traffic bottlenecks (Wang et al 2017). For a possible detection of these traffic bottlenecks, a transportation infrastructure classification scheme for different TIP densities could be tested.…”
Section: Discussionmentioning
confidence: 99%
“…Transportation infrastructures as on-ramps are associated with grade changes (as from highway to minor roads), can indicate static traffic bottlenecks (Wang et al 2017). For a possible detection of these traffic bottlenecks, a transportation infrastructure classification scheme for different TIP densities could be tested.…”
Section: Discussionmentioning
confidence: 99%
“…This elevation data in the United States is available in National Elevation Dataset (NED), provided by the Geological Survey (USGS), at resolutions between 1 arc-second (about 30 meters) and 1/9 arc-second (about 3 meters) depending on the location. According to [7] that assessed the accuracy of road elevation data extracted from Google Earth, root mean squared error (RMSE) and standard deviation of roadway elevation error are 2.27 meters and 2.27 meters, respectively, even in the areas where USGS NED provides 1/3 arc-second (about 10 meters) resolution. Thus, Google Elevation API provides sufficient accuracy and resolution for generating road grade map.…”
Section: A Road Grade Map Generationmentioning
confidence: 99%
“…Velocity (v) is a state variable and travelled distance (s) is the independent variable. The vehicle longitudinal dynamics (7) in the spatial domain is…”
Section: B High-level Planner: Dynamic Programming Formulationmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, road-network datasets, such as OSM/shapefile data from OpenStreetMap (http://www.openstreetmap.org), are usually stored as vectors in two-dimensional (2D) coordinates (longitude/latitude), missing the elevation information in most cases (Wang, Zou, Henrickson, Tang, & Park, 2017). Node coordinates determine the distance between nodes during the construction of a graph from a road network (Luxen & Vetter, 2011) and directly impact the results of shortest-path computations, especially for a graph whose arcs' weights are set as distance.…”
mentioning
confidence: 99%