Horizontal positional accuracy of Google Earth's imagery over rural areas: a study case in Tamaulipas, Mexico

Cutberto Uriel Paredes-Hernández,Wilver Enrique Salinas-Castillo,Xicoténcatl Martínez-Becerra,Francisco Guevara-Cortina

doi:10.1590/s1982-21702013000400005

Cutberto Uriel Paredes-Hernández, Wilver Enrique Salinas-Castillo

Open Access

https://doi.org/10.1590/s1982-21702013000400005

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Abstract

Due to the popularity of Google Earth (GE), users commonly assume that it is a credible and accurate source of information. Consequently, GE's imagery is frequently used in scientific and others projects. However, Google states that data available in their geographic products are only approximations and, therefore, their accuracy is not officially documented. In this paper, the horizontal positional accuracy of GE's imagery is assessed by means of comparing coordinates extracted from a rural cadastral database against coordinates extracted from well-defined and inferred check points in GE's imagery. The results suggest that if a large number of well-defined points are extracted from areas of high resolution imagery, GE's imagery over rural areas meets the horizontal accuracy requirements of the ASPRS for the production of "Class 1" 1:20,000 maps. Nonetheless, the results also show that georegistration and large horizontal errors occur in GE's imagery. Consequently, despite its overall horizontal positional accuracy, coordinates extracted from GE's imagery should be used with caution.

Highlights

Google Earth (GE) is the most popular virtual globe that offers free access to high resolution imagery for most of the planet
2.3 Accuracy Assessment (Methodology) Once the coordinates of the check points (CPs) were extracted from both datasets, GE’s imagery horizontal positional accuracy was assessed in terms of x, y and horizontal root mean squared error (RMSEx, RMSEy and RMSEr, respectively) (equations taken from FGDC (1998)): (1)
Georegistration errors are identified visually from disjoint linear features such as roads and parcel boundaries (POTERE, 2008). These errors are inherited by all locations captured by misregistered images and, when new imagery is added, the location of features extracted from misregistered images “will be offset by any positional difference in the new registration” (GOODCHILD et al, 2012, p. 11089)

Summary

INTRODUCTION

Google Earth (GE) is the most popular virtual globe that offers free access to high resolution imagery for most of the planet. The RMSEr estimated for GE by Benker, Langford and Pavlis (2011) should be regarded only as a statistic relevant to their study and not as a valid accuracy assessment of GE (SALINAS-CASTILLO; PAREDES-HERNANDEZ, 2013) In another regional study, Yousefzadeh and Mojaradi (2012) estimated GE’s horizontal accuracy in 6.1 m RMSEr, with a mean horizontal error of 4.8 m, using 13 check points (CPs) collected from 1:2,000-scale maps and distributed over a study area of approximately 215 km in width located in southern Iran. We describe the datasets used in this study and the methodology used to assess the horizontal accuracy of GE’s imagery, present the results of the study case and, draw a series of conclusions from the results of this and other studies

DATASETS AND METHODOLOGY

RESULTS

CONCLUSIONS