Positional Accuracy Assessment of Googleearth in Riyadh

Farah, Ashraf; Algarni, Dafer Ali

doi:10.2478/arsa-2014-0008

Cited by 40 publications

(19 citation statements)

References 3 publications

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“…While the horizontal accuracy of Google Earth images has not been conducted systematically across the world, studies indicate an average value of 2.18 meters for root-mean-square error (Farah and Algarni 2014 We also find biases in Google relative to GPS measures in the Viet Nam input demand estimates. Google estimates bias in the intensive margin of organic fertilizer demand downward by 32.4 percentage points and in the extensive margin of hired labor demand by 11.5 percentage points (Table 8).…”

mentioning

confidence: 67%

Land Measurement Bias:

Dillon¹,

Rao²

2018

ADB Economics Working Paper Series

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mentioning

confidence: 67%

Land Measurement Bias:

Dillon¹,

Rao²

2018

ADB Economics Working Paper Series

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“…Thus, the exterior orientation will absorb some interior errors, thereby influencing the orientation accuracy of the whole image. Many studies have shown that the horizontal positioning accuracy of Google Earth is better than 3 m [38][39][40]. Given that the resolution of GF-1 WFV is 16 m, the accuracy of Google Earth renders it appropriate for validation and to illustrate the influence of compensation.…”

Section: Accuracy Validationmentioning

confidence: 99%

Correction of Pushbroom Satellite Imagery Interior Distortions Independent of Ground Control Points

et al. 2018

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Abstract:Compensating for distortions in pushbroom satellite imagery has a bearing on subsequent earth observation applications. Traditional distortion correction methods usually depend on ground control points (GCPs) acquired from a high-accuracy geometric calibration field (GCF). Due to the high construction costs and site constraints of GCF, it is difficult to perform distortion detection regularly. To solve this problem, distortion detection methods without using GCPs have been proposed, but their application is restricted by rigorous conditions, such as demanding a large amount of calculation or good satellite agility which are not met by most remote sensing satellites. This paper proposes a novel method to correct interior distortions of satellite imagery independent of GCPs. First, a classic geometric calibration method for pushbroom satellite is built and at least three images with overlapping areas are collected, then the forward intersection residual between corresponding points in the images are used to calculate interior distortions. Experiments using the Gaofen-1 (GF-1) wide-field view-1 (WFV-1) sensor demonstrate that the proposed method can increase the level of orientation accuracy from several pixels to within one pixel, thereby almost eliminating interior distortions. Compared with the orientation accuracy by classic GCF method, there exists maximum difference of approximately 0.4 pixel, and the reasons for this discrepancy are analyzed. Generally, this method could be a supplementary method to conventional methods to detect and correct the interior distortion.

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“…Annual land cover can be recorded using historical VHR images such as Quickbird in GE [14], although lower volumes of VHR images were found in unpopulated areas and remote areas as well as sufficient historical VHR data were not available in GE for some years (e.g., data were relatively sparse in 2001, 2002, 2007, and 2008). Several recent studies have reported the geo-referencing errors of VHR images in GE to be in the range of 1.8-5.0 m RMSE [70][71][72]. While the geo-referencing accuracy should be taken into account for all VHR images, this is impractical and any errors would not affect our results severely because the coarse (231.7 m) resolution data was used.…”

Section: Reference Datamentioning

confidence: 99%

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

et al. 2016

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Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001-2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km 2 /year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches.

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Positional Accuracy Assessment of Googleearth in Riyadh

Cited by 40 publications

References 3 publications

Land Measurement Bias:

Land Measurement Bias:

Correction of Pushbroom Satellite Imagery Interior Distortions Independent of Ground Control Points

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

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