Automatic positional accuracy assessment of geospatial databases using line-based methods

Ruiz-Lendínez, Juan José; López, Francisco Javier Ariza; Ureña-Cámara, Manuel Antonio

doi:10.1179/1752270613y.0000000044

Cited by 21 publications

(27 citation statements)

References 21 publications

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“…Many questions remain open to the next stages of our research. An immediate problem concerns to deal with more complex matching approaches, notably for linear (Mustière and Devogele, 2008) and areal (Ruiz-Lendínez et al, 2013) features.…”

Section: Discussionmentioning

confidence: 99%

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Web Service for Positional Quality Assessment: The WPS Tier

Xavier

López

Ureña-Cámara

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In the field of spatial data every day we have more and more information available, but we still have little or very little information about the quality of spatial data. We consider that the automation of the spatial data quality assessment is a true need for the geomatic sector, and that automation is possible by means of web processing services (WPS), and the application of specific assessment procedures. In this paper we propose and develop a WPS tier centered on the automation of the positional quality assessment. An experiment using the NSSDA positional accuracy method is presented. The experiment involves the uploading by the client of two datasets (reference and evaluation data). The processing is to determine homologous pairs of points (by distance) and calculate the value of positional accuracy under the NSSDA standard. The process generates a small report that is sent to the client. From our experiment, we reached some conclusions on the advantages and disadvantages of WPSs when applied to the automation of spatial data accuracy assessments.

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Section: Discussionmentioning

confidence: 99%

“…In our Research Group emerged a successful research focused on the automation of the positional accuracy evaluation, due to Ruiz-Lendínez et al (2013). The authors proposed a solution for automatic positional accuracy assessment of polygonal features using a matching approach.…”

Section: Friis-christensen Et Al (2009) Introduced the Term Distributedmentioning

confidence: 99%

Web Service for Positional Quality Assessment: The WPS Tier

Xavier

López

Ureña-Cámara

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For example, Gösseln and Sester () used the ratio of overlapping area to find corresponding polygonal objects; Tong, Liang, and Jin () improved the Hausdorff distance (Deng, Li, & Chen, ) between two polylines to match road networks. Other geometric measures, such as size, direction, shape, and length, have also been employed in the works of Tong, Shi, and Deng (), Zhang et al (), Ruiz‐lendinez, Ariza‐lopez, and Ureña‐cámara (), and Wang, Chen, Zhao, Ren, and Du (). Among these geometric measures, the ratio of the overlapping area between two polygonal objects is associated with size, direction, and shape (Winter, ; Wang, Tang, Qiu, & Wang, ); thus, it is considered an effective metric to measure similarity (Qi, Li, & Chen, ; Revell & Antoine, ).…”

Section: Related Workmentioning

confidence: 99%

M:N Object matching on multiscale datasets based on MBR combinatorial optimization algorithm and spatial district

Liu

Zhu

et al. 2018

Transactions in GIS

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Object matching is critical for updating, maintaining, integrating, and quality assessing spatial data. However, matching data are often obtained from different sources and have problems of positional discrepancy and different levels of detail. To resolve these problems, this article presents a multiscale polygonal object-matching approach, called the minimum bounding rectangle combinatorial optimization (MBRCO) with spatial district (SD). This method starts with the MBRCO algorithm and its enhancement using the SD to find corresponding MBRs of one-to-one, one-to-many, and many-to-many matching pairs. Then, it aligns the MBRs of the matching pairs to identify objectmatching pairs, which are evaluated using a matching criterion to find geometrically corresponding objects. Our approach was experimentally validated using two topographical datasets at 1:2k and 1:10k. The proposed approach outperforms the common two-way area overlap method and another method based on the contextual information and relaxation labeling algorithm. The proposed method achieved accurate aggregation of the many-tomany matching pairs under the positional discrepancy scenario.

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“…These matching methods based on the probability model are limited to identifying corresponding areal objects that have only 1:1 or 1:2 relationships. In addition, to solve the problem of weight, Ruiz‐Lendínez, Ariza‐López, and Ureña‐Cámara () detected a set of homologous polygons using a generic algorithm (GA) in the matching process for position accuracy assessment of both geospatial databases. They used criteria (minimum bounding rectangle, turn function, perimeter, area, moment of inertia, number of convex and concave angles, etc.)…”

Section: Introductionmentioning

confidence: 99%

A multi‐criteria decision‐making approach for geometric matching of areal objects

Kim

Bang

2017

Transactions in GIS

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We propose a method for geometric areal object matching based on multi-criteria decision making. To enable this method, we focused on determining the matched areal object pairs that have all relations, one-to-one relationships to many-to-many relationships, in different spatial data sets by fusing geometric criteria without user invention.First, we identified candidate corresponding areal object pairs with a graph-based approach in training data. Second, three matching criteria (areal hausdorff distance, intersection ratio, and turning function distance) were calculated in candidate corresponding pairs and these criteria were normalized. Third, the shape similarity was calculated by weighted linear combination using the normalized matching criteria (similarities) with the criteria importance through intercriteria correlation method. Fourth, a threshold (0.738) of the shape similarity estimated in the plot of precision versus recall versus all possible thresholds of training data was applied, and the matched pairs were determined and identified. Finally, we visually validated the detection of similar areal feature pairs and conducted statistical evaluation using precision, recall, and F-measure values from a confusion matrix. Their values were 0.905, 0.848, and 0.876, respectively. These results validate that the proposed classifier, which detects 87.6% of matched areal pairs, is highly accurate.

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Automatic positional accuracy assessment of geospatial databases using line-based methods

Cited by 21 publications

References 21 publications

Web Service for Positional Quality Assessment: The WPS Tier

Web Service for Positional Quality Assessment: The WPS Tier

M:N Object matching on multiscale datasets based on MBR combinatorial optimization algorithm and spatial district

A multi‐criteria decision‐making approach for geometric matching of areal objects

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