Influence of Sample Size on Automatic Positional Accuracy Assessment Methods for Urban Areas

Abstract

In recent years, new approaches aimed to increase the automation level of positional accuracy assessment processes for spatial data have been developed. However, in such cases, an aspect as significant as sample size has not yet been addressed. In this paper, we study the influence of sample size when estimating the planimetric positional accuracy of urban databases by means of an automatic assessment using polygon-based methodology. Our study is based on a simulation process, which extracts pairs of homologous polygons from the assessed and reference data sources and applies two buffer-based methods. The parameter used for determining the different sizes (which range from 5 km up to 100 km) has been the length of the polygons' perimeter, and for each sample size 1000 simulations were run. After completing the simulation process, the comparisons between the estimated distribution functions for each sample and population distribution function were carried out by means of the Kolmogorov-Smirnov test. Results show a significant reduction in the variability of estimations when sample size increased from 5 km to 100 km.

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

“…In addition, the determination of the reference data is not the only problem, there are other critical aspects on which the success of our ADM depends. Thus, and from a statistical and practical perspective, the most controversial aspect of any PAA method is the number, location and categorization of control elements [41], that is to say, the main aspects related to the sample: its size-specifically, its influence on the uncertainty of the estimated accuracy values-, its distribution and its categorization. In the specific case of our ADM, it only makes sense to address the first and last of the aforementioned aspects because the sample distribution depends on the spatial distribution of the phenomenon studied-in our case, for example, the sample dis- Originally developed for linear entities, this metric had to be adapted to the lineclosed case-polygons (Figure 4b).…”

“…Such is the case of [40,42,43]. In order to fill this gap, Ariza et al [41] studied the influence of sample size in terms of uncertainty when automatically estimating the planimetric positional accuracy of urban GDBs by means of these types of methods (buffer methods such as the SBOM).…”

Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods

“…Both GDBs are presented as a set of vector covers distributed by layers, including a vector layer of buildings (city-blocks) that contains the same type of geometrical information. Their description, the degree of fulfillment of the previously mentioned interoperability requirements, as well as the justification for the selection of features-buildings as polygonal features for developing the inter-elements matching procedure and the vertexes which define their contour as well-defined punctual features for assessing the positional accuracy-are provided in detail in [1,2], so once again we must note that these issues will not be addressed here. With regard to the independence and coexistence constraints, both GDBs were independently produced which means that the tested source (BCN25) is not derived from the reference source (MTA10), and thanks to the limitations imposed by the matching methodology used-through a matching accuracy indicator [1]-all the elements used in our APAA procedure exist in both datasets.…”

“…-5 -the inter-elements matching procedure and the vertexes which define their contour as well-defined punctual features for assessing the positional accuracy-are provided in detail in [1,2], so once again we must note that these issues will not be addressed here. With regard to the independence and coexistence constraints, both GDBs were independently produced which means that the tested source (BCN25) is not derived from the reference source (MTA10), and thanks to the limitations imposed by the matching methodology used-through a matching accuracy indicator [1]-all the elements used in our APAA procedure exist in both datasets.…”

“…The position is the basis of mapping and navigation, which are essential for engineering, natural sciences, land management, etc. Consequently, the assessment of the positional quality of cartographic products has become an issue of particular significance and relevance [1,2]. Positional quality is determined by positional accuracy [3] which, in turn, is evaluated by means of statistical methods based on measuring positional discrepancies between the location of "well-defined point entities" stored in a geospatial database (GDB) and their true (real world) location [1].…”