What is it about?

Various approaches have been proposed to address the delineation of built-up areas for a wide range of applications. Recently developed approaches are based on the increasing availability of road network data. However, most approaches have employed one or more parameters to divide built-up from non-built-up areas. Very few studies have discussed how to determine appropriate thresholds for such parameters. This study employed an empirical approach for threshold determination, and validated that the approach is applicable for the delineation of built-up areas using road network data. A series of experiments were designed to investigate the most-appropriate thresholds (determined using a similarity measure) for multiple parameters of three existing approaches (street blocks, grid-based, and kernel density) with regard to different administrative regions and cities/towns. The results show that in most cases, the most-appropriate thresholds or ranges for different subdivisions are either identical or overlap—thus validating the use of the most-appropriate thresholds to delineate built-up areas for one or multiple small subdivisions and, by inference, for a much larger region.

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Why is it important?

The results show that in most cases, the most-appropriate thresholds or ranges for different subdivisions are either identical or overlap—thus validating the use of the most-appropriate thresholds to delineate built-up areas for one or multiple small subdivisions and, by inference, for a much larger region.

Perspectives

The results show that in most cases, the most-appropriate thresholds or ranges for different subdivisions are either identical or overlap—thus validating the use of the most-appropriate thresholds to delineate built-up areas for one or multiple small subdivisions and, by inference, for a much larger region.

Dr QI ZHOU
China University of Geosciences

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This page is a summary of: Empirical approach to threshold determination for the delineation of built-up areas with road network data, PLoS ONE, March 2018, PLOS,
DOI: 10.1371/journal.pone.0194806.
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