Expert Knowledge as Basis for Assessing an Automatic Matching Procedure
Bibliographic Data
| ID | 22034646 |
|---|---|
| Authors | Juan José Ruiz-Lendínez (0000-0002-6492-4865, Universidad de Jaén, corresponding author), Francisco Javier Ariza López (0000-0002-5204-3630, Universidad de Jaén), Manuel Antonio Ureña-Cámara (0000-0002-6373-4410, Universidad de Jaén) |
| Year | 2021 |
| Volume | 10 |
| Issue | 5 |
| Pages | 289 |
| Publication date | 2021-05-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10050289 |
| OpenAlex | W3158745199 |
| Language | EN |
| References cited | 40 |
The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks
Automation · Cartography · Data mining · Geography · Geospatial analysis · Machine learning · Statistics · Automated Road and Building Extraction · Computer Science · Engineering · Geographic Information Systems Studies · Mathematics · Remote Sensing and LiDAR Applications · Artificial Intelligence
| Highly cited | No |
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