When Traditional Selection Fails
How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning
Bibliographic Data
| ID | 22033227 |
|---|---|
| Authors | Izabela Karsznia (0000-0001-5510-8770, University of Warsaw, corresponding author), Karolina Sielicka (0000-0003-2330-2426, University of Warsaw) |
| Year | 2020 |
| Volume | 9 |
| Issue | 4 |
| Pages | 230 |
| Publication date | 2020-04-09 |
| 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/ijgi9040230 |
| OpenAlex | W3015356643 |
| Language | EN |
| Citations received | 6 |
| References cited | 17 |
Effective settlements generalization for small-scale maps is a complex and challenging task. Developing a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we want to fill this gap and explore settlement characteristics, named variables that can be decisive in settlement selection for small-scale maps. We propose 33 variables, both thematic and topological, which may be of importance in the selection process. To find essential variables and assess their weights and correlations, we use machine learning (ML) models, especially decision trees (DT) and decision trees supported by genetic algorithms (DT-GA). With the use of ML models, we automatically classify settlements as selected and omitted. As a result, in each tested case, we achieve automatic settlement selection, an improvement in comparison with the selection based on official national mapping agency (NMA) guidelines and closer to the results obtained in manual map generalization conducted by experienced cartographers
Archaeology · Cartography · Data mining · Data science · Generalization · Geography · Human settlement · Machine learning · Social science · Sociology · Thematic map · Automated Road and Building Extraction · Computer Science · Data Management and Algorithms · Engineering · Geographic Information Systems Studies · Mathematics · Artificial Intelligence
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| Citations per year | 1.5 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |