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When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning

Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw; Krakowskie Przedmiescie 30, 00-927 Warsaw, Poland
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ISPRS Int. J. Geo-Inf. 2020, 9(4), 230; https://doi.org/10.3390/ijgi9040230
Received: 7 February 2020 / Revised: 7 April 2020 / Accepted: 8 April 2020 / Published: 9 April 2020
(This article belongs to the Special Issue Map Generalization)
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. View Full-Text
Keywords: cartographic generalization; machine learning; settlement selection; small-scale cartographic generalization; machine learning; settlement selection; small-scale
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MDPI and ACS Style

Karsznia, I.; Sielicka, K. When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning. ISPRS Int. J. Geo-Inf. 2020, 9, 230. https://doi.org/10.3390/ijgi9040230

AMA Style

Karsznia I, Sielicka K. When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning. ISPRS International Journal of Geo-Information. 2020; 9(4):230. https://doi.org/10.3390/ijgi9040230

Chicago/Turabian Style

Karsznia, Izabela, and Karolina Sielicka. 2020. "When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning" ISPRS International Journal of Geo-Information 9, no. 4: 230. https://doi.org/10.3390/ijgi9040230

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