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

Abstract

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.

Make It Simple: Effective Road Selection for Small-Scale Map Design Using Decision-Tree-Based Models

“…We can conclude that the development of generalization rules supported by artificial intelligence leads to an algorithm that approximates the decision-making process previously undertaken by a cartographer. Evidence for such an outcome has already been presented in previous research on settlement generalization [13,15]. Here, we verified this assumption for an initial sample of a road selection as a representation of the network data type.…”

“…Machine learning (ML), which is successfully used in cartography and many other domains, provides such opportunities. This approach has proven to be a promising solution for settlement selection at small scales [13][14][15] and generalization of buildings [16], also with the use of deep learning (DL) [17], as well as for smoothing and selecting of line objects [18,19], especially with the use of neural networks [20].…”

Semantic Resizing of Charts Through Generalization:A Case Study with Line Charts

“…Generalization with deep learning models: Our approach employs spatial metrics for determining the generalization operation. Neural networks and deep learning methods are promising avenues for learning good generalization examples through pattern recognition [23]. Exploring how such deep learning approaches can replicate many difficult aspects of expert generalization, such as line smoothing, enlargement, and displacement, would help scale the applicability of chart generalization over a variety of use cases.…”

Scale-Dependent Point Selection Methods for Web Maps

“…A practical textbook for cartographers (Laubert et al 1988, p. 90) recommends the following criteria: the character of the area, settlement density, area, importance, and settlement type. There are approaches to reproduce such a complex selection process by machine learning (Karsznia and Sielicka 2020;Karsznia and Weibel 2018). The authors explore former day's cartographer's decision criteria with these methods and reach similar results.…”