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.

This research presents the formalization and verification of the methodology for the automatic generalization of buildings, road networks, forests and surface waters from the Topographic Objects Database (BDOT10k) in Poland. The paper makes the following contributions. First, the generalization methodology contained in the official documents was acquired and presented in the form of the knowledge base. Second, the possibilities and limitations of the implementation of the knowledge base in ArcGIS were discussed. Third, the suitability of the BDOT10k structure for the purpose of automatic generalization performance was verified. As a result of the conducted generalization tests, it was found that the formalization and implementation of the methodology contained in the official specifications, in the automatic mode is not entirely possible. The generalization results, however, are promising. The presented research is in line with the studies recently conducted not only by Polish but also other European national mapping agencies.

The presented research concerns the methodology for selecting settlements and road networks from 1:250 000 to 1:500 000 and 1:1 000 000 scales. The developed methodology is based on the provisions of the Regulation of the Ministry of Interior from 17 November 2011. The correctness of the generalization principles contained in the Regulation has not yet been verified. Thus this paper aims to fulfil this gap by evaluating map specifications concerning settlement and road network generalizations. The goal was to automate the selection process by using formalized cartographic knowledge. The selection operators and their parameters were developed and implemented in the form of a generalization model. The input data was the General Geographic Object Database (GGOD), whose detail level corresponds to 1:250 000 scale. The presented research is in line with works on the automation of GGOD generalization performed by the National Mapping Agency (NMA) in Poland (GUGiK). The paper makes the following contributions. First, the selection methodology contained in the Regulation was formalised and presented in the form of a knowledge base. Second, the models for the generalization process were developed. The developed methodology was evaluated by generalizing the settlements and roads in the test area. The results of the settlement and road network generalization for both 1:500 000 and 1:1 000 000 detail levels were compared with the maps designed manually by experienced cartographers.

The decision about removing or maintaining an object while changing detail level requires taking into account many features of the object itself and its surrounding. Automatic generalization is the optimal way to obtain maps at various scales, based on a single spatial database, storing up-to-date information with a high level of spatial accuracy. Researchers agree on the need for fully automating the generalization process (Stoter et al., 2016). Numerous research centres, cartographic agencies as well as commercial companies have undertaken successful attempts of implementing certain generalization solutions (Stoter et al., 2009, 2014, 2016; Regnauld, 2015; Burghardt et al., 2008; Chaundhry and Mackaness, 2008). Nevertheless, an effective and consistent methodology for generalizing small-scale maps has not gained enough attention so far, as most of the conducted research has focused on the acquisition of large-scale maps (Stoter et al., 2016). The presented research aims to fulfil this gap by exploring new variables, which are of the key importance in the automatic settlement selection process at small scales. Addressing this issue is an essential step to propose new algorithms for effective and automatic settlement selection that will contribute to enriching, the sparsely filled small-scale generalization toolbox.The main idea behind this research is using machine learning (ML) for the new variable exploration which can be important in the automatic settlement generalization in small-scales. For automation of the generalization process, cartographic knowledge has to be collected and formalized. So far, a few approaches based on the use of ML have already been proposed. One of the first attempts to determine generalization parameters with the use of ML was performed by Weibel et al. (1995). The learning material was the observation of cartographers manual work. Also, Mustière tried to identify the optimal sequence of the generalization operators for the roads using ML (1998). A different approach was presented by Sester (2000). The goal was to extract the cartographic knowledge from spatial data characteristics, especially from the attributes and geometric properties of objects, regularities and repetitive patterns that govern object selection with the use of decision trees. Lagrange et al. (2000), Balboa and López (2008) also used ML techniques, namely neural networks to generalize line objects. Recently, Sester et al. (2018) proposed the application of deep learning for the task of building generalization. As noticed by Sester et al. (2018), these ideas, although interesting, remained proofs of concepts only. Moreover, they concerned topographic databases and large-scale maps. Promising results of automatic settlement selection in small scales was reported by Karsznia and Weibel (2018). To improve the settlement selection process, they have used data enrichment and ML. Thanks to classification models based on the decision trees, they...

Geograficzne spotkania przy Krakowskim Przedmieściu: wykłady o świecie, Polsce i Warszawie (EBOOK) | Publikacja jest pokłosiem „Geograficznych Spotkań przy Krakowskim Przedmieściu” – cyklu wykładów odbywających się w roku akademickim 2024/2025 na Wydziale Geografii i Studiów Regionalnych Uniwersytetu Warszawskiego. Zebrano w niej interdyscyplinarne | Nauki humanistyczne \ Kulturoznawstwo \ Historia kultury Nauki techniczne \ Architektura i urbanistyka Nauki o Ziemi \ Geografia |

Geograficzne spotkania przy Krakowskim Przedmieściu: wykłady o świecie, Polsce i Warszawie (EBOOK) | Publikacja jest pokłosiem „Geograficznych Spotkań przy Krakowskim Przedmieściu” – cyklu wykładów odbywających się w roku akademickim 2024/2025 na Wydziale Geografii i Studiów Regionalnych Uniwersytetu Warszawskiego. Zebrano w niej interdyscyplinarne | Nauki humanistyczne \ Kulturoznawstwo \ Historia kultury Nauki techniczne \ Architektura i urbanistyka Nauki o Ziemi \ Geografia |