## 5 Conclusions and Future Research Lines  
In this paper, we introduced a novel supervised approach for identifying meaningful places using mobility data derived from call detail records (CDRs) provided by Telecom Italia from the province of Trento mobile phone network. This method serves as a significant advancement to traditional unsupervised clustering techniques, offering a more targeted framework for classification. Unlike cluster analysis, which groups or classifies mobility data based on general characteristics that may not align with specific classification objectives, as we have seen, our approach leverages supervised learning. Specifically, the random forest (RF) algorithm, which demonstrated the best performance in our evaluations, learns a classification function using prior knowledge provided to the model. This is the novelty of our experimental design: it aligns classification goals with labels and does not depend on general clustering patterns. By applying this method, we successfully identified key place categories such as homes, workplaces, and forest areas, enabling us to assess the effectiveness of the proposed framework in classifying spatial grid cells. Furthermore, our findings highlighted the potential of this approach in uncovering the types of activities recorded in CDRs that are most indicative of meaningful place identification, contributing new findings to mobility analysis. Figure 9: kMeans clustering for the full grid. Green: no data present, yellow: work, blue: home, red: forest. Geojson.io visualization. This study shows the utility of supervised learning in mobility analysis, paving the way for more accurate and context-aware place classification in urban and environmental research. This study makes a few important contributions to the literature on mobility and land use analysis. Most notably, it introduces a novel supervised learning framework that can outperform traditional clustering methods when identifying meaningful places from call detail records (CDRs). It addresses an important gap in the literature concerning mobility analysis in sparsely connected and geographically diverse regions by tailoring the methodology to the unique alpine environment of Trento. The superior performance of random forest, especially over sparse RBS coverage and in complex terrain, speaks to the robustness and scalability of this method. It is not only a methodological advancement but will also provide directly actionable insights into the decisions related to infrastructure development and regional planning for urban planners and policymakers. These contributions extend the applicability of supervised learning approaches to other complex terrains, paving the way for future research in mobility analytics and urban studies. Examining the subgrid predictions across different algorithms reveals notable differences in performance. While kNN predictions appear to be somewhat better than those of SVM, which largely fails to identify work locations in the study set, kNN still misclassifies a significant number of home and forest areas. This results in predictions that are less precise and refined compared to those of random forest, which provides a more accurate and well-curated classification. Deep learning (DL) proved to be unsuitable as a ready-to-use solution, as it requires tailored hyperparameter tuning for each specific problem. In the prediction with RF for the whole province of Trento, we can see that it conducts good work overall in forest and home identification while also providing several work hubs around Trento, Pergine Valsugana, and Lavis. It detects a good amount of small villages and communes, providing us with a good overview of the province and showing the potential of RF for generalization once it learns correctly from a smaller subset of data. However, drawbacks and limitations include challenges arising from Trento's alpine terrain and RBS coverage variability.