A crucial component of our methodology is the development of a high-quality training dataset. We generate this dataset through careful manual labeling of selected subregions using multiple sampling strategies. These strategies include the random selection of areas and targeted sampling of larger regions that contain representative examples of all place types we aim to identify. The labeled data capture the predominant building type or land use within each grid cell, providing ground truth for three main categories: home (residential areas), work (commercial/industrial areas), and forest areas. To evaluate the effectiveness of our supervised approach, we conduct a comparative study against traditional clustering methods commonly used in mobility scenarios [12; 13; 21]. Using mobility CDR data from the province of Trento [27], we implement both supervised classifiers (SVM, kNN, and random forest) and unsupervised approaches (k-means and k-medoids) to identify homes and workplaces. By training our classification model on expertly labeled examples, we aim to predict place categories across the entire study region. This approach ensures that the model learnsfrom reliable, context-specific examples rather than relying solely on emergent patterns in the data. The combination of strategic sampling and expert labeling helps create a robust foundation for the classification task, while the supervised learning framework allows us to directly optimize for our specific place identification objectives. This research seeks to develop a robust supervised learning framework for finding meaningful places using CDR mobility data, hence addressing the limitations of traditional clustering methods. We will train classification models using expertly labeled training data to predict place categories such as home, work, and forest areas with high accuracy. The methodology has been designed for alpine regions, taking the Province of Trento as a case study in order to prove the effectiveness of the approach in such a complex geographical setting, but it should be applicable to other scenarios as well. The added value of the proposed approach will be underlined when compared to that of supervised classifiers, such as SVM, random forest, and kNN, and conventional clustering algorithms, such as k-means and k-medoids. This study also explores which CDR features are the most contributive in enhancing place classification and evaluates how robust such contributions are across contexts. Ultimately, this work seeks to improve the methodologies of data analysis about mobility while providing useful insights for practical application in urban planning, infrastructure development, and management in under-explored territories. Overall, the contributions of our work are as follows:  
* We propose a novel supervised learning approach to meaningful place identification from CDR data, addressing limitations of traditional unsupervised clustering methods typically used for large-scale unlabeled mobility data. * We develop a strategic sampling and expert labeling methodology for creating high-quality training data, focusing on the Province of Trento as a case study. Our approach enables the accurate classification of home, work, and forest areas across the entire region by training on carefully selected and labeled representative subregions. * We conduct a comprehensive comparative analysis between traditional unsupervised methods (k-means and k-medoids) and supervised classifiers (SVM, kNN, and random forest), demonstrating the advantages of our supervised approach. * We provide novel insights into the most discriminative CDR features for place identification, evaluate the method's robustness across different geographical contexts, and identify key considerations for applying supervised learning to mobility data analysis in mountainous regions.