While clustering is an easy way to analyze data using unsupervised learning and with limited preprocessing, the clusters produced are inherently based on the characteristics of the data, which might not be aligned with the classification desired, as stated by [22]. This is because the clusters that are created from measures of similarity or proximity do not necessarily map onto semantic categories of interest for the application, such as the identification of home or work locations. The limitations of clustering become particularly apparent when dealing with the complexity of today's mobility data, especially in challenging scenarios such as CDR analysis. Issues such as noisy data, irregular sampling rates, computational scalability, and privacy preservation highlight the need for more sophisticated approaches. This encourages researchers to seek help from another methodology, such as supervised classification. Some benefits of supervised classification for place identification include learning patterns from correctly labeled training data. In this approach, a model learns a datasetof input features with their desired outputs, or labels, in order to predict accurate labels of new, unseen data points. On the contrary, supervised learning explicitly uses domain knowledge by labeling examples and explicitly separates the training and test datasets. This provides a more systematic approach that enables the model to learn particular patterns of specific types of places based on actual examples rather than relying on general similarities inherent in the data. Neural networks have also been explored in the field to identify visual patterns in the areas. They are mostly related to analyzing land use patterns [23; 24], but they also provide a different approach because they focuses on land-specific characteristics, such as trees, asphalt, rivers, etc. These approaches differ from our approach since they focus on the land terrain and lack the dynamics and mobility patterns that we are studying with the mobile data from CDRs. Multi-class classification [25; 26] is a category of supervised learning in which an algorithm is trained to classify data points into one of several classes. Other than binary classification, which contains only two classes, multi-class classification includes several labels and is often required to use complex models since it may include so many varieties of outcomes. One-vs.-rest and one-vs.-one methods extend binary classification algorithms for a multi-class problem. This helps the machines to provide more fine-detailed predictions and helps them become useful across different fields. Methods such as support vector machines (SVM), random forest (RF), and k-nearest neighbors (kNN) have also been found quite useful in multi-class classification problems [25]. SVMs are powerful because they can construct high-dimensional hyperplanes for decision boundaries, generally using kernel functions to handle nonlinear separability. RF, an ensemble learning method based on decision trees, excels in robustness and generalization by reducing overfitting through bootstrap aggregation and random feature selection. kNN, a distance-based method, is simple yet effective, relying on the majority class among the nearest neighbors for predictions. While both supervised and unsupervised learning methods offer valuable approaches for spatial analysis, our work focuses on supervised learning techniques, particularly leveraging classification algorithms to capitalize on expert domain knowledge of the region. Rather than relying on the unsupervised analysis of unlabeled data, our method leverages expert-labeled locations to train a classifier that can effectively distinguish between different types of places. Building upon these machine learning foundations and considering the unique characteristics of the province of Trento, we propose a supervised learning approach to place identification using CDR mobility data, leveraging classification algorithms to address the specific challenges posed by alpine environments.