Although many research studies focus on urban environments, the peculiar characteristics of alpine regions are underexplored, with few investigations on aspects like sparse network infrastructure, varied patterns of land use, and less connectivity. Also, the underutilization of domain knowledge and expert-labeled data in existing methods limits the development of models that are regionally adaptable and contextually relevant. Although such limitations can be overcome by supervised learning, it has seen very limited applications to date in the analysis of mobility data and, in particular, CDR-based place identification. There is a lack of general methodologies for the creation of high-quality training datasets that capture the complexities of specific regions. We attempted to solve this by developing a supervised approach applied to the city of Milan [16]. Despite this, the identification and exploitation of discriminative CDR features remain insufficiently explored as well, which further limits the precision and scalability of existing approaches. These gaps highlight the requirement for a tailor-made framework which integrates supervised learning and domain knowledge to realize accurate place identification in difficult contexts, like alpine environments. Robust analysis methods are required to effectively analyze CDR data in such a complex geographic setting. In the area of machine learning, various algorithms play different roles in solving the classification and clustering problems associated with identifying location patterns in mountainous regions. Clustering has been explored in the literature as a fundamental approach for the identification of important places from mobility data [17]. Cluster analysis is an unsupervised machine learning technique that divides unlabeled data points into continued and distinct groups called clusters based on similarities among the data. It tries to achieve a large intra-cluster similarity and a small inter-cluster similarity; hence, a cluster contains comparable objects. In the context of place identification, various clustering algorithms have been applied to meaningful location extraction from mobility data. [PERSON] et al. [18] demonstrated the effectiveness of clustering for identifying significant locations from global system for mobile communications (GSM) cell data. Building on this foundation, [PERSON] et al. [19] used k-means clustering to analyze weekly call patterns and successfully distinguished between work and home locations based on temporal communication patterns. Similarly, [PERSON] et al. [20] used clustering approaches to identify meaningful locations from mobile phone data while addressing privacy concerns. [PERSON] et al. [13] represent another major contribution to the area, introducing a much more sophisticated approach which merges clustering with supervised learning. First, it clusters frequently visited locations and then uses logistic regression in order to classify those clusters into home or work locations. A regression model trained on ground-truth data from 18 volunteers showed that only two temporal variables were critical to the classifier: frequency of events during work hours (from 1 PM to 5 PM) and home hours (from 7 PM to 7 AM on weekdays and weekends). Although the model parameters were tuned on a relatively small sample of volunteers, the results showed the validity of these temporal feature discriminators extracted from CDR data. [PERSON] et al. [21] further extended this work by adding time-based clustering for place identification. Their approach clusters not only the spatial data points but also temporal patterns, thus enhancing accuracy in the classification of places. This approach of time-based clustering has proved very effective in identifying places that are spatially close yet serve different purposes at different times. Despite their widespread use, clustering algorithms present inherent challenges in place identification applications.