A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques  
[PERSON]  
1 Computer and Numerical Analysis, Campus de Rabanales, Albert Einstein Building, University of Cordoba, 14071 Cordoba, Spain; [EMAIL_ADDRESS] (G.C.-G.); [EMAIL_ADDRESS] (D.O.-B. )1 Computer and Numerical Analysis, Campus de Rabanales, Albert Einstein Building, University of Cordoba, 14071 Cordoba, Spain; [EMAIL_ADDRESS] (G.C.-G.); [EMAIL_ADDRESS] (D.O.-B  
While GPS provides highly accurate location data but requires active device participation and higher battery consumption, and radio-frequency proximity enables moderate-accuracy indoor positioning, CDRs offer a passive and privacy-preserving data collection method. CDRs contain essential information about network interactions, including the type of activity (voice calls, SMS messages, or internet usage), timestamps, and the serving radio base station (RBS) identifier. This information enables user geolocation within the RBS coverage area, providing less granular but more widespread data collection. This comprehensive data collection infrastructure has created unprecedented opportunities for understanding human mobility patterns and social dynamics at both individual and population levels, enabling evidence-based decision-making across multiple sectors of society. While the positioning accuracy of CDR-based geolocation is limited by RBS coverage areas, the vast amount of data contained within these records is of great value in identifying and understanding population patterns across various aspects of life. These include work habits, leisure activities, domestic routines, travel patterns, or tourism trends. Recent research has demonstrated the diverse applications of CDR data analysis. For example, a study by [PERSON] et al. (2014) explored the use of mobile phone data to map urban mobility patterns and identify key urban nodes in Shanghai. By analyzing anonymized CDRs, researchers were able to identify areas with high pedestrian traffic and significant daily activity, providing insights for urban planning and infrastructure development. Similarly, [PERSON] et al. (2015) proposed a methodology for identifying routine locations of individual users and assigning semantic interpretations to these locations by utilizing anonymized CDRs extracted from mobile network data. In population analysis, it is especially important to identify significant locations or \"anchor points\" that individuals frequently visit. Among these anchor points, home and work locations are typically the most prominent and frequent, making them critical reference points for understanding population mobility patterns. This growing body of research demonstrates the potential of CDR data to provide insights into human mobility patterns while preserving individual privacy through anonymization. Although the analysis of CDR data has already been successfully applied to several urban contexts, mountainous regions pose unique challenges and opportunities for such studies. The Province of Trento (or Trentino) is a peculiar alpine environment with an extended presence of forests, which constitutes a very interesting case study for extending CDR-based analysis to more complex terrains. The ability to identify and analyze these forested and remote areas using mobile phone data, however, provides quite specific and real value for this mountainous region. Current methodologies in mobility data analytics, especially the process of identifying places, are largely dependent on clustering algorithms. Present unsupervised techniques usually group similar data with respect to some intrinsic properties but cannot align these clusters with semantically meaningful categories such as home, work, or forest. Noisy data, irregular sampling rates, and computational scalability are challenges that become more pronounced for complex geographical terrains such as mountainous regions.