A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques

A Supervised Approach for Land Use Identification in Trento Using Mobile Phone Data as an Alternative to Unsupervised Clustering Techniques

This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located in an alpine environment, Trento presents unique geographic challenges, including varied terrain and sparse network coverage, mak...

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Bibliographic Details
Main Authors: Manuel Mendoza-Hurtado, Gonzalo Cerruela-García, Domingo Ortiz-Boyer
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Applied Sciences
Subjects:
machine learning
place identification
call detail records
urban mobility
supervised learning
clustering
Online Access:https://www.mdpi.com/2076-3417/15/4/1753
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