Predicting land use of Italian cities using structural semantic models

Gianni Barlacchi, Bruno Lepri, Alessandro Moschitti

Research output: Contribution to journalConference article


We propose a hierarchical semantic representation of urban areas extracted from a social network to classify the most predominant land use, which is a very common task in urban computing. We encode geo-social data from Location-Based Social Networks with standard feature vectors and a conceptual tree structure that we call Geo-Tree. We use the latter in kernel machines, which can thus perform accurate classification, exploiting hierarchical substructure of concepts as features. Our comparative study on three datasets extracted from Milan, Rome and Naples shows that Tree Kernels applied to Geo-Trees are very effective improving the state of the art.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2017


ASJC Scopus subject areas

  • Computer Science(all)

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