Predicting land use of Italian cities using structural semantic models

Gianni Barlacchi, Bruno Lepri, Alessandro Moschitti

Research output: Contribution to journalConference article

Abstract

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
Volume2006
Publication statusPublished - 1 Jan 2017

Fingerprint

Land use
Semantics

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Predicting land use of Italian cities using structural semantic models. / Barlacchi, Gianni; Lepri, Bruno; Moschitti, Alessandro.

In: CEUR Workshop Proceedings, Vol. 2006, 01.01.2017.

Research output: Contribution to journalConference article

@article{89a82cf0b5ca4e5ab74e9821cd713632,
title = "Predicting land use of Italian cities using structural semantic models",
abstract = "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.",
author = "Gianni Barlacchi and Bruno Lepri and Alessandro Moschitti",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "2006",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",

}

TY - JOUR

T1 - Predicting land use of Italian cities using structural semantic models

AU - Barlacchi, Gianni

AU - Lepri, Bruno

AU - Moschitti, Alessandro

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85037362673&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85037362673&partnerID=8YFLogxK

M3 - Conference article

VL - 2006

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -