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Article

POI Mining for Land Use Classification: A Case Study

by 1,2, 1,2,* and 1
1
Centre of Informatics and Systems (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal
2
Instituto Superior de Engenharia de Coimbra (ISEC), Instituto Politécnico de Coimbra, 3030-199 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 493; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090493
Received: 9 June 2020 / Revised: 7 August 2020 / Accepted: 19 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue Geographic Information Extraction and Retrieval)
The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types. View Full-Text
Keywords: data mining; machine learning; land use classification; points of interest; smart cities data mining; machine learning; land use classification; points of interest; smart cities
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  • Externally hosted supplementary file 1
    Link: http://tiny.cc/gq6iqz
    Description: The datasets and figures used are available online.
MDPI and ACS Style

Andrade, R.; Alves, A.; Bento, C. POI Mining for Land Use Classification: A Case Study. ISPRS Int. J. Geo-Inf. 2020, 9, 493. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090493

AMA Style

Andrade R, Alves A, Bento C. POI Mining for Land Use Classification: A Case Study. ISPRS International Journal of Geo-Information. 2020; 9(9):493. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090493

Chicago/Turabian Style

Andrade, Renato, Ana Alves, and Carlos Bento. 2020. "POI Mining for Land Use Classification: A Case Study" ISPRS International Journal of Geo-Information 9, no. 9: 493. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090493

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