Next Article in Journal
Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra
Next Article in Special Issue
Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover
Previous Article in Journal
Differences in Thematic Map Reading by Students and Their Geography Teacher
Previous Article in Special Issue
Map Metadata: the Basis of the Retrieval System of Digital Collections

POI Mining for Land Use Classification: A Case Study

by 1,2, 1,2,* and 1
Centre of Informatics and Systems (CISUC), University of Coimbra, 3030-290 Coimbra, Portugal
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;
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
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    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.

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.

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.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop