Next Article in Journal
A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications
Next Article in Special Issue
Using Social Networks to Analyze the Spatiotemporal Patterns of the Rolling Stock Manufacturing Industry for Countries in the Belt and Road Initiative
Previous Article in Journal
Supporting Disaster Resilience Spatial Thinking with Serious GeoGames: Project Lily Pad
Previous Article in Special Issue
An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II
Open AccessArticle

Experiment in Finding Look-Alike European Cities Using Urban Atlas Data

Department of Geoinformatics, Faculty of Science, Palacký University, 17. listopadu 50, 779 00 Olomouc, Czech Republic
ISPRS Int. J. Geo-Inf. 2020, 9(6), 406; https://doi.org/10.3390/ijgi9060406
Received: 26 May 2020 / Revised: 22 June 2020 / Accepted: 24 June 2020 / Published: 26 June 2020
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone. View Full-Text
Keywords: Urban Atlas; Orange software; land use; machine learning Urban Atlas; Orange software; land use; machine learning
Show Figures

Graphical abstract

MDPI and ACS Style

Dobesova, Z. Experiment in Finding Look-Alike European Cities Using Urban Atlas Data. ISPRS Int. J. Geo-Inf. 2020, 9, 406.

Show more citation formats Show less citations formats
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

1
Search more from Scilit
 
Search
Back to TopTop