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Machine Learning Methods for Classification of the Green Infrastructure in City Areas


Kranjčić, Nikola; Medak, Damir; Župan, Robert; Rezo, Milan
Machine Learning Methods for Classification of the Green Infrastructure in City Areas // ISPRS International Journal of Geo-Information, 8 (2019), 463; 1-15 doi:10.3390/ijgi8100463 (međunarodna recenzija, članak, znanstveni)


Naslov
Machine Learning Methods for Classification of the Green Infrastructure in City Areas

Autori
Kranjčić, Nikola ; Medak, Damir ; Župan, Robert ; Rezo, Milan

Izvornik
ISPRS International Journal of Geo-Information (2220-9964) 8 (2019), 463; 1-15

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Green urban infrastructure ; support vector machines ; artificial neural networks ; naïve Bayes classifier ; random forest ; Sentinel 2-MSI

Sažetak
Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation ; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2016-06-5621 - Geoprostorno praćenje zelene infrastrukture na temelju terestričkih, zračnih i satelitskih snimaka (Damir Medak, )

Ustanove
Geodetski fakultet, Zagreb,
Geotehnički fakultet, Varaždin

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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