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master's thesis
Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R
diplomski rad
Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2023. urn:nbn:hr:169:086466
Rukavina, Dominik
University of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
Department of Geophysical Exploration and Mine Surveying

Institutional repository: Faculty of Mining, Geology and Petroleum Engineering Repository, University of Zagreb

Cite this document

Rukavina, D. (2023). Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad (Master's thesis). Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:169:086466

Rukavina, Dominik. "Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2023. https://urn.nsk.hr/urn:nbn:hr:169:086466

Rukavina, Dominik. "Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad." Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, 2023. https://urn.nsk.hr/urn:nbn:hr:169:086466

Rukavina, D. (2023). 'Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad', Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, accessed 09 October 2024, https://urn.nsk.hr/urn:nbn:hr:169:086466

Rukavina D. Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering; 2023 [cited 2024 October 09] Available at: https://urn.nsk.hr/urn:nbn:hr:169:086466

D. Rukavina, "Upotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad", Master's thesis, University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:169:086466

Metadata
TitleUpotreba i usporedba metoda nenadzirane i nadzirane klasifikacije SENTINEL-2 satelitskih snimki na području Grada Zagreba u programskom jeziku R : diplomski rad
Title (english)Use and comparison of unsupervised and supervised classification methods of SENTINEL-2 satellite images of Zagreb City area in R programming language : master's thesis
AuthorDominik Rukavina
MentorIvan Medved (mentor)
Committee memberIvan Medved (predsjednik povjerenstva)
Committee memberJasna Orešković (član povjerenstva)
Committee memberTomislav Korman (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
(Department of Geophysical Exploration and Mine Surveying)
Zagreb
Defense date and country2023-02-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mining, Petroleum and Geology Engineering
Geological Engineering
Abstract
Klasifikacija SENTINEL-2 satelitske snimke na području Grada Zagreba pomoću metoda nenadziranog i nadziranog strojnog učenja. Prikupljanje ulaznih podataka rađeno je u softveru QGIS. Izgradnja modela nenadzirane i nadzirane klasifikacije provedena je u R-studio-u. Usporedba točnosti predikcije korištenih modela random forest i extreme gradient boost. Vizualni pregled dobivenih rezultata stvorenih u programskom jeziku R.
Abstract (english)
Classification of SENTINEL-2 satellite images of Zagreb City area using the methods of unsupervised and supervised machine learning. Input data collection was done in QGIS software. Construction of unsupervised and supervised classification models was performed in R-studio. Comparison of prediction accuracy of used random forest and extreme gradient boost models. Visual overview of the results created in R programming language.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:169:086466
Study programmeTitle: Geological engineering; specializations in: Hydrogeology and engineering geology, Environmental geology
Course: Hydrogeology and engineering geology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka geološkog inženjerstva (magistar/magistra inženjer/inženjerka geološkog inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-08-08 07:43:04