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master's thesis
Classification of Land Cover and Agricultural Land Using Google Earth Engine
2024. urn:nbn:hr:151:873314
Šumanovac, Luka
Josip Juraj Strossmayer University of Osijek
Faculty of Agrobiotechnical Sciences Osijek
Department of Agricultural Engineering and Renewable Energy Resources

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Šumanovac, L. (2024). Classification of Land Cover and Agricultural Land Using Google Earth Engine (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:151:873314

Šumanovac, Luka. "Classification of Land Cover and Agricultural Land Using Google Earth Engine." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:151:873314

Šumanovac, Luka. "Classification of Land Cover and Agricultural Land Using Google Earth Engine." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:151:873314

Šumanovac, L. (2024). 'Classification of Land Cover and Agricultural Land Using Google Earth Engine', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, accessed 22 January 2025, https://urn.nsk.hr/urn:nbn:hr:151:873314

Šumanovac L. Classification of Land Cover and Agricultural Land Using Google Earth Engine [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek; 2024 [cited 2025 January 22] Available at: https://urn.nsk.hr/urn:nbn:hr:151:873314

L. Šumanovac, "Classification of Land Cover and Agricultural Land Using Google Earth Engine", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:151:873314

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Metadata
TitleClassification of Land Cover and Agricultural Land Using Google Earth Engine
Title (croatian)Klasifikacija zemljišta I poljoprivrednog zemljišta pomoću Google Earth Enginea
AuthorLuka Šumanovac
MentorDorijan Radočaj (mentor)
Committee memberMladen Jurišić (predsjednik povjerenstva)
Committee memberIvan Plaščak (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Agrobiotechnical Sciences Osijek
(Department of Agricultural Engineering and Renewable Energy Resources)
Osijek
Defense date and country2024-06-06, Croatia
Scientific / art field,
discipline and subdiscipline		BIOTECHNICAL SCIENCES
Agronomy
Agricultural Engineering and Technology
Abstract
Research was carried out to classify the land and agricultural land in Osijek-Baranja County. Six classification methods were tested to evaluate the efficiency of these machine learning algorithms, identifying the most efficient and the least efficient algorithms. LULC maps were created in the QGIS software, each containing three classification methods for the years 2021, 2022, and 2023. The first LULC map contained the classification algorithms kNN, CART, and Naïve Bayes, and the second LULC map contained the classification algorithms MD, RF, and libsvm. QGIS proved to be a valuable tool for visualizing the data exported from GEE. The RF was the most efficient classification method after evaluation with 99% Overall Accuracy in each study year, along with other metrics such as Producer’s Accuracy, Consumer Accuracy, and Kappa Coefficient. The least efficient algorithm was Naïve Bayes.
Abstract (english)
Istraživanje je provedeno u svrhu klasifikacije zemljišta i poljoprivrednog zemljišta u Osječko-baranjskoj županiji. Testirano je šest metoda klasifikacije kako bi se procijenila učinkovitost ovih algoritama strojnog učenja, identificirajući najučinkovitije i najmanje učinkovite algoritme. LULC karte izrađene su u softveru QGIS, a svaka sadrži tri metode klasifikacije za godine 2021., 2022. i 2023. Prva LULC karta sadržavala je klasifikacijske algoritme kNN, CART i Naïve Bayes, a druga LULC karta sadržavala je klasifikacijske algoritme MD , RF i libsvm. QGIS se pokazao kao vrijedan alat za vizualizaciju podataka eksportiranih iz GEE-a. RF je bio najučinkovitija metoda klasifikacije nakon evaluacije s 99% ukupne točnosti u svakoj studijskoj godini, zajedno s drugim metrikama kao što su Producer’s Accuracy, Consumer’s Accuracy, i Kappa Coefficient.. Najmanje učinkovit algoritam bio je Naïve Bayes.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:151:873314
Study programmeTitle: Digital Agriculture - graduate – university
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra inženjer/inženjerka digitalne poljoprivrede (sveučilišni/a magistar/magistra inženjer/inženjerka digitalne poljoprivrede)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Public notediplomski rad
Created on2024-12-04 08:49:52