Land cover classification using machine learning algorithms on GEE, and its relationship with gaseous air pollutants based on remote sensing data

A tétel rövid nézete
dc.contributor.advisorSzabó, Szilárd
dc.contributor.authorGuaman Pintado, Pamela Maricela
dc.contributor.departmentDE--Természettudományi és Technológiai Kar--Földtudományi Intézet
dc.date.accessioned2023-05-05T06:57:12Z
dc.date.available2023-05-05T06:57:12Z
dc.date.created2023-05-04
dc.description.abstractThis study explores the application of machine learning algorithms on Google Earth Engine (GEE) for land cover classification and examines the relationship between different land cover types and gaseous air pollutants (O3, NO2, SO2) using remote sensing data. The research focuses on evaluating the performance of various machine learning techniques, such as Random Forest, Support Vector Machines, and CART, in classifying land cover types.
dc.description.correctorLB
dc.description.courseGeography MSc
dc.description.degreeMSc/MA
dc.format.extent55
dc.identifier.urihttps://hdl.handle.net/2437/351911
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectLand Cover
dc.subjectMachine Learning
dc.subjectGEE
dc.subjectAir pollutantss
dc.subject.dspaceDEENK Témalista::Informatika::Geoinformatika
dc.subject.dspaceDEENK Témalista::Földtudományok::Környezetföldrajz
dc.subject.dspaceDEENK Témalista::Földtudományok::Természeti földrajz
dc.titleLand cover classification using machine learning algorithms on GEE, and its relationship with gaseous air pollutants based on remote sensing data
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Theses (Faculty of Science and Technology)