Classification of Urban Areas Between 1986-2022 with Machine Learning in Atlantico Colombia

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dc.contributor.advisorAbriha, Dávid
dc.contributor.authorPardo Gomez, Alexandra Carolina
dc.contributor.departmentDE--Természettudományi és Technológiai Kar--Földtudományi Intézet
dc.date.accessioned2025-06-20T09:12:01Z
dc.date.available2025-06-20T09:12:01Z
dc.date.created2025
dc.description.abstractMachine Learning algorithms are currently being studied for their applications on image classification procedures, where with the help of training and test data, remote sensed images land covers can be classified. In this study two Machine Learning methods are being tested and evaluated to analyze their performance on urban cover classification: Support Vector Machine and Random Forest. To accomplish it, three images of different time periods (1986, 2000 and 2022) from the Atlantico Department in Colombia were obtained, training and test data generated, and the algorithms were tuned and evaluated. Finally, the results from this procedure was analysed and discussed.
dc.description.courseGeoinformatics
dc.description.degreeMSc/MA
dc.format.extent38
dc.identifier.urihttps://hdl.handle.net/2437/394054
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectRemote Sensing
dc.subjectMachine Learning
dc.subjectLand Classification
dc.subject.dspaceEarth Sciences
dc.titleClassification of Urban Areas Between 1986-2022 with Machine Learning in Atlantico Colombia
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