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master's thesis
Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:256428
Šimac, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šimac, L. (2022). Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:256428

Šimac, Luka. "Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:256428

Šimac, Luka. "Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:256428

Šimac, L. (2022). 'Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:256428

Šimac L. Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 August 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:256428

L. Šimac, "Detekcija klizišta na LiDAR snimkama temeljena na dubokom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:256428

Metadata
TitleDetekcija klizišta na LiDAR snimkama temeljena na dubokom učenju
Title (english)Deep Learning-based Landslide Detection in LiDAR Images
AuthorLuka Šimac
MentorMarko Subašić (mentor)
Committee memberMarko Subašić (predsjednik povjerenstva)
Committee memberJuraj Petrović (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema diplomskog rada je detekcija klizišta na LiDAR snimkama temeljem dubokog učenja. Za rješavanje problema korišteni su algoritmi temeljeni na semantičkoj segmentaciji i dubokim konvolucijskim mrežama. Za kvalitetno rješavanje problema potrebno je bilo provesti predprocesiranje izvornih LiDAR snimki kako bi se mogao izvući maksimalan broj informacija i kreirati adekvatan skupa podatka za treniranje i evuliranje modela te isto tako provesti agumentaciju tokom treniranja modela u svrhu povećanja količine podataka. Istraživanjem dostupnih algoritama semantičke segementacije odlučeno je da su mreže arhitekture enkoder-dekoder pogodne za problem kojim se bavimo. U svrhu dobivanja kvalitetnih rezultata proveden je niz eksperimenata gdje su se ispitivale različite konfiguracije enkoder-dekoder arhitekture u jednakim uvjetima.
Abstract (english)
The topic of the thesis is the detection of landslides on LiDAR images based on deep learning. Algorithms based on semantic segmentation and deep convolutional networks were used to solve the problem. To solve the problem well, it was necessary to pre-process the original LiDAR images in order to extract the maximum amount of information and to create an adequate dataset for training and evaluating models, as well as to apply data augmentation during model training to increase dataset size. By researching the available semantic segmentation algorithms, it was decided that the encoder-decoder architecture networks are suitable for the problem of landslide detection. In order to obtain quality of results, a series of experiments were conducted where different configurations of the encoder-decoder architecture were tested under the same conditions.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:256428
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-14 10:02:46