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Title:Razpoznava vinogradov iz podatkov LiDAR
Authors:ID Šprajc, Žan Tomaž (Author)
ID Jesenko, David (Mentor) More about this mentor... New window
ID Bizjak, Marko (Comentor)
Files:.pdf MAG_Sprajc_Zan_Tomaz_2025.pdf (2,38 MB)
MD5: 7066DFE2A10E9252C359105DC2320189
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Tehnologija LiDAR človeku omogoča nove načine odkrivanja svojega okolja, v katerem se skrivajo arheološki ostanki naših prednikov in namigi k optimizaciji vsakdanjika. V magistrskem delu smo raziskovali pot, ki je vodila do daljinskega zaznanja, in rešitve, ki so omogočile njeno raziskovanje. Algoritme iskanja po drevesih in gručanja smo vgradili v tri rešitve, ki so iz podatkov LiDAR, zajetih z letalnikom in letalom, poskušale razpoznati linije vinske trte. Zagoni rešitev so temeljili na cevovodu, ki je izvorne .laz datoteke vodil do ciljnih oblik. Izplene zagonov smo predstavili v okviru njihove statistične analize, s katero smo izpostavili prednosti in slabosti posamičnega pristopa.
Keywords:LiDAR, DBSCAN, RANSAC, vinogradništvo, daljinsko zaznavanje
Place of publishing:Maribor
Publisher:[Ž. T. Šprajc]
Year of publishing:2025
PID:20.500.12556/DKUM-94284 New window
UDC:004.932:528.8.044.6(043.2)
COBISS.SI-ID:254402563 New window
Publication date in DKUM:04.09.2025
Views:226
Downloads:70
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:13.08.2025

Secondary language

Language:English
Title:Identifying vineyards from LiDAR data
Abstract:LiDAR technology has given humanity new ways to explore its environment. The latter boasts the archaeological remnants of our ancestors and clues for optimizing everyday processes. Our work involved exploring the path that led to remote sensing and the solutions that enabled its development. Our three implemented solutions were comprised of search tree and grouping algorithms, whose LiDAR input data was derived from drone and plane flights. The implemented solutions, which were run through a pipeline that transformed the original .laz files into their target forms, attempted to detect grapevine rows. The results of said processes were presented in light of their statistical analysis, which featured the pros and cons of each solution.
Keywords:LiDAR, DBSCAN, RANSAC, viticulture, remote sensing


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