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Title:Napovedovanje geoprostorskih rastrskih podatkov s konvolucijskimi nevronskimi mrežami : diplomsko delo
Authors:Žalik, Mitja (Author)
Lukač, Niko (Mentor) More about this mentor... New window
Kohek, Štefan (Co-mentor)
Files:.pdf UN_Zalik_Mitja_2020.pdf (3,14 MB)
MD5: F08F852244161909E9AD4FAF970782F4
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu predstavimo uporabo konvolucijskih nevronskih mrež za napoved geoprostorskih rastrskih podatkov. V prvem delu opišemo geoprostorske podatke in zgradbo ter značilnosti konvolucijskih nevronskih mrež. V drugem delu predlagamo model nevronske mreže, ki ga uporabimo za dolgoročno napoved sončnega potenciala in kratkoročno napoved vegetacijskega indeksa NDVI. Povprečna napaka po metriki NRMSE znaša 0,22% pri napovedi sončnega potenciala in 15% pri napovedi indeksa NDVI. Diplomsko delo zaključimo s predlogi možnih razširitev.
Keywords:umetna inteligenca, globoko učenje, konvolucijske nevronske mreže, geoprostorski podatki, rastrski podatki
Year of publishing:2020
Place of performance:Maribor
Publisher:[M. Žalik]
Number of pages:XIII, 37 f.
COBISS_ID:40918019 New window
Categories:KTFMB - FERI
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License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:24.08.2020

Secondary language

Title:Prediction of geospatial raster data using convolutional neural networks
Abstract:In this thesis, the use of convolutional neural networks for predicting geospatial raster data is presented. In the first part, geospatial data are described. Then, the structure and characteristics of convolutional neural networks are explained. In the second part, we propose neural network model. It is used for long-term prediction of a solar potential and short-term prediction of normalized difference vegetation index (NDVI). The results are then evaluated. The Solar potential and NDVI index are predicted with average error 0.22% and 15% respectively, according to the NRMSE metric. The thesis is concluded with some suggestions for further enhancements.
Keywords:artificial intelligence, deep learning, convolutional neural network, geospatial data, raster data


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