20.500.12556/RUL-140081 Prepoznava poplavnih območij iz satelitskih posnetkov Sentinel-2 z modeli konvolucijskih nevronskih mrež magistrsko delo št.: 155/II. GIG Detection of flooded areas from satellite imagery Sentinel-2 with convolutional neural network models master thesis no.: 155/II. GIG Konvolucijske nevronske mreže so v zadnjem desetletju v zelo velikem razvoju in se uporabljajo na skoraj vseh področjih znanosti. Pri nalogah prepoznave poplavnih območij se v veliki meri uporabljajo nevronske mreže, ki zagotavljajo avtomatizirano prepoznavo le teh z zanesljivimi rezultati. Ti rezultati so pomembni za ocenjevanje škode in pri načrtovanju obnove poplavnega območja. V raziskavi sem uporabil dva modela konvolucijske nevronske mreže, in sicer MobileNetV2 in dve stopnji EfficientNet. Za učenje modela sem uporabil podatke satelita Sentinel-2. Za ločevanje poplavljenega in nepopravljenega območja sem izdelal lastno zbirko oznak. Izdelali smo program, ki se uporablja za predobdelavo podatkov in učenje modelov. Uporabljene modele sem testiral s spreminjanjem hiperparametrov. Prav tako sem izvedel test spreminjanja po nivoju produkta in izbiro kanalov satelita Sentinel-2. V tretjem delu testiranj sem izboljšal rezultat zgolj z bogatenjem količine podatkov. Po vsakem testiranju sem podatke analiziral in pridobil optimiziran model, kot rezultat, ki je sposoben uspešno prepoznati poplavno območje. V izbranem GMS-GIS-u sem uporabil izdelano metodo in jo preizkusil na novih podatkih. Convolutional neural networks have developed a lot over the past decade and are used in almost all fields of science. Neural networks are already used to a large extent for detecting flooded area and provide automated detection with reliable results. These are important for assessing damage and planning the reconstruction of the flood areas. In the study, I used two models of the convolutional neural network MobileNetV2 and two stages of EfficientNet. For learning models, I used Sentinel-2 satellite data. To separate the flooded and not flooded areas, I created my own collection of annotations. We have developed a program that we use to pre-process data and learn models. The models used were tested by changing the hyperparameters. I also performed a product-level change test and a selection of Sentinel-2 satellite channels. In the third part of the tests, I improved the results only by enriching the amount of data. After each test, I analyzed the data and obtained an optimized model as a result that can successfully detect the flooded area. In the selected GMS-GIS, I used the developed method and tested it on new data. geodezija magistrska dela GIG strojno učenje semantična segmentacija konvolucijska nevronska mreža hiperparametri binarna klasifikacija MobileNetV2 EfficientNet Sentinel-2 poplave geodesy master thesis machine learning semantic segmentation convolutional neural network hyperparameters binary classification MobileNetV2 EfficientNet Sentinel-2 floods true false false [P. Skledar] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2022-09-10 08:45:17 2022-09-10 08:45:21 2025-08-08 13:21:24 0000-00-00 00:00:00 2022 0 Ljubljana 2022 Ljubljana XI, 74 str. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 528.7:556.531/.532(043.3) 150627 125106435 8532.pdf 8532.pdf 1 04A63D0F16F74E1B2FDE78BB97E7CA7F 508c7607da0a071bcfab89648b517547786b2e308639f1da256a29328d61ed1d 15391f6b-30d4-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=160747 Fakulteta za gradbeništvo in geodezijo 0 0 0