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040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aM.Sc
099 _aCai01.13.05.M.Sc.2021.Es.B
100 0 _aEslam Mustafa Mahmoud Muhammed
245 1 0 _aBuilding rooftops extraction from satellite images using machine learning algorithms for solar photovoltaic potential estimation /
_cEslam Mustafa Mahmoud Muhammed ; Supervised Adelelshazly , Salem Morsy
246 1 5 _aاستخلاص أسطح المبانى من صور الأقمار الصناعية باستخدام خوارزميات التعلم الآلى لتقدير الطاقة الشمسية الكهروضوئية المحتملة
260 _aCairo :
_bEslam Mustafa Mahmoud Muhammed ,
_c2021
300 _a87 P . :
_bcharts , facsmilies , maps ;
_c30cm
502 _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
520 _aThis research involves the extraction of buildings from satellite images to be used for solar PV potential estimation. Pre-processing algorithms were used to enhance the image of a city in Cairo, Egypt called Madinaty. Then, two machine learning techniques were used to extract the buildings{u2019} rooftops. SVM exceeded Naïve Bayes in terms of the detection accuracy with an F1 score of 94.7%. The detected gross areas of the rooftops were used in the second phase of this thesis, which is the PV potential estimation of PV panels mounted over the detected rooftops. Three methods were used for the solar modeling of the study area which are PVWatts calculator, PVGIS, and ArcGIS. The estimated PV potentials were calculated to be 21, 24.9, and 22.3 GWh/year for the three methods respectively. CO2 was reduced by an average of 62% after using solar panels instead of depending on traditional energy sources
530 _aIssued also as CD
650 0 _aSatellite images
653 _aImage processing
653 _aMachine learning
653 _aRooftops extraction
700 0 _aAdelelshazly ,
_eSupervising
700 0 _aSalem Morsy ,
_eSupervising
856 _uhttp://172.23.153.220/th.pdf
905 _aAmira
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
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_d84316