BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS

Green energy is increasingly used due to the lack of traditional resources and the increase in environmental pollution, which badly affects our planet in all aspects of life including air, plant life, seas, oceans, etc. In this context, buildings’ rooftops extraction approach for photovoltaic (PV) p...

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Main Authors: E. Muhammed, S. Morsy, A. El-Shazly
Format: Article
Language:English
Published: Copernicus Publications 2021-08-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-3-2021/119/2021/isprs-archives-XLIV-M-3-2021-119-2021.pdf
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spelling doaj-2893066b04ec43aa83ae68f7243a875a2021-08-11T00:42:18ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-08-01XLIV-M-3-202111912510.5194/isprs-archives-XLIV-M-3-2021-119-2021BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODSE. Muhammed0S. Morsy1A. El-Shazly2Public Works Department, Faculty of Engineering, Cairo University, El Gamaa Street, Giza, EgyptPublic Works Department, Faculty of Engineering, Cairo University, El Gamaa Street, Giza, EgyptPublic Works Department, Faculty of Engineering, Cairo University, El Gamaa Street, Giza, EgyptGreen energy is increasingly used due to the lack of traditional resources and the increase in environmental pollution, which badly affects our planet in all aspects of life including air, plant life, seas, oceans, etc. In this context, buildings’ rooftops extraction approach for photovoltaic (PV) potential estimation is presented into two main phases. First, rooftops detection from satellite images using image pre-processing techniques and a machine learning algorithm. The pre-processing steps include gamma correction, shadow, vegetation masking, kmeans, and connected components. Support Vector Machine (SVM) algorithm is then applied to extract rooftops. Second, using two GIS-based methods, PVGIS and Solar Analyst Tool in ArcGIS, for PV estimation. Satellite images for a part of Madinaty city in Egypt were used to evaluate our approach. The accuracy assessment of SVM expressed by the precision and recall were 95.7% and 90%, respectively. The identifiable rooftops in the image were 112 rooftops with a total area of 26,131&thinsp;m<sup>2</sup>. The annual PV potential area was estimated to be 9.3 and 8.7 MWh/year using PVGIS and Solar Analyst Tool, respectively. PVGIS was more accurate as it uses more recent data from solar databases that exist in Africa. On the other hand, Solar Analyst Tool was less accurate as it depends on a digital elevation model with a resolution of 30&thinsp;m. According to our calculations, the electric energy and the amount of CO<sub>2</sub> emission were compensated by an annual average value of 48% for using solar panels instead of the traditional sources of energy.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-3-2021/119/2021/isprs-archives-XLIV-M-3-2021-119-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author E. Muhammed
S. Morsy
A. El-Shazly
spellingShingle E. Muhammed
S. Morsy
A. El-Shazly
BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet E. Muhammed
S. Morsy
A. El-Shazly
author_sort E. Muhammed
title BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
title_short BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
title_full BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
title_fullStr BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
title_full_unstemmed BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
title_sort building rooftops extraction for solar pv potential estimation using gis-based methods
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2021-08-01
description Green energy is increasingly used due to the lack of traditional resources and the increase in environmental pollution, which badly affects our planet in all aspects of life including air, plant life, seas, oceans, etc. In this context, buildings’ rooftops extraction approach for photovoltaic (PV) potential estimation is presented into two main phases. First, rooftops detection from satellite images using image pre-processing techniques and a machine learning algorithm. The pre-processing steps include gamma correction, shadow, vegetation masking, kmeans, and connected components. Support Vector Machine (SVM) algorithm is then applied to extract rooftops. Second, using two GIS-based methods, PVGIS and Solar Analyst Tool in ArcGIS, for PV estimation. Satellite images for a part of Madinaty city in Egypt were used to evaluate our approach. The accuracy assessment of SVM expressed by the precision and recall were 95.7% and 90%, respectively. The identifiable rooftops in the image were 112 rooftops with a total area of 26,131&thinsp;m<sup>2</sup>. The annual PV potential area was estimated to be 9.3 and 8.7 MWh/year using PVGIS and Solar Analyst Tool, respectively. PVGIS was more accurate as it uses more recent data from solar databases that exist in Africa. On the other hand, Solar Analyst Tool was less accurate as it depends on a digital elevation model with a resolution of 30&thinsp;m. According to our calculations, the electric energy and the amount of CO<sub>2</sub> emission were compensated by an annual average value of 48% for using solar panels instead of the traditional sources of energy.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-3-2021/119/2021/isprs-archives-XLIV-M-3-2021-119-2021.pdf
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