BUILDING ROOFTOPS EXTRACTION FOR SOLAR PV POTENTIAL ESTIMATION USING GIS-BASED METHODS
Keywords: Rooftops Extraction, SVM, Image Processing, Solar PV Potential, Green Energy
Abstract. 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 m2. 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 m. According to our calculations, the electric energy and the amount of CO2 emission were compensated by an annual average value of 48% for using solar panels instead of the traditional sources of energy.