Rutuja Sambhaji Sandbhor ,Dr. Pallavi Adke,Akshada Kharade ,Chaitanya Sonawane

This research paper investigates the application of Deep Learning, specifically employing the DeepLabV3 architecture, for Semantic Segmentation in identifying Rooftop Photovoltaic (PV) Panels from both Satellite and Aerial Images. The primary objective is to compute solar panel efficiency through precise identification and spatial analysis of PV panels. Leveraging DeepLabV3's pixel-level segmentation, this study provides detailed insights that enable a comprehensive assessment of solar panel performance. The proposed methodology involves the utilization of DeepLabV3 on satellite and aerial images, with a focus on classifying pixels corresponding to solar panels and deriving efficiency metrics. The research outcomes aim to enhance solar energy utilization and facilitate informed decision-making in the renewable energy sector. The experimental results indicate that the recog

https://doi.org/10.58357/jedcs.2024.v3.i3.1