Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images
Hasan Nasrallah, Abed Ellatif Samhat, Yilei Shi, Xiaoxiang Zhu, Ghaleb, Faour, Ali J. Ghandour

TL;DR
This study maps Lebanon's solar rooftop potential using deep learning for building segmentation from satellite images, revealing significant potential for energy security and policy planning.
Contribution
It introduces the first comprehensive rooftop footprint and solar potential maps for Lebanon using advanced deep learning techniques.
Findings
Lebanon's total solar rooftop potential is about 28.1 TWh/year.
A small roof surface can fulfill a single-family residence's yearly energy needs.
Certain districts like Baalbeck and Beirut have notably high potential.
Abstract
Estimating solar rooftop potential at a national level is a fundamental building block for every country to utilize solar power efficiently. Solar rooftop potential assessment relies on several features such as building geometry, location, and surrounding facilities. Hence, national-level approximations that do not take these factors into deep consideration are often inaccurate. This paper introduces Lebanon's first comprehensive footprint and solar rooftop potential maps using deep learning-based instance segmentation to extract buildings' footprints from satellite images. A photovoltaic panels placement algorithm that considers the morphology of each roof is proposed. We show that the average rooftop's solar potential can fulfill the yearly electric needs of a single-family residence while using only 5% of the roof surface. The usage of 50% of a residential apartment rooftop area…
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Taxonomy
TopicsSolar Radiation and Photovoltaics · Impact of Light on Environment and Health
MethodsCutMix
