# Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images

arXiv: https://arxiv.org/abs/2111.11397 · HTML: https://mlsanity.com/paper/2111.11397/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Dual Path Networks](https://mlsanity.com/paper/1707.01629/) (2017)
- [A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes](https://mlsanity.com/paper/1904.05730/) (2019)
- [TernausNetV2: Fully Convolutional Network for Instance Segmentation](https://mlsanity.com/paper/1806.00844/) (2018)
- [Satellite imagery analysis for operational damage assessment in Emergency situations](https://mlsanity.com/paper/1803.00397/) (2018)
- [Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data](https://mlsanity.com/paper/2106.15268/) (2021)

## Built on by (most cited first, 4 of 4)

- [Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation](https://mlsanity.com/paper/2310.01828/) (2023)
- [Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation](https://mlsanity.com/paper/2310.01837/) (2023)
- [Zero-Shot Refinement of Buildings' Segmentation Models using SAM](https://mlsanity.com/paper/2310.01845/) (2023)
- [Entropy-Centric Explainable AI for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2608.11064/) (2026)

## Abstract (arXiv)

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 would achieve energy security for up to 8 households. We also compute the average and total solar rooftop potential per district to localize regions corresponding to the highest and lowest solar rooftop potential yield. Factors such as size, ground coverage ratio and PV_out are carefully investigated for each district. Baalbeck district yielded the highest total solar rooftop potential despite its low built-up area. While, Beirut capital city has the highest average solar rooftop potential due to its extremely populated urban nature. Reported results and analysis reveal solar rooftop potential urban patterns and provides policymakers and key stakeholders with tangible insights. Lebanon's total solar rooftop potential is about 28.1 TWh/year, two times larger than the national energy consumption in 2019.