| id |
ascaad2023_125 |
| authors |
Shata, Dina; Omrani, Sara; Drogemuller, Robin; Denman, Simon; Wagdy, Ayman |
| year |
2023 |
| title |
Segmented Rooftop Dataset Generation: A Simplified Approach for Harnessing Solar Power Potential Using Aerial Imagery and Point Cloud Data |
| source |
C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 134-153. |
| summary |
With rising global energy demands and climate change concerns, solar energy has gained traction as a sustainable source. However, optimal utilization of solar systems relies on accurately determining rooftop solar potential. This research presents a simplified methodology to generate a comprehensive dataset of segmented rooftops using publicly available aerial imagery and light detection and ranging (LiDAR) point cloud data. The primary objective is to enable precise prediction of solar photovoltaic (PV) capacity on residential rooftops by extracting key geometric features. The proposed approach first preprocesses raw LiDAR data to isolate building points and generates 3D mesh models of rooftops. A mesh analysis technique computes surface normal and tilt angles, stored as RGB images. Masks derived from the 3D meshes are combined with high-resolution aerial photos to extract cropped rooftop image segments. This overcomes the limitations of manually labelling imagery or relying on scarce 3D city models. The resulting dataset provides critical training and validation inputs for developing machine learning models to assess rooftop solar potential. An initial sample dataset of over 1100 residential rooftops in Brisbane, Australia was created to demonstrate the methodology's effectiveness. The workflow is structured, scalable and replicable, facilitating expansion across larger regions to generate big datasets encompassing diverse rooftop configurations. Overall, this research presents an efficient automated solution to harness essential dataset for training Deep Learning models. It holds significant potential to drive solar PV prediction, enabling the optimization of renewable energy systems and progressing sustainability goals. |
| series |
ASCAAD |
| email |
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| full text |
 file.pdf (1,545,239 bytes) |
| references |
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| last changed |
2024/02/13 14:41 |
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