Contained in:
Book Chapter

Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University

  • Aritra Pal
  • Yun-Tsui Chang
  • Chien-Wen Chen
  • Chen-Hung Wu
  • Pavan Kumar
  • Shang-Hsien Hsieh

As climate change intensifies, we must embrace renewable solutions like solar energy to combat greenhouse gas emissions. Harnessing the sun's power, solar energy provides a limitless and eco-friendly source of electricity, reducing our reliance on fossil fuels. Rooftops offer prime real estate for solar panel installation, optimizing sun exposure, and maximizing clean energy generation at the point of use. For installing solar panels, inspecting the suitability of building rooftops is essential because faulty roof structures or obstructions can cause a significant reduction in power generation. Computer vision-based methods proved helpful in such inspections in large urban areas. However, previous studies mainly focused on image-based checking, which limits their usability in 3D applications such as roof slope inspection and building height determination required for proper solar panel installation. This study proposes a GIS-integrated urban point cloud segmentation method to overcome these challenges. Specifically, given a point cloud of a metropolitan area, first, it is localized in the GIS map. Then a deep-learning-based point cloud classification model is trained to detect buildings and rooftops. Finally, a rule-based checking determines the building height, roof slopes, and their appropriateness for solar panel installation. While testing at the National Taiwan University campus, the proposed method demonstrates its efficacy in assessing urban rooftops for solar panel installation

  • Keywords:
  • Sustainable campus,
  • renewable energy,
  • point cloud segmentation,
  • deep learning,
+ Show More

Aritra Pal

National Taiwan University, Taiwan (Province of China) - ORCID: 0000-0002-1644-7400

Yun-Tsui Chang

National Taiwan University, Taiwan (Province of China) - ORCID: 0000-0003-1515-4187

Chien-Wen Chen

National Taiwan University, Taiwan (Province of China)

Chen-Hung Wu

National Taiwan University, Taiwan (Province of China) - ORCID: 0000-0003-3997-1907

Pavan Kumar

National Taiwan University, Taiwan (Province of China)

Shang-Hsien Hsieh

National Taiwan University, Taiwan (Province of China)

  1. Chen, C.C., Chang, Y.T. & Hsieh, S.H. (2023). A Digital Twin Platform Based on 3D Building Models and Smart IoT for A Climate-Resilient Campus: A Case Study of National Taiwan University. 2023 ASCE International Conference on Computing in Civil Engineering (i3CE 2023).
  2. Feng, H., Chen, Y., Luo, Z., Sun, W., Li, W., & Li, J. (2022). Automated Extraction of Building Instances from Dual-channel Airborne LiDAR Point Clouds. International Journal of Applied Earth Observation and Geoinformation, 114, 103042.
  3. Huang, J., Stoter, J., Peters, R., & Nan, L. (2022). City3D: Large-scale Building Reconstruction from Airborne LiDAR Point Clouds. Remote Sensing, 14(9), 2254.
  4. IPCC (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  5. Lin, P.H., Chen, C.C., Chang, Y.T. & Hsieh, S.H. (2022). Identifying Obstacles For Solar Panel Installation on Building Rooftops Utilizing Satellite Imagery and Computer Vision Models - A Case Study of National Taiwan University. Proceedings of the 22nd International Conference on Construction Applications of Virtual Reality (CONVR2022), November 16-18, 2022, Seoul, South Korea, 9-15.
  6. Sierra, E.M., Gupta, B., Chang, Y.T. & Hsieh, S.H. (2022). Parametric Design of Solar Parking Lot Layout with Evolutionary Optimization - A Case Study of National Taiwan University. Proceedings of the 22nd International Conference on Construction Applications of Virtual Reality (CONVR2022), November 16-18, 2022, Seoul, South Korea, 562-568.
  7. THE (2022). Impact Rankings Methodology 2022. Version 1.3. Times Higher Education (THE).
  8. Wang, R., Peethambaran, J., & Chen, D. (2018). Lidar Point Clouds to 3-D Urban Models: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 606-627.
  9. Dawood, N., Dawood, H., Rodriguez-Trejo, S., & Crilly, M. (2017). Visualising Urban Energy Use: the Use of LiDAR and Remote Sensing Data in Urban Energy Planning. Visualization in Engineering, 5(1), 22. DOI: 10.1186/s40327-017-0060-3
  10. Pal, A., & Hsieh, S. H. (2021). Deep-learning-based Visual Data Analytics for Smart Construction Management. Automation in Construction, 131(August), 103892. DOI: 10.1016/j.autcon.2021.103892
  11. Pohle-Fröhlich., R., Bohm., A., Ueberholz., P., Korb., M., & Goebbels., S. (2019). Roof Segmentation based on Deep Neural Networks. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP, 326–333. DOI: 10.5220/0007343803260333
  12. Stack, V., & Narine, L. L. (2022). Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus. Sustainability, 14(2). DOI: 10.3390/su14020626
  13. Sun, Y., Wang, C., Li, J., Zhang, Z., Zai, D., Huang, P., & Wen, C. (2016). Automated Segmentation of LiDAR Point Clouds for Building Rooftop Extraction. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1472–1475. DOI: 10.1109/IGARSS.2016.7729376
  14. Tan, T., Chen, K., Lu, W., & Xue, F. (2019). Semantic Enrichment for Rooftop Modeling using Aerial LiDAR Reflectance. Proceedings of the 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1–4. DOI: 10.1109/ICSPCC46631.2019.8960769
  15. Yang, J., Matsushita, B., & Zhang, H. (2023). Improving Building Rooftop Segmentation Accuracy Through the Optimization of UNet Basic Elements and Image Foreground-background Balance. ISPRS Journal of Photogrammetry and Remote Sensing, 201, 123–137. DOI: 10.1016/j.isprsjprs.2023.05.013
PDF
  • Publication Year: 2023
  • Pages: 1042-1048

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University

Authors

Aritra Pal, Yun-Tsui Chang, Chien-Wen Chen, Chen-Hung Wu, Pavan Kumar, Shang-Hsien Hsieh

DOI

10.36253/979-12-215-0289-3.104

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

113

Fulltext
downloads

72

Views

Export Citation

1,331

Open Access Books

in the Catalogue

2,094

Book Chapters

3,709,757

Fulltext
downloads

4,300

Authors

from 904 Research Institutions

of 65 Nations

65

scientific boards

from 347 Research Institutions

of 43 Nations

1,246

Referees

from 379 Research Institutions

of 38 Nations