Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University
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 s...
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Firenze University Press
2024
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20240402_9791221502893_110 |
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| _version_ | 1869523144696397824 |
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| author | Chen, Chien-Wen Kumar, Pavan Hsieh, Shang-Hsien Pal, Aritra Chang, Yun-Tsui Wu, Chen-Hung |
| author_browse | Chang, Yun-Tsui Chen, Chien-Wen Hsieh, Shang-Hsien Kumar, Pavan Pal, Aritra Wu, Chen-Hung |
| author_facet | Chen, Chien-Wen Kumar, Pavan Hsieh, Shang-Hsien Pal, Aritra Chang, Yun-Tsui Wu, Chen-Hung |
| author_sort | Chen, Chien-Wen |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-136789 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1367892024-05-08T19:06:25Z Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University Chen, Chien-Wen Kumar, Pavan Hsieh, Shang-Hsien Pal, Aritra Chang, Yun-Tsui Wu, Chen-Hung Sustainable campus renewable energy point cloud segmentation deep learning thema EDItEUR::U Computing and Information Technology 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 2024-05-08T19:06:22Z 2024-05-08T19:06:22Z 2024-04-02T15:47:46Z 2023 chapter ONIX_20240402_9791221502893_110 2704-5846 https://library.oapen.org/handle/20.500.12657/89141 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136789 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89141/1/9791221502893_104.pdf Firenze University Press 10.36253/979-12-215-0289-3.104 10.36253/979-12-215-0289-3.104 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 7 Florence open access |
| spellingShingle | Sustainable campus renewable energy point cloud segmentation deep learning thema EDItEUR::U Computing and Information Technology Chen, Chien-Wen Kumar, Pavan Hsieh, Shang-Hsien Pal, Aritra Chang, Yun-Tsui Wu, Chen-Hung Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title | Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title_full | Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title_fullStr | Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title_full_unstemmed | Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title_short | Chapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University |
| title_sort | chapter building rooftop analysis for solar panel installation through point cloud classification a case study of national taiwan university |
| topic | Sustainable campus renewable energy point cloud segmentation deep learning thema EDItEUR::U Computing and Information Technology |
| topic_facet | Sustainable campus renewable energy point cloud segmentation deep learning thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_110 |
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