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dc.contributor.authorChen, Chien-Wen
dc.contributor.authorKumar, Pavan
dc.contributor.authorHsieh, Shang-Hsien
dc.contributor.authorPal, Aritra
dc.contributor.authorChang, Yun-Tsui
dc.contributor.authorWu, Chen-Hung
dc.date.accessioned2024-04-02T15:47:46Z
dc.date.available2024-04-02T15:47:46Z
dc.date.issued2023
dc.identifierONIX_20240402_9791221502893_110
dc.identifier.issn2704-5846
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/89141
dc.description.abstractAs 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
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.subject.classificationthema EDItEUR::U Computing and Information Technology
dc.subject.otherSustainable campus
dc.subject.otherrenewable energy
dc.subject.otherpoint cloud segmentation
dc.subject.otherdeep learning
dc.titleChapter Building Rooftop Analysis for Solar Panel Installation Through Point Cloud Classification - A Case Study of National Taiwan University
dc.typechapter
oapen.identifier.doi10.36253/979-12-215-0289-3.104
oapen.relation.isPublishedBybf65d21a-78e5-4ba2-983a-dbfa90962870
oapen.relation.isbn9791221502893
oapen.series.number137
oapen.pages7
oapen.place.publicationFlorence


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