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- Publisher Website: 10.1109/ICSPCC46631.2019.8960769
- Scopus: eid_2-s2.0-85078874700
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Conference Paper: Semantic Enrichment for Rooftop Modeling using Aerial LiDAR Reflectance
Title | Semantic Enrichment for Rooftop Modeling using Aerial LiDAR Reflectance |
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Authors | |
Keywords | Rooftop building information model city information model LiDAR reflectance decision tree |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800540 |
Citation | 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Dalian, China, 20-22 September 2019, p. 1-4 How to Cite? |
Abstract | As demanded by smart city applications, the recognition and enrichment of urban semantics from unstructured spatial big data became an emerging trend for the development of building information model (BIM) and city information model (CIM). Rooftop constructs the essential part of BIM and CIM and loads various new application practices and scenarios. The recognition and enrichment of rooftop elements represent the trending requirements. This study develops a new approach for semantic enrichment of aerial Light Detection and Ranging (LiDAR) point clouds. In this paper, machine learning models such as decision tree are applied to predict green roof elements based on the geometry and laser reflectance, and was validated in a pilot zone in the main campus of The University of Hong Kong. The recognized rooftop elements could provide a solid foundation for further research, such as rooftop landscape, rooftop energy, rooftop farming. |
Persistent Identifier | http://hdl.handle.net/10722/283347 |
DC Field | Value | Language |
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dc.contributor.author | Tan, T | - |
dc.contributor.author | Chen, K | - |
dc.contributor.author | Xue, F | - |
dc.contributor.author | Lu, WW | - |
dc.date.accessioned | 2020-06-22T02:55:18Z | - |
dc.date.available | 2020-06-22T02:55:18Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Dalian, China, 20-22 September 2019, p. 1-4 | - |
dc.identifier.uri | http://hdl.handle.net/10722/283347 | - |
dc.description.abstract | As demanded by smart city applications, the recognition and enrichment of urban semantics from unstructured spatial big data became an emerging trend for the development of building information model (BIM) and city information model (CIM). Rooftop constructs the essential part of BIM and CIM and loads various new application practices and scenarios. The recognition and enrichment of rooftop elements represent the trending requirements. This study develops a new approach for semantic enrichment of aerial Light Detection and Ranging (LiDAR) point clouds. In this paper, machine learning models such as decision tree are applied to predict green roof elements based on the geometry and laser reflectance, and was validated in a pilot zone in the main campus of The University of Hong Kong. The recognized rooftop elements could provide a solid foundation for further research, such as rooftop landscape, rooftop energy, rooftop farming. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800540 | - |
dc.relation.ispartof | IEEE International Conference on Signal Processing, Communications and Computing Proceedings | - |
dc.rights | IEEE International Conference on Signal Processing, Communications and Computing Proceedings. Copyright © IEEE. | - |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Rooftop | - |
dc.subject | building information model | - |
dc.subject | city information model | - |
dc.subject | LiDAR reflectance | - |
dc.subject | decision tree | - |
dc.title | Semantic Enrichment for Rooftop Modeling using Aerial LiDAR Reflectance | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Xue, F: xuef@hku.hk | - |
dc.identifier.email | Lu, WW: wilsonlu@hku.hk | - |
dc.identifier.authority | Xue, F=rp02189 | - |
dc.identifier.authority | Lu, WW=rp01362 | - |
dc.identifier.doi | 10.1109/ICSPCC46631.2019.8960769 | - |
dc.identifier.scopus | eid_2-s2.0-85078874700 | - |
dc.identifier.hkuros | 310565 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 4 | - |
dc.publisher.place | United States | - |