Semantic Enrichment for Rooftop Modeling using Aerial LiDAR Reflectance

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.

[1]  Pablo J. Rosado,et al.  Reflectometer measurement of roofing aggregate albedo , 2014 .

[2]  F. Orsini,et al.  Exploring the production capacity of rooftop gardens (RTGs) in urban agriculture: the potential impact on food and nutrition security, biodiversity and other ecosystem services in the city of Bologna , 2014, Food Security.

[3]  Weisheng Lu,et al.  A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  Dong Chen,et al.  Topologically Aware Building Rooftop Reconstruction From Airborne Laser Scanning Point Clouds , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Ke Chen,et al.  Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge , 2018, Automation in Construction.

[6]  Yi-Hsing Tseng,et al.  EXTRACTION OF BUILDING BOUNDARY LINES FROM AIRBORNELIDAR POINT CLOUDS , 2016 .

[7]  Syed Ali Naqi Gilani,et al.  Segmentation of Airborne Point Cloud Data for Automatic Building Roof Extraction , 2018 .

[8]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[9]  Yi Sun,et al.  Automated segmentation of LiDAR point clouds for building rooftop extraction , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[10]  Manchun Li,et al.  Dynamic triangle — Based method for 3D building rooftop reconstruction from LiDAR data , 2011, 2011 19th International Conference on Geoinformatics.

[11]  Ke Chen,et al.  Automatic Generation of Semantically Rich As‐Built Building Information Models Using 2D Images: A Derivative‐Free Optimization Approach , 2018, Comput. Aided Civ. Infrastructure Eng..

[12]  J. Shan,et al.  A global optimization approach to roof segmentation from airborne lidar point clouds , 2014 .