Seminar Series #1: Machine Learning with Structured (Geo)Spatial Information in Processing Unstructured 3D Point Clouds
-
Date
03 May 2024
-
Organiser
Research Centre for Artificial Intelligence in Geomatics
-
Time
15:00 - 16:00
-
Venue
Z414 Map
Speaker
Prof. Fan XUE (Frank)
Remarks
All are welcome! Please register now to join us on-site!
Summary
Large-scale 3D point cloud data sets are increasingly available for next-generation buildings and city management, thanks to the exciting LiDAR and photogrammetry technologies in remote sensing. However, no “silver bullets” exist to resolve unstructured 3D point clouds representing these complex systems. Even the state-of-the-art 3D machine learning (ML) methods can hardly satisfy the classification or regression tasks of the point-level labels or object-level instances. In this talk, we will unveil the possibility of enriching generic ML with structured geospatial information, i.e., ML with (rather than of) geospatial information. First, we will present several general ways to chip structured domain knowledge into the paradigms of ML, including pre-processing, extra features, spatial attention, and post processing. Drawing from our recent work, we will showcase examples of detecting labels, global symmetries, local similarities, and objects in urban point clouds. Our dataset and experiments have demonstrated the unique and vital role of structured geospatial information in applying ML to built environment data sets, paving the way for long-term, innovative, smarter city applications.
Keynote Speaker
Prof. Fan XUE (Frank)
Associate Professor
Department of Real Estate and Construction
The University of Hong Kong
Prof. Fan XUE (Frank) is an Associate Professor in the Department of Real Estate and Construction at The University of Hong Kong. Professor XUE has an interdisciplinary background in Automation (BEng), Computer Science (MSc), Industrial and Systems Engineering (PhD), and Construction Management (Postdoctoral fellow to present). He is member of ACM, senior member of CGS, member of ASC, member of HKGISA, member of ISDE, and member of IEEE. He is Deputy Director of HKUrbanLabs—iLab and Vice Convenor of Hong Kong-Zhuhai-Macao Research Station of the Key Scientific Research Base of Application of Spatial Information Technologies in Cultural Heritage Conservation of National Cultural Heritage Administration of China. He also serves as Vice-Chair of ACM-Hong Kong Chapter, committee of CGS-BIM Chapter, committee of ASC-Smart Construction, and member of Engineering Panel for Competitive Research Funding Schemes for the Local Self-financing Degree Sector (APSF) of Research Grants Council (RGC). His research interests include building and city information modeling, LiDAR data processing, derivative-free optimization, blockchain, and machine learning.
