LIDAR Automatic Feature Detection and Object Classification

点击翻译按钮获取中文摘要

LiDAR (LIght Detection And Ranging) is a relatively new technology for scanning the earth's surface quickly and accurately. The resulting geometry models can be applied to crucial applications in many fields, including urban planning, management, and visualization; security; updating and maintaining cadastral (property line) data; landscape change detection; virtual reality; and operations mission planning. In most relevant applications, the delineation and identification of geospatial features (herein referred to simply as features), or objects such as buildings, vegetation, roadways and waterways, are of primary interest. Since traditional manual extraction methods are costly and time consuming, and large quantities of LiDAR data are being gathered from different sources, there is a growing demand for automatic LiDAR feature extraction [Cary 2009]. However, it must be noted that the scale of LiDAR datasets (potentially billions of points) found in industrial settings introduces formidable performance challenges, beyond the scope of even the most elegant published research techniques. Object Raku Technology, an industry leader in applied geospatial and 3D visualization technologies, has been working on the feature extraction problem since 2004. This project will create a research team that unites Object Raku's development expertise with skills of the Graphics Usability and Visualization (GrUVi) Lab, School of Computing Science, Simon Fraser University, to develop effective and efficient algorhithms for LiDAR data processing and analysis utilizing global geometric information and domain knowledge for rapid feature extraction. All parties will enjoy both short and long-term benefits as a result of this planned five year project. Canadian-based Object Raku has plans for immediate product releases followed by additional permanent support staff. Dr. Zhang's GrUVi lab at SFU will gain valuable expertise in this growing research area. Also, this project will contribute to Canada's strength in the geospatial processing industry.