This Project researches techniques and algorithms for the detection, recognition and analysis of low-level and high-level features from mobile mapping data along urban and rural transport corridors.
Mobile mapping systems use integrated vehicle-borne positioning, imaging and laser scanning sensors to record data that cannot be readily acquired from aerial and satellite platforms. Examples include vertical building facades and street furniture. Low-level features include edges, corners, planar surfaces, and high-level features include buildings, road signs and the attributes of other man-made and natural features within the transport corridors.
The research uses fused imagery, video, LiDAR, GNSS and sensor orientation data recorded by mobile mapping platforms to recognise, locate, map and measure the spatial attributes and characteristics of features of interest.
Automatic feature extraction for spatial information product generation is essential for organisations such as local government authorities and other government agencies, as well as for 43pl companies, to more efficiently monitor and manage transport corridor infrastructure and associated assets such as power lines, roads and railways, and street-side furniture.
The implementation of low-level feature extraction techniques from 2D and 3D point clouds has now been completed.
An interactive query technique to allow for navigation of 3D image data and low-level feature extraction from specific regions of 3D image data has been developed.
The graphical user interface (GUI) developed for feature recognition of user chosen high-level features in 3D images has been successfully used for ground truthing by volunteers from the Autism Research Programme.
A total of 23 conference and journal papers have been prepared throughout this project. This quarter two peer-reviewed papers were presented at the ISPRS Workshop on Laser Scanning in Turkey. A paper on recognition methods was presented at DICTA 2013 in Hobart and a journal article “Robust Statistical Approaches for Local Planar Surface Fitting in 3D Laser Scanning Data” was accepted, subject to revisions in the ISPRS Journal of Photogrammetry and Remote Sensing.