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 Department of Spatial Sciences at Curtin University has bought its own mobile mapping laser scanner and image capture system, so the team can acquire their own datasets. The system can capture 3D point clouds and co-registered 2D images and allows the team to explore workflow issues around generating accurate datasets quickly and extracting useful features from the data.
PhD candidate Abdul Nurunnabi has developed an algorithm that is improving the workflow of processing point cloud data. The algorithm merges several pieces of segmented slices, reducing the need to manually process a large point cloud data, slice by slice. For each slide, he extracts various features and then merges them across the slices to generate consistent results along long transport corridors. His work is generating great interest. He presented a paper to the Robotic Vision conference in Canada and a seminar in Calgary, and hopes to present his proposed surface extraction algorithm at the ISPRS ‘Laser Scanning Conference’ in Turkey in November.
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