Feature Extraction Workflows for Urban Mobile-Terrestrial LiDAR Data
McQuat, Gregory John
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Mobile Terrestrial LiDAR (MTL) is an active remote sensing technology that uses laser-based ranging and global positioning systems (GPS) to record 3D point location measurements on surfaces within and near transportation corridors, such as along a railroad track or a street. This thesis examines geovisualization for improving user-oriented workflows and also examines geographic object-based image analysis (GEOBIA) for the development of automated feature extraction. A LiDAR sensor-centric perspective during the data acquisition phase is used to organize data for the user and to transform the data into a 2D reference frame for object-oriented image analysis of MTL data. Organizing the display of MTL data relative to the scanner presented new opportunities for visualization techniques and was an effective method for communicating space that was scanned, or not, in an urban scene. It offers new avenues for quality assessment of MTL survey of urban environments by explicitly displaying gaps in data coverage. A number of techniques for navigating and visualizing data from a sensor-perspective are examined. A novel sensor-perspective transformation of MTL data from three to two dimensions enables analysis of MTL data in common GIS and image-processing environments. GEOBIA software (Definiens’ eCognition) is used to construct a procedural feature extraction workflow. The procedures are constructed with semantic classes, data processing rules and functions that drive geometric segmentation and feature recognition. Geometric regularities in urban scenes and knowledge about spatial and semantic relationships are incorporated into the rule set. The results are fluidly integrated back into a GIS environment. Investigation of alternative approaches to handling MTL data such as those carried out in this thesis are essential if this technology is to see widespread use.