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Welcome to Feature Extraction from Mobile Mapping Data

Author: Michael Borck

PhD Title: Feature Extraction from Multi-Modal Mobile Mapping Data

Abstract

Automated recognition and analysis of objects in images from urban transport corridors are important for many applications including asset management, measurement, location, analysis and change detection. Current object recognition algorithms are not robust enough to automatically label all objects solely from images, and interactive tagging tools require significant manual effort. The availability of registered 2D images and 3D scanner data (”3D image”) of real-world environments has created new opportunities for automated object labeling. Automatically tagging objects in large 3D images is complex and computationally demanding. It is proposed to segment the images into regions and then classify the objects within these regions. An interactive interface to select region exemplars will be developed. Extracting features from these exemplars and using machine learning, relevance feedback, and other techniques will allow similar regions of interest within the data set to be identified and labeled. Algorithms will be developed to enable efficient search through the data set. Features will be recognised in 2D imagery as well as in 3D point clouds. Techniques and workflows will be developed to allow the selection of exemplars, the development of algorithms to search the image space to locate, and to segment regions of interest from terrestrial based scanned 3D point clouds and 2D imagery of an urban environment.