Maintenance and expansion of transport and communications infrastructure requires ongoing construction work on a large scale. To plan and execute these in the best possible way, up-to-date and highly detailed digital maps are needed. For example, until recently, telecommunication companies have performed documentation and mapping of as-built urban structures for construction work manually and with great time expense. Mobile mapping systems offer a solution for documenting urban environments fast and mostly automated. In consequence, large amounts of recorded data emerge in short time, creating the need for automated processing and modeling of these data to provide reliable foundations for digital planning in reasonable time. We present (a) a procedure for fully automated processing of mobile mapping data for digital construction planning in the context of nationwide broadband network expansion and (b) an in-depth study of the performance of this procedure on real-world data. Our multi-stage pipeline segments georeferenced images and fuses segmentations with 3D data, which allows exact localization of surfaces and objects, which can then be passed via interface, e.g., to a geographic information system (GIS). The final system is able to distinguish between similar looking surfaces, such as concrete and asphalt, with a precision between 80% and 95%, regardless of setting or season.

Neurotechnology offers the potential to improve performance in cognitive tasks by tailoring the learning paradigm to the neurophysiological correlates of mental processes. Up to date, there are few studies that investigate the single trial performance of neural decoding in cognitive tasks. In this study we examine EEG data while a given subject is solving questions which are commonly used in intelligence quotient tests. Subjects are instructed to solve a number of visual template matching tasks. Our findings suggest that it is possible to decode the true answer from the subjects' ERP responses at the time of its presentation. These results indicate that neurophysiological markers could be useful for neurotechnology assisted learning paradigms.

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