Research projects

With the democratization of acquisition systems, it is possible to create digital twins of our reality at increasingly smaller rates. Point clouds from different modalities (mobile mapping, airborne, terrestrial, photogrammetric, etc.), surface reconstructions, or geodetic measurements need be constantly integrated into a common environment. This poses a challenge to the Smart Worlds workflow consisting of acquisition, processing & understanding, simulation, and decision-making.

The main research goals in the project AMASE are:

• Tackle high volume, heterogeneous data at increasingly smaller acquisition intervals: Out-of-core and level-of-detail approaches scale well for processing, visualization, or manipulation of large geodetic data. However, high-frequency acquisitions require new approaches to keep processing results, analysis, and even decisions in sync with new incoming data.
• Provide experts with a collaborative geospatial environment: Analysis workflows become increasingly complex, thus they can only be approached by multiple experts combining workforce and cross-discipline expertise. Therefore, it is crucial to create a shared environment that allows experts to collaborate by creating analyses, sharing results, and making decisions. An essential tool for creating a common situational awareness between collaborators is storytelling to communicate results in a nutshell.
• Speed up feature extraction workflows with novel automatic and semi-automatic modelling approaches: To cope with the increasing data velocity, it is necessary to automatize certain processing steps and support users with guidance mechanisms and steering mechanisms in others. In any case, results and parametrization of algorithms need to be transparent, comparable, and traceable.
• Derive new methods for comparing variations of digital twins: This involves the quantification of changes between acquisition intervals, e.g. volume, tunnel crack development over time, but also the comparison of surfaces originating from different reconstruction algorithms or parametrizations.
• Create a new level of realism for bringing hard-to-quantify perceptual effects into digital planning: Constructing buildings or infrastructure is inherently subjected to regulations and policies. In addition to these hard constraints, subjective and hard-to-quantify planning constraints emerge, such as, how comfortable or safe does one feel in a certain environment, or how a new building affects the cityscape.
• Establish reproducible Smart Worlds workflows including rigorous error tracking: With high velocity, collaboration, and complex workflows in mind, experts need to reproduce results on a regular basis, e.g., for retargeting analyses when new input data is available. Additionally, every step in the workflow is a potential source of inaccuracy, which is accumulated, e.g., error stemming from acquisition, reconstruction, or manual annotation and modelling.