Smart technologies for automation in data processing and storage

In order to cope with the high amount of digital input data (i.e. some billion laser scanning points and high resolution image data), an automated data processing chain based on an adequate data repository is essential. The “standard“ TLS processing steps are:

  • Point cloud pre-processing: Filtering the given point clouds to minimize noise and eliminate outliers prior to subsequent point cloud based feature computation (e.g. Nothegger & Dorninger, 2009).
  • Registration: Determining the transformation of the individual scanning positions to one project coordinate system (Rusinkiewicz, 2001) as well as assigning the image data to the scanning data.
  • Model generation: Triangulation provides models commonly used for further application (e.g. visualization, interpretation) (Donald & Baker, 2002).

The quality of these three steps directly influences the quality of the shell detection to be applied on their result, i.e. merged point clouds or triangulation models, both representing the entire shell field. To enable an automated detection of the shells, we will evaluate the capabilities of global segmentation algorithms for the given task. 

Furthermore, within this project, we will:

  • evaluate the requirements on smart device based data representation for the given scientific purpose (indoor-positioning, display restrictions, operability), 
  • investigate the integration of smart devices to improve the efficiency and / or quality of geological/paleontological interpretation.

Additionally, these data may be made available for the geotainment-park. Visitors will have the opportunity to download the application for a virtual journey through the oyster reef using their smart devices (e.g. TabletPC).