Leibniz Universität Hannover Faculty Faculty of Civil Engineering and Geodetic Science
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Institute of Cartography and Geoinformatics
 
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Research Projects

Thiemann - Research Projects

Big Data and Machine Learning

  • Object detection in airborne laser scanning (ALS) data using deep learning
    In partnership with the Lower Saxony State Office for Preservation of Historic Monuments, we are developing a method for automatically detecting archaeological objects in airborne laser scanning data. The type of objects to be detected are mainly those of interest by archaeologists such as heaps, shafts, charcoal piles, pits, barrows, bomb craters, hollow ways, etc. They could be point, linear, or areal objects. To this end, we are using deep learning techniques; namely, convolutional neural networks (CNNs) to classify height images from the region of interest. A combination of multiple (in most cases 5) CNN classifiers are then used to detect and localize objects of interest in a digital terrain model acquired from the region of interest.
    Team: Kazimi, Thiemann, Sester
    Year: 2018
    Funding: MWK Pro*Niedersachsen

Generalization

  • PUSH -- Automatic Cartographic Displacement Based on Optimization
    The software program PUSH allows the automatic displacement of geo-objects of all types, i.e. points, lines and polygons. The object characteristics which influence the displacement behaviour can be parametrized in a very flexible way: either object specific or object-class specific. The results allow for an automatic quality control. The program is able to generalize also large data sets.
    Team: Sester, Thiemann
    Year: 2021

Laser Scanning

  • Object detection in airborne laser scanning (ALS) data using deep learning
    In partnership with the Lower Saxony State Office for Preservation of Historic Monuments, we are developing a method for automatically detecting archaeological objects in airborne laser scanning data. The type of objects to be detected are mainly those of interest by archaeologists such as heaps, shafts, charcoal piles, pits, barrows, bomb craters, hollow ways, etc. They could be point, linear, or areal objects. To this end, we are using deep learning techniques; namely, convolutional neural networks (CNNs) to classify height images from the region of interest. A combination of multiple (in most cases 5) CNN classifiers are then used to detect and localize objects of interest in a digital terrain model acquired from the region of interest.
    Team: Kazimi, Thiemann, Sester
    Year: 2018
    Funding: MWK Pro*Niedersachsen

Last Change: 10.03.23 Print

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