Sester - Research Projects

Mobility

  • Traffic Regulator Detection and Identification from Crowdsourced Data
    Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this research is motivated by this fact.
    Team: Zourlidou, Sester
    Year: 2020
  • Visual communication to control route choice behavior
    The individual choice of transport modality and route depends on a number of factors. In particular, information about the expected traffic situation is considered important. It should therefore be examined whether the mediation of the current and the anticipated situation on site (including the indication of certain securities) leads to the choice of a different route or even a different modality.
    Team: Fuest, Sester
    Year: 2018
    Funding: DFG-Graduiertenkolleg SocialCars
  • Deep learning of user behavior in road space - particularly in shared spaces
    The project aims to investigate the behaviour of different road users in unregulated spaces, i.e. spaces open to all road users. Existing approaches are based on a given movement model, which describes the individual behaviour as well as the interactive behaviour of different road users.
    Team: Cheng, Sester
    Year: 2018
    Funding: DFG-Graduiertenkolleg SocialCars
    Duration: 2014-2023

Big Data and Machine Learning

  • TransMIT - Resource-optimised transformation of combined and separate drainage systems in existing quarters with high settlement pressure
    Increasing heavy rainfall events and growing urban districts pose great challenges for urban drainage. Using three neighbourhoods in the cities of Braunschweig, Hanover and Hildesheim as examples, it will be shown how urban development and water management aspects can be linked in the long term in neighbourhood planning.
    Led by: Dr.-Ing. M. Beier; Prof. S. Köster, ISAH; Prof. Sester, ikg
    Team: Yu Feng, Udo Feuerhake
    Year: 2019
  • 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
  • 3D object extraction of high-resolution 3D point clouds
    National Survey Departments acquire area-wide, controlled airborne laser scanning (ALS) datasets with different point densities, which are at least classified into ground and non-ground points. The Working Committee of the Surveying Authorities of the Laender of the Federal Republic of Germany (AdV) is discussing about an update cycle of 10 years for ALS point clouds. The national survey departments also acquire 3D point clouds from aerial images every 2-3 years with high overlapping ratios using a method called Dense Image Matching (DIM). Those DIM point clouds have a high point density, which is equal to the original aerial image resolution. In addition, those DIM point clouds also contain radiometric information from the aerial images, but only reconstruct the surface due to image correlation. This project is split into four distinctive topics.
    Led by: Sester, Monika
    Team: Politz, Florian
    Year: 2017
    Funding: Forschungs- und Entwicklungsvorhaben zwischen den Landesvermessungsämtern Niedersachsen, Schleswig-Holstein und Mecklenburg-Vorpommern
    Duration: seit 2017
  • Scene analysis - pattern recognition in person tracks
    The project deals with the automatic recognition of conspicuous movement patterns from given trajectories as (x, y, t) sequences of objects. For this purpose, patterns are defined that indicate conspicuous behavior for which automatic extraction methods are to be researched. This includes, among others, the qualitative description of the recognition rates of patterns.
    Team: Fischer, Sester
    Year: 2017
  • Real Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas (EVUS)
    This project aims at developing a fast forecast model for pluvial floods in the city of Hannover. The main goal of the subproject for ikg is to integrate new sensors for the flood prediction models.
    Team: Feng, Sester
    Year: 2017
    Funding: BMBF Georisiken

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

  • TransMIT - Resource-optimised transformation of combined and separate drainage systems in existing quarters with high settlement pressure
    Increasing heavy rainfall events and growing urban districts pose great challenges for urban drainage. Using three neighbourhoods in the cities of Braunschweig, Hanover and Hildesheim as examples, it will be shown how urban development and water management aspects can be linked in the long term in neighbourhood planning.
    Led by: Dr.-Ing. M. Beier; Prof. S. Köster, ISAH; Prof. Sester, ikg
    Team: Yu Feng, Udo Feuerhake
    Year: 2019
  • 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
  • 3D object extraction of high-resolution 3D point clouds
    National Survey Departments acquire area-wide, controlled airborne laser scanning (ALS) datasets with different point densities, which are at least classified into ground and non-ground points. The Working Committee of the Surveying Authorities of the Laender of the Federal Republic of Germany (AdV) is discussing about an update cycle of 10 years for ALS point clouds. The national survey departments also acquire 3D point clouds from aerial images every 2-3 years with high overlapping ratios using a method called Dense Image Matching (DIM). Those DIM point clouds have a high point density, which is equal to the original aerial image resolution. In addition, those DIM point clouds also contain radiometric information from the aerial images, but only reconstruct the surface due to image correlation. This project is split into four distinctive topics.
    Led by: Sester, Monika
    Team: Politz, Florian
    Year: 2017
    Funding: Forschungs- und Entwicklungsvorhaben zwischen den Landesvermessungsämtern Niedersachsen, Schleswig-Holstein und Mecklenburg-Vorpommern
    Duration: seit 2017