Leibniz Universität Hannover Faculty Faculty of Civil Engineering and Geodetic Science
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Institute of Cartography and Geoinformatics
 
Institute of Cartography and Geoinformatics Institute Search for Persons Sester
Research Projects
Completed projects

Sester - Research Projects

Mobility

  • Prediction of behaviour and its storage in maps
    In the context of the RTG i.c.sens, the behavior of objects and phenomena in the environment will be studied in order to describe it and store it in maps.
    Led by: Sester, Monika
    Team: Xu, Yiming
    Year: 2023
    Funding: DFG Graduiertenkolleg i.c.sens
    Duration: 2022-2024
  • 5GAPS - Anwendung im Bereich Urbane Logisitk
    Das Projekt 5GAPS (Access to Public Spaces) entwickelt ein alternatives 5G-mobilfunkgestütztes, zeitlich dynamisches Positionierungssystem auf Basis eines digitalen Zwillings des öffentlichen und halböffentlichen Raums in Form eines dreidimensionalen Rasters. Am ikg werden die Themen 1) Lokalisierung innerhalb und mit Hilfe der 3D-Struktur 2) Visualisierung und Interaktion mit der 3D-Struktur 3) Anwendung der 3D-Struktur für die urbane Logistik bearbeitet.
    Led by: Sester, Monika; Feuerhake, Udo
    Team: Wage, Oskar
    Year: 2022
    Funding: Bundesministerium für Digitales und Verkehr, Förderkennzeichen: 45FGU121_E
  • 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
  • Zukunftslabor Mobilität
    Im Rahmen des Zukunftslabors Mobilität arbeitet das ikg im Collaborative Research Field 4 am Thema der Mobilitätsdienste. Am CRF 4 sind WissenschaftlerInnen der Disziplinen Dienstleistungsmanagement ( Prof. David Woisetschläger, TU Braunschweig), Wirtschaftsinformatik (Prof. Jörg Müller, TU Clausthal) und Geoinformatik beteiligt. Ausgehend von den Potentialen der hochgradigen Vernetzung intelligenter Fahrzeugsysteme und Infrastrukturen sollen neue Dienstleistungen und Geschäftsmodelle für intelligente Fahrzeuge und (intermodale) Mobilitätslösungen entwickelt, untersucht und demonstriert werden. Im Fokus steht die Anwendung von Methoden für die Exploration von Anforderungen, die Entwicklung und Bewertung von Dienstleistungen für die nutzerspezifische Mobilitätsplanung, Untersuchungen zur Akzeptanz sowie Methoden zur Konzeption, Implementierung und Evaluation digitaler Geschäftsmodelle und hybrider Dienste.
    Led by: Sester
    Team: Koetsier
    Year: 2019
    Funding: MWK Niedersachsen
    Duration: 2019-2024
  • 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
  • Controlling pedestrian motion using Augmented Reality
    Controlling pedestrian motion pattern using augmented reality requires explainable visualizations to convince the user to change directions and speed of motion. Such AR visualizations should avoid cognitive overload and should provide motion guidance that are accurate representations of expected user actions to avoid conflicts / collisions.
    Led by: Sester
    Team: Kamalasanan, Vinu
    Year: 2020
    Funding: DAAD - im Rahmen des GRK SocialCars
    Duration: 2020-2023

Data Integration

  • VGI-LOC: Zukunftslabor Wasser - ZDIN
    Das ikg ist Partner im Projekt Zukunftlabor Wasser, einer vom MWK geförderten Initiative zur Digitalsierung im Bereich Wasser.
    Led by: Sester, Monika
    Team: Tim Schimansky
    Year: 2022
    Funding: MWK Niedersachsen
    Duration: 2022-2025

3D Visualization

  • DiViAS - Digitalisierung, Visualisierung und Analyse von Sammlungsgut
    Digitalisierung, Visualisierung und Analyse von Sammlungsgut (DiViAS) Der Forschungsverbund DiViAS bringt wissenschaftliche Methoden und Praktiken beim Digitalisieren, Erforschen und Repräsentieren von Sammlungsgut aus kolonialen Kontexten zusammen. Das Projekt verbindet systematisch die Expertise aus Museums-, Geschichts- und Kulturwissenschaften mit derjenigen aus Technik und Informatik – etwa in puncto Künstliche Intelligenz (KI), Datenanalyse, Geoinformatik und dreidimensionaler Messtechnik. Das Projekt wird aus dem Programm „zukunft.niedersachsen“ der VolkswagenStiftung gefördert. Am ikg werden Möglichkeiten der Visuellen Kommunikation von raum-zeitlichen Objekten und Phänomenen unter dem Gesichtspunkt der Unsicherheit untersucht.
    Led by: Sester
    Team: Fuest
    Year: 2024
    Funding: MWK zukunft.niedersachsen
  • Controlling pedestrian motion using Augmented Reality
    Controlling pedestrian motion pattern using augmented reality requires explainable visualizations to convince the user to change directions and speed of motion. Such AR visualizations should avoid cognitive overload and should provide motion guidance that are accurate representations of expected user actions to avoid conflicts / collisions.
    Led by: Sester
    Team: Kamalasanan, Vinu
    Year: 2020
    Funding: DAAD - im Rahmen des GRK SocialCars
    Duration: 2020-2023

Laser Scanning

  • 5GAPS - Lokalisierung
    “Lokalisierung von mobilen Objekten in 3D-Rasterdaten” Das Projekt 5GAPS entwickelt ein alternatives 5G-mobilfunkgestütztes, hochgenaues und um Eigenschaften erweitertes dynamisches Positionierungssystem auf Basis eines digitalen Zwillings des (halb)öffentlichen Raums in Form eines 3-D-Rasters und führt erste Proofs-of-Concept durch. Das Projekt wird mit verschiedenen Partnern aus Wissenschaft und Wirtschaft bearbeitet. Am Institut für Kartographie und Geoinformatik sollen dabei Aufgaben im Bereich der Erfassung, Verarbeitung und Visualisierung der hochdimensionalen Daten durchgeführt werden.
    Led by: Sester, Monika
    Team: Faezeh Sadat Mortazavi
    Year: 2022
    Funding: Bundesministerium für Digitales und Verkehr, Förderkennzeichen: 45FGU121_E
    Duration: 2022-2024
  • 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

Last Change: 10.03.23 Print

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