Statistics

  • Detection of structural breaks in stochastic processes with spatial dependencies
    With growing availability of high-resolution spatial data, like high-definition images, 3d point clouds of LIDAR scanners, or communication and sensor networks, it might become challenging to timely detect changes and simultaneously account for spatial interactions. To detect local changes in the mean of isotropic spatiotemporal processes with a locally constraint dependence structure, we propose a monitoring procedure, which can completely be run on parallel processors. This allows for a fast detection of local changes, i.e., only a few spatial locations are affected by the change. Due to parallel computation, high-frequency data could also be monitored. We, therefore, additionally focus on the processing time required to compute the control statistics.
    Led by: Prof. Dr. Philipp Otto
    Year: 2019
  • Spatial and spatio-temporal GARCH models
    The project is concerned with a subfield of spatial statistics, which deals in particular with the analysis of random processes in space. When analyzing such processes, it can often be found that observations that are located in spatial proximity to each other are similar. If, for example, land prices in a municipality are high, high prices can also be expected in the surrounding municipalities. In addition to this spatial dependency in the level of observations, a spatial dependency in the dispersion of observations as well as the conditional heteroskedasticity can also be determined. In the project, models for this will be developed and extended. The spatial models are analogous to the ARCH model of Robert F. Engle (1982) in time series analysis, who was awarded the Nobel Prize for Economics in 2003.
    Led by: Prof. Dr. Philipp Otto
    Year: 2019
    Funding: Deutsche Forschungsgemeinschaft

Student Research Projects

  • Processing of large-scale data sets in the context of autonomous driving
    The research training group i.c.sens has produced large quantities of data to support scientific research in the context of autonomous driving. To this end, multiple cars have been equipped with complex sensor setups for self-localization and mapping, including multiple GNSS systems, stereo cameras and multiple LiDAR systems. In order to enable secondary usage of these data sets and to publish the data set at a later point in time, the data needs to be prepared using established sensor-specific data processing methods or manual data annotation processes (e.g. labeling of images or point clouds towards a reliable ground-truth). The range of possible activities (programming, using a GIS for analyses, manual editing/annotation of data using provided tools and many more) in this research project is wide and can support multiple students at the same time. However, there is a lot of flexibility in determining the specific tasks to carry out (in a meeting before starting the research project).
    Led by: Kuntzsch, Peters
    Year: 2018

Mobility

  • 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
  • USEfUL
    Due to its location at the center of Europe and the global operating companies, logistics and mobility have always been of outstanding importance in Hanover, a city rebuilt car-friendly after the war. A growing city is associated with increasing mobility and supply needs as well as an individually and systemically caused need of logistics for supply and disposal.
    Team: Wage, Feuerhake
    Year: 2018
    Funding: BMBF: 03SF0547
  • 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
  • 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
  • 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
  • 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
  • Mobile Mapping Bike
    The ikg Mobile Mapping Bike is a supplement to existing mobile mapping systems such as those used at the Institute of Cartography and Geoinformatics, which are traditionally mounted on a car or van. The cargo bike makes it possible to cover or develop remote, winding or inaccessible regions. But the Mobile Mapping Bike can also be used independently, not only to record the surroundings, but also to enrich various other measured values with georeferencing. It can also be used and reconfigured more easily for student projects, as no driving license is required and the software and hardware can be flexibly adapted. The sensor platform is a customized design that was manufactured at the institute. It consists of a stable frame that is mounted on the cargo bike's loading box with dampers. The frame offers the possibility to mount different sensors on several levels and thus expand the sensor platform in many ways. All sensor data is merged by an on-board computer and stored on an external data carrier. The fusion of the individual data sources takes place on the basis of the Robot Operating System (ROS) and uses GPS time for time synchronization.
    Led by: Schimansky, Wage, Golze, Feuerhake
    Year: 2023
    Funding: Institutsprojekt
    Duration: fortlaufend
    MobileMappingBike MobileMappingBike

3D Visualization

  • 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
  • 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

  • 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
  • Collaborative acquisition of predictive maps
    Self-driving cars and robots that run autonomously over long periods of time need high precision and up-to-date models of the environment. Natural environments contain dynamic objects and change over time. Since a permanent observation of “everything” is impossible and there will always be a first time visit of the changed area, a map that takes into account the possibility of change is needed.
    Team: Schachtschneider, Brenner
    Year: 2017
    Funding: DFG-Graduiertenkolleg i.c.sens
  • 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
  • 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
  • 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
  • Mobile Mapping Bike
    The ikg Mobile Mapping Bike is a supplement to existing mobile mapping systems such as those used at the Institute of Cartography and Geoinformatics, which are traditionally mounted on a car or van. The cargo bike makes it possible to cover or develop remote, winding or inaccessible regions. But the Mobile Mapping Bike can also be used independently, not only to record the surroundings, but also to enrich various other measured values with georeferencing. It can also be used and reconfigured more easily for student projects, as no driving license is required and the software and hardware can be flexibly adapted. The sensor platform is a customized design that was manufactured at the institute. It consists of a stable frame that is mounted on the cargo bike's loading box with dampers. The frame offers the possibility to mount different sensors on several levels and thus expand the sensor platform in many ways. All sensor data is merged by an on-board computer and stored on an external data carrier. The fusion of the individual data sources takes place on the basis of the Robot Operating System (ROS) and uses GPS time for time synchronization.
    Led by: Schimansky, Wage, Golze, Feuerhake
    Year: 2023
    Funding: Institutsprojekt
    Duration: fortlaufend
    MobileMappingBike MobileMappingBike

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

Robotics

  • Landmark-based localization
    Within the project, new approaches are developed for a highly accurate localization of vehicles relative to their environment. Furthermore, it is analyzed how detailed descriptions of the environment can be used for interpreting the scenery, for example for active driver assistance systems
    Team: Schlichting, Brenner
    Year: 2017
  • Collaborative acquisition of predictive maps
    Self-driving cars and robots that run autonomously over long periods of time need high precision and up-to-date models of the environment. Natural environments contain dynamic objects and change over time. Since a permanent observation of “everything” is impossible and there will always be a first time visit of the changed area, a map that takes into account the possibility of change is needed.
    Team: Schachtschneider, Brenner
    Year: 2017
    Funding: DFG-Graduiertenkolleg i.c.sens

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

Big Data and Machine Learning

  • RainCars
    This idea would be easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This initial research will explore theoretically the benefits of such an approach. For that valid relationships between wiper speed and rainfall rate (W-R relationship) are assumed and derived from laboratory and field experiments. Different traffic models are developed to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled from the conventional rain gauge and dynamic car networks. Areal rainfall is calculated from these networks for different scales using geostatistical interpolation methods and compared against truth radar data. The car sensors can be considered as a geosensor network. It allows to measure and process information locally in a decentralized way and thus has benefits with respect to scalability, which is crucial when large areas have to be covered with large amounts of measurement units.
    Team: Fitzner, Sester
    Year: 2017
  • 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
  • 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
  • Interdisciplinary Center for Applied Machine Learning - ICAML
    The ICAML (Interdisciplinary Center for Applied Machine Learning) aims at increasing the accessibility of machine learning across disciplines. Therefore, three fundamental components are developed and used.
    Team: Artem Leichter
    Year: 2018
    Funding: Federal Ministry of Education and Research
    Duration: 11/2017-11/2019
  • USEfUL
    Due to its location at the center of Europe and the global operating companies, logistics and mobility have always been of outstanding importance in Hanover, a city rebuilt car-friendly after the war. A growing city is associated with increasing mobility and supply needs as well as an individually and systemically caused need of logistics for supply and disposal.
    Team: Wage, Feuerhake
    Year: 2018
    Funding: BMBF: 03SF0547
  • Statistical estimation of high-dimensional, spatial dependency structures using machine learning methods
    The project deals with an important, fundamental problem of spatial and spatiotemporal statistics – the full estimation of the underlying spatial dependence structure. For these models, the focus has so far been on processes showing a dependence in the conditional means. That is, the mean of a realization of the random process at a particular measurement point depends on the adjacent observations. This finding goes back to Tobler’s first law of Geography. The surrounding observations are defined on the basis of their geographical proximity, although this does not necessarily lead to a dependence of the observations of the random variables, i.e. the covariances. Various application examples will be used to demonstrate how the estimated parameters can be interpreted. Here, the focus will be on natural processes in the environment, such as air pollution or particulate matter. Using freely available sensor data, the results can be used, for example, to obtain local predictions of fine dust pollution in an urban area, which can then be used for optimal routing with respect to air quality.
    Led by: Prof. Dr. Philipp Otto
    Year: 2019
    Funding: Deutsche Forschungsgemeinschaft
  • 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
  • AgrImOnIA: The impact of agriculture on air quality and the COVID-19 pandemic
    AgrImOnIA is the project short name, being the acronym of Agricultural Impact On Italian Air. Also, Agrimonia eupatoria or Common agrimony is a plant and a flower spread in Alpine, Mediterranean and Central Europe, which is the positive symbol of this project. Lombardy has about 10 Million inhabitants and is the first Italian region for agriculture production. Bovines are about 1.5 Million and swine are about 4 Million. On average, there is a swine every 2.5 inhabitants and a bovine every 6.7 inhabitants. Considering that agriculture land is 69% of the region, there are about 245 swine and 92 bovines per rural km2. There is a large scientific consensus on the fact that livestock and fertilizers are responsible for about 95% of the ammonia emissions. After some reactions in the atmosphere, ammonia mainly turns to fine particulate matters known as PM2.5. Since PM2.5 is quite stable in the atmosphere, due to the limited air circulation in the Po Valley, the air quality is often affected by high concentrations of such pollutants. Adding the contributions of vehicle traffic and house heating, the plain of Lombardy is one of the most polluted areas in Europe. As a result, epidemiological studies found Lombardy to be the region with the highest PM2.5-related mortality rate: 164 deaths per 100 thousand inhabitants.
    Team: Otto, Shaboviq
    Year: 2021
    Funding: Cariplo Foundation (European Project)

Bachelor Theses (finished)

  • Trajectory modeling
    With global navigation satellite systems and their free positioning services, as well as small, low-cost GNSS receivers, it's never been easier to capture and record motion anywhere, anytime. The resulting data volumes can quickly become very large. This makes these records impractical when it comes to storage and evaluation. Approaches to reduce the amount of data while preserving a maximum of spatio-temporal information are required.
    Led by: Colin Fischer
    Team: Sebastian Leise
    Year: 2016
    Duration: 2016

Open Bachelor Theses

  • Kartographie: Automatische Platzierung von Böschungsschraffen
    Für die Darstellung von Wällen und Gräben in archäologischen Plänen werden Schraffen verwendet. Anders als bei neuzeitlichen künstlichen Böschungen sind die historischen Böschungen durch Einwirkung der Erosion sehr unregelmäßig geformt. Standardalgorithmen scheitern aus diesem Grund bei der automatischen Anordnung der Schraffen.
    Led by: Thiemann
    Year: 2019
  • Homogenisierung der Gebäudeausrichtung
    Topographischen Karten 1 : 25.000 werden Gebäude noch grundrissähnlich dargestellt. Detailierte Gebäudegrundrisse aus dem Kataster (ALKIS) müssen dazu generalisert (klassifiziert, selektiert, aggregiert, vereinfacht, betont, verdrängt) werden. Ein Aspekt der Generalisierung ist die homogene Ausrichtung der Gebäude.
    Led by: Thiemann
    Year: 2019
  • Bestimmung von Mustern in Fahrzeugtrajektorien
    Die Bewegungstrajektorien von Fahrzeugen erlauben Rückschlüsse auf raum-zeitliche Situationen. So können beispielsweise Haltepunkte detektiert werden oder auch Stausituationen, oder auch Anomalien wie temporär nicht zu befahrende Straßensegmente. In der Arbeit sollen in einem großen Trajektoriendatenbestand solche Muster automatisch erkannt werden. Der Datenbestand umfasst sehr viele Trajektorien. Bei Interesse kann ein Schwerpunkt auf die skalierbare Datenanalyse mittels Hadoop und Spark gelegt werden. Je nach Schwerpunkt ist die Arbeit sowohl als Bachelor- als auch als Masterarbeit bearbeitbar.
    Team: Feuerhake, Sester
    Year: 2020
  • Exploring Herrenhausen Gardens
    Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience
    Led by: Feuerhake, Sester
    Year: 2023
  • Detection of Signatures in old Maps using Deep Learning
    Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.
    Led by: Thiemann, Sester
    Year: 2023
  • Unveiling the Wireless Jungle
    With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.
    Led by: Schimansky, Golze
    Year: 2023
  • Mobile Mapping Bike LiDAR Evaluation
    Das ikg setzt bereits seit einigen Jahren ein Auto basiertes LiDAR Mobile Mapping System zum Erfassen von Punktwolken ein. Diesen bieten vielfältige Analyse- und Visualisierungsmöglichkeiten. Allerdings ist die Nutzung des Messsystems auf mit dem Auto befahrbare Straßen beschränkt und die Prozessierung hängt von proprietärer Software ab. Um die Nutzungsmöglichkeiten zu erweitern wurde daher am ikg ein Lastenfahrrad basiertes Mobile Mapping System konstruiert. Dazu wurde ein Trike mit E-Unterstützung um ein Multisensorsystem erweitert, bestehend aus: LiDAR, RTK-GNSS, IMU und Erweiterungsmöglichkeiten um z.B. eine Thermalkamera. Dank der robusten Positionierung sind auch Messfahrten durch partiell abgeschattete Bereich (auch durch Innenbereiche) möglich. Auf dem Bordrechner werden die eingehenden Sensorstreams mittels ROS aufgezeichnet und bieten so individuelle Möglichkeiten zur weiteren Verarbeitung und Erweiterung des Systems. Ziel einer Abschlussarbeit wäre die Evaluation der resultierenden Punktwolke in Hinblick auf ihre relative Genauigkeit, sowie im Vergleich zum hochgenauen bisherigen System.
    Led by: Schimansky, Wage
    Year: 2023

Master Theses (finished)

  • Gesture-based interaction with virtual 3D environments
    With the availability of increasingly powerful computing technology in the home/leisure sector, a race to develop affordable virtual reality (VR) and augmented reality hardware broke out on the technology market a few years ago, targeting potential markets, in particular, for realistic 3D content presentation (e.g. computer games). The core of this technology is the processing of three-dimensional information in the form of 2D stereo image pairs, which can be consumed via suitable output hardware (glasses/helmets). However, this principle can also be used elsewhere, for example for better exploration of or interaction with 3D spatial data.
    Led by: Colin Fischer
    Team: Florian Politz
    Year: 2016
    Duration: 2016
  • Automatic enrichment of route instructions to form a cognitive map
    Commonly used navigation instructions are based on metric turn descriptions (like "turn left onto Nienburger Straße in 100 m"). For the user it is easy to follow the course, but later it is hard to remember how s/he got there. Natural orientation is based on remarkable objects or locations called landmarks. Multiple of them are combined to the psychological model of a cognitive map, a network of landmarks and connecting actions. The resulting goal is to enhance the people's own sense of orientation by enriching common routing instruction with hints on landmarks. The automatic description of relevant landmarks along a route is implemented as a interactive web-map.
    Team: Oskar Wage
    Year: 2017
    Duration: 2017
  • Deep Learning for Flood Relevant Images and Texts from Social Media
    Floods are among Earth's most common and most destructive natural hazards. This work explores the idea of utilizing user-generated information from social media to recognize early signs of flood relevant events. The goal of this work lies in the development and implementation of a Deep Learning solution with the ability to detect the presence of flood relevant events from user-generated images and texts.
    Led by: Yu Feng, Prof. Brenner
    Team: Sergiy Shebotnov
    Year: 2018
  • Development of environmentally-balaced and congestion-avoiding routing algorithms by means of traffic simulation
    Due to the constantly growing volume of traffic in urban environments and the resulting problems such as increased air pollution, environmentally oriented approaches to achieve better urban sustainability of transport play an increasingly important role. This thesis deals with the development of environmentally-friendly routing algorithms and their validation in traffic simulations. The routing algorithm used is the A* - algorithm using the developed criteria as weights.
    Led by: Sester, Fuest
    Team: Christian Hartberger
    Year: 2019
  • Development of a Client-Server Module for Cooperative Multi-Robot Longterm Map Registration
    Nowadays a big amount of robots are used in production and logistic. Due to the large working environment, dynamic objects (e.g. humans or other robots), and semi-static objects (e.g.machine and furniture), a high performance navigation system is required. But only focus on the high performance long term SLAM on single robot is not enough to guarantee the flexible and accurate performance of whole robot fleet in large changing environment.
    Led by: Tobias Ortmaier (IMES), Claus Brenner, Steffen Busch (IKG), Philipp Schnattinger (FraunhoferIPA)
    Team: Jiang Liwei
    Year: 2019
  • Classification and detection of road users using neural networks and Active Shape models
    Autonomous vehicles interpret their environment based on their sensor data. 360° laser scanners provide comprehensive and highly accurate information about the distance of objects. Predicting the behavior of road users differs between cars, trucks/buses, cyclists and pedestrians. The exact position of the different road users depends on their orientation and geometric dimensions. Active Shape models offer the possibility to estimate the center of objects by estimating deformable models, based on CAD plans and taking into account their orientation.
    Led by: Bodo Rosenhahn (TNT), Claus Brenner, Steffen Busch (IKG)
    Team: Xiaoyu Jiang
    Year: 2019
  • Laser scanner-based prediction of pedestrian movements by filtering and classifying posture
    Against the background of road safety, an algorithm is presented below that uses point clouds to make the most accurate prediction possible about the future position of pedestrians. A core element is to classify the current state of movement of pedestrians over a random forest. The focus is on early detection of changes between individual states.
    Led by: Claus Brenner, Steffen Busch
    Team: Matthias Fahrland
    Year: 2019
  • Travel Delay Analysis Using VISSIM and Pattern Recognition at Regulated Junctions
    This thesis explores the travel time and travel delay at T- and four-way junctions under different regulator settings (yield/priority traffic signs and uncontrolled junctions), conducting experiments both with simulation originated and real data. First this thesis uses VISSIM simulation software to estimate the delay and stop time at yield controlled and uncontrolled T- and and Four-way intersections. At the second part of the thesis, the same objective is being pursued by using real data.
    Led by: Zourlidou
    Team: Qingyuan Wang
    Year: 2019
  • Pattern Recognition of Movement Behavior for Intersection Classification using GPS Trace Data
    The aim of this thesis is to classify different regulator types of traffic road intersections based on GPS trace data. To reach this aim a variety of features is calculated to describe the driving behavior at intersections. These are derived from the measured units of the GPS trace data that compose an individual’s movement trajectory.
    Led by: Zourlidou
    Team: Jens Golze
    Year: 2019
  • Robust registration of airborne point clouds
    Goal of this thesis is the robust registration of airborne point clouds, which are derived from Airborne Laser Scanning (ALS) and Dense Image Matching (DIM). We implemented a coarse, translative registration method using a Maximum Consensus Estimator and compared our results with a standard ICP. In addition, we tested several methods to prune object points from point clouds, which are represented differently in both point cloud types.
    Led by: Politz, Brenner
    Team: Jannik Busse
    Year: 2019
  • Optimal assignment of point clouds using deep learning
    The main goal of this master thesis was to register airborne 3D point clouds from different sensor systems. These point clouds are derived from airborne laser scanning (ALS) and dense image matching (DIM) of aerial images. Those point clouds may cover the same surface, but do contain different attributes and characteristics. One major problem when dealing with those two point clouds is vegetation. The laser beam in ALS is able to penetrate vegetation leading to ground and vegetation points in the final point cloud. Since they are derived from aerial images, DIM point clouds only contain the surfaces and thus describes the treetops. This major difference between ALS and DIM causes problems for the registration of point clouds and established algorithms such as the iterative closest points (ICP) algorithm are facing issues when dealing with both point cloud types at the same time, because they assume that similar points are close to each other.
    Led by: Brenner, Politz
    Team: Stephan Niehaus
    Year: 2019
  • Klassifikation und Änderungsdetektion in Mobile Mapping LiDAR Punktwolken
    3D-Modelle der statischen Umgebung zu erstellen ist eine wichtige Aufgabe für das Voranbringen von Fahrerassistenzsystemen und dem autonomen Fahren. Hierzu stehen in dieser Arbeit Mobile Mapping LiDAR Punktwolken aus 14 Messepochen zur Verfügung, die mithilfe eines Voxel Grids zu einer Referenzkarte weiterverarbeitet werden. Ein Voxel Grid ist eine Datenstruktur, die den realen Raum in volumenhafte Elemente unterteilt und die Punktdichte der Punktwolken reduziert. Zusätzlich werden Daten aus einer Strahlverfolgung bereitgestellt, sodass zwischen durchschossenen und unbekannten Voxeln unterschieden werden kann, wodurch sich Verdeckungen erkennen lassen.
    Led by: Brenner, Schachtschneider
    Team: Mirjana Voelsen
    Year: 2019
  • Semi-Supervised Deep Learning for Object Detection in Airborne Laser Scanning Data
    The main objective of this experiment is to compare the performance of Semi-Supervised Learning classification method and normal convolutional neural network classification method on Airborne Laser Scanning data (ALS) and find out the best suitable model that we can use to detect object in a large-scale region by using windows sliding approach. The data I used is digital terrain model (DTM) which generated by ALS point cloud and contains 4 different classes. In each class include 6 different visualization formats (digital terrain model, sky-view factor, local Dominance, positive-negative openness and simple local relief model) which use different visualization techniques to be generated from the original digital terrain model.
    Led by: Kazimi
    Team: Xin Yang
    Year: 2019
  • Trajectory Analysis at Intersections
    In this thesis, we focus on trajectories at different intersections with various regulated types (traffic-light-controlled, priority/yield- controlled, uncontrolled) and test some methods to detect and recognise movement patterns, in terms of their geometrical and spatio-temporal components. That is, in the first case the geometric paths that vehicles follow while driving through the intersections and in the second case the way that they follow these geometric paths.
    Led by: Zourlidou
    Team: Chenxi Wang
    Year: 2020
  • Trajectory anomaly detection using spectral clustering and RNN-based auto-encoder
    Anomaly detection is important, because anomalous behavior may indicate critical events or objects within diverse research areas and application domains. One of such domains is transport, especially integrated urban mobility. Trajectories of moving objects are good representations of their behaviors in surveillance data and useful in detecting anomalous behavior. On one hand, trajectories can provide more agent-based, long-term information comparing with simple physical features. On the other hand, comparing with raw video data, which is usually represented as a sequence of images, trajectory data requires less storage space and computational resources. Moreover, it has a wide variety of sources, such as GPS instruments and laser-scanners.
    Led by: Sester, Koetsier
    Team: Yao Li
    Year: 2020
  • Identification and analysis of movement patterns in trajectories
    In this work, movement patterns in trajectory datasets are identified with respect to the respective visited locations of a trajectory. For this purpose, further semantic information is assigned to the whereabouts points depending on the position, time of day, and duration of stay; the assignment of semantic information with respect to position is done using OpenStreetMap data. Another focus was on the identification of related trajectory segments, since the given dataset was anonymized as a consequence of data protection; for this purpose, coordinate prediction was performed for all trajectory endpoints in order to identify a suitable continuing starting point of another trajectory using a proximity search and temporal proximity. Recurrent motion pattern detection performed based on the whereabouts points does not produce meaningful patterns detected in multiple trajectories throughout the dataset for the datasets used; however, meaningful recurrent patterns are found for individual trajectories. An increasing level of detail in assigning categories with respect to whereabouts results in fewer recurring patterns, which, on the other hand, allow for greater meaningfulness given the interpretation of an observed person’s movement behavior.
    Led by: Golze, Feuerhake, Wage, Sester
    Team: Friderike Fischer
    Year: 2022
  • Development of a modular sensor platform for mobile detection of vehicle encounters
    Riding a bike in a shared traffic area with motor vehicles causes discomfort for many bicyclists. Avoiding busy roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles on most roads. Acquiring a dataset that collects smartphone sensor data on vehicle encounters could become the basis for a smartphone-based vehicle detector. Magnetometer and barometer readings are used as indicators of passing vehicles. In this thesis, a sensor platform is first constructed to collect smartphone and other sensor data while driving. The system is designed to be used with other sensor configurations in the future. A methodology is then presented to create a dataset of vehicle encounters based on data from a camera and a distance sensor on the sensor platform. This data set contains all important sensor data of a commercially available smartphone including the timestamp of vehicle encounters. Finally, a three-class classifier is trained and evaluated based on the data set. It is investigated which approach can provide a generalizable classifier. Approaches based on Random Forests are investigated for the classifier. The structure and parameters of a sliding window function are adjusted for feature generation.
    Led by: Wage, Feuerhake, Golze, Sester
    Team: Tim Schimansky
    Year: 2022
  • Hololens 2 – Evaluating 3D Mapping and Technical Capabilities
    In this study, the technical and 3D mapping capabilities of Hololens 2 was evaluated. The Microsoft Hololens 2 is a head-worn mobile mixed reality device that is capable of mapping its direct environment in real time. It is equipped with different sensors including four visible light tracking cameras and a depth sensor. The 3D map created using these sensor streams can be accessed by research mode. This makes Hololens 2 a powerful tool for mapping an indoor space. In this work, we evaluate the capabilities of Hololens 2 with respect to the task of the 3D indoor mapping, semantic segmentation and 3D modelling as the quality of scanned data highly influences the accuracy of reconstruction and segmentation.
    Led by: Vinu Kamalasanan, Monika Sester
    Team: Vishal Rudani
    Year: 2022
  • Nutzungsdatengetriebene Analyse des Potentials von Mikromobilitätsdiensten
    Der geteilten Mobilität wird in der öffentlichen Debatte um die Verkehrswende häufig eine entscheidende Rolle zugeordnet. Darunter fallen auch die sogenannten Mikromobilitätsdienste. Das Ziel dieser Masterarbeit ist es, das Potential von Mikromobilitätsdiensten für die Verkehrswende im Hinblick auf die Intentionen der Nutzer, auf zeitliche Variationen, sowie auf Vorteile gegenüber anderen Transportmitteln datenbasiert zu bewerten. Dafür wird eine Fallstudie anhand von Mobilitätsdaten der Bikesharing-Fahrräder und Elektrotretroller zweier Anbieter in Hannover durchgeführt.
    Led by: Wage, Feuerhake, Golze
    Team: Finn Boie
    Year: 2022
  • Comparison of network representations for analysing temporal power plant data
    As renewable energy is increasingly used in power generation, the temporal and spatial balance of electric power supply and demand requires large-scale power transmission to maintain. Describing such systems requires network modeling theory. This dissertation takes the German power transmission network as an example and explores the impact of different representations. The representation forms include unweighted network, weighted network, multiplex network and interconnected network. In this dissertation, the static topological characteristics of networks under different representations are examined. Then, the temporal data of the available capacity is also introduced, and a temporal network with the power flow path as the time variable is constructed based on Djikstra’s algorithm. In this research, we find that the weighted network is more suitable for modeling transmission networks than the unweighted network, and the multi-layer network may be more suitable for modeling more complex systems.
    Led by: Anna Malinovskaya, Philipp Otto
    Team: Ruochen Yang
    Year: 2022
  • Hololens 2 - Analysis of capabilities and quality
    The Hololens is a device, which captures information of the environment and creates a 3D model of it. At the same time, it is able to place virtual objects into the environment and thus allows AR-applications. The goal of the thesis is to investigate the potential of the Hololens for capturing indoor environments. This includes the acquisition of 3D point clouds and a thorough quality assessment. Subsequently, the point could has to be processed in order to segment important objects or features (e.g. walls, furniture). To this end, the use of Deep Learning models has to be considered.
    Led by: Kamalasanan, Sester
    Year: 2023
  • Future trajectory and Motion guidance with Augmented reality
    Controlling pedestrian motion pattern using augmented reality would require 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. The focus of this master thesis would be to design and evaluate 3D motion guidance augmentations using AR emphasizing how such visualizations can avoid collisions between pedestrian / smartphone zombie. The student is expected to design and validate motion guidance visualizations in augmented reality
    Led by: Kamalasanan, Sester
    Year: 2023

Open Master Theses

  • Bestimmung von Mustern in Fahrzeugtrajektorien
    Die Bewegungstrajektorien von Fahrzeugen erlauben Rückschlüsse auf raum-zeitliche Situationen. So können beispielsweise Haltepunkte detektiert werden oder auch Stausituationen, oder auch Anomalien wie temporär nicht zu befahrende Straßensegmente. In der Arbeit sollen in einem großen Trajektoriendatenbestand solche Muster automatisch erkannt werden. Der Datenbestand umfasst sehr viele Trajektorien. Bei Interesse kann ein Schwerpunkt auf die skalierbare Datenanalyse mittels Hadoop und Spark gelegt werden. Je nach Schwerpunkt ist die Arbeit sowohl als Bachelor- als auch als Masterarbeit bearbeitbar.
    Team: Feuerhake, Sester
    Year: 2020
  • Exploring Herrenhausen Gardens
    Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience
    Led by: Feuerhake, Sester
    Year: 2023
  • Detection of Signatures in old Maps using Deep Learning
    Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.
    Led by: Thiemann, Sester
    Year: 2023
  • Unveiling the Wireless Jungle
    With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.
    Led by: Schimansky, Golze
    Year: 2023
  • Investigation of the Spatio-Temporal Impact of Traffic Accidents
    Traffic accidents play an important role in our lives in terms of safety and security, especially for people. Everyone is affected by traffic accidents either directly (involved) or indirectly (consequences). Consequences such as traffic jams or road (lane) closures not only disrupt delivery and rush hour traffic, but can also lead to additional accidents. In addition, different types of traffic accidents can have different consequences. For example, an impact could be found in a reduction of the average travel speed on the road or on nearby roads in the time after an accident has occurred. The goal of this thesis is to investigate the impact of traffic accidents based on vehicle trajectories. Therefore, accident and trajectory data need to be linked and the spatial and temporal impact needs to be analyzed.
    Led by: Golze
    Year: 2023
  • Occupancy-free Space Modeling and Navigation Path Planning in a 3D Voxel Grid Environment for Urban Digital Twin Applications
    The urban digital twin is an innovative concept within smart city technology, aiming to develop integrated and intelligent systems by harnessing diverse data from a multitude of sensors. Three-dimensional (3D) geodata plays a pivotal role in the representation and operation of urban digital twins. Tasks such as smart space management and navigation have become increasingly essential in urban digital twin applications, and they can be effectively facilitated using a foundation of 3D geospatial data. Therefore, this master thesis focuses on the modeling of unoccupied space and navigation path planning, employing a 3D voxel grid environment representation. The objective of the thesis is to develop a suitable approach for defining vacant space within urban area, which is utilized to enable collision-free 3D navigation. To achieve this, it is proposed to integrate the point cloud data of the Hannover urban area into a 3D voxel grid structure. In this context, grid cells containing point cloud data are treated as obstacles, while unoccupied cells are collectively constitute the occupancy-free space. The identified vacant space serve as a graph for implementing the shortest path algorithm. Ultimately, both the occupancy-free space and an illustrative route through it are visualized to demonstrate the approach viability.
    Led by: Shkedova, Feuerhake
    Team: Shkedova, Feuerhake
    Year: 2023
  • Development of an approach for integrating various format data into a 3D voxel-based Urban Digital twin
    The advancements in instruments and methodologies for collecting, transmitting, analyzing, and representing three-dimensional (3D) geodata over the past few decades have opened up extensive possibilities for various applications. 3D geoinformation plays a pivotal role in the operational frameworks of Smart City technology that can be represented within an Urban Digital Twin concept. This involves utilizing diverse data from numerous sensors and designing an adaptive digital model that learns from and evolves alongside the real city.
    Led by: Shkedova, Feuerhake, Sester
    Year: 2023
  • Localization of mobile objects in the Absence of GPS/GNSS: A Hybrid 2D-3D Approach
    In today's dynamic landscape of autonomous vehicles and robotics, accurate and real-time localization is imperative. While 3D methods have been employed for vehicle localization, their time-consuming nature poses challenges. This research seeks to a novel hybrid approach, bridging the efficiency of 2D methods with the precision of 3D refinement, to offer a faster and more robust solution for vehicle localization.
    Led by: Mortazavi, Sester
    Year: 2023
  • Evaluating uncertainty estimation techniques using deep leaning models for classification of point clouds
    Deep learning models have proven their ability to perform powerful classification and segmentation considering both 2d and 3D datasets. However, these classifications are often assumed to be accurate, without providing how reliable are these decisions made by the model. There are some possible techniques to convert a stochastic deep learning model into a probabilistic one, to provide a distribution over the weights, instead of finding point estimation for them, leading to an output distribution. Such distribution over output of a model shows not only the true class but also its reliability as the variance of the distribution.
    Led by: Shojaei
    Year: 2023

[uncategorized]

  • Junior research group "Automatic methods for the fusion, reduction and consistent combination of complex, heterogeneous geoinformation."
    The automated capture of three-dimensional models is a key topic of the junior research group at the ikg. The main focus is on images and laser scan data. Laser scanning is a relatively new technique which allows to measure tens of thousands of points in a matter of seconds. It can be done from the ground (terrestrial scanning) or based on aerial platforms (helicopter, fixed wing aircraft).
    Led by: apl. Prof. Claus Brenner
    Team: Dr.-Ing. Nora Ripperda, Dr.-Ing. Christoph Dold
    Year: 2017
  • Update of Digital Terrain Models and Adjustment with ATKIS DLM Vector Data
    A target of this research cooperation is to develop methods to update the ATKIS DGM5 automatic. Further we try to adjust the terrain model with the landscape model (BasisDLM) and we are looking for a generalization method for the terrain model to make it usable together with landscape data for small scale maps. It is necessary to detect area that are out of date. In this areas we use special methods to update the terrain and landscape model. For this it is important to match the two-dimensional representation of the topography and the terrain model.
    Year: 2017
  • Automatic Geometric Registration of Legacy Maps
    This research aims at solving automatically the geometric registration of point-features that exist in analogue legacy map and digital topographic database. The aspiration of this process is to replace the commonly used manual geo-referencing, and thus enabling the matching of the legacy map to a more updated and precise dataset, and hence enhancing it geometrically and topologically.
    Year: 2017
  • Data integration and fusion, data quality
    Heterogeneous data poses a challenge for Spatial Data Infrastructures. Data being acquired from different providers using different modeling schemes or scales has to be conflated into a single consistent dataset. As part of the Spatial Data Infrastructure Grid (SDI-Grid) open Web Processing Services (WPS) are being developed for integration, fusion, annotation and quality evaluation of geospatial data.
    Year: 2017