Former Projects

Semantic Data Mining for OSM Building Layer

Duration: 2022 - 2023

 Contact: Leul Kahsay/ Peng Luo

One of the fundamental supporting technologies of AR map is to build a complete, accurate, consistent and up-to-date 3D city model dataset. The future success of the AR map application relies on the capability of extending its coverage to city-level and even larger areas. For the moment, the major data sources for the ultimate experience areas include LiDAR point clouds, aerial imagery and panoramic images. The data collection is costly and requires large amount of manual work. While for the city-level 3D model construction, one of the approaches is the use of stereo satellite imagery. This method is able to provide the global coverage, but has a relatively long repetition cycle (more than three years) and is severely affected by weather conditions. Besides, its cost per square kilometer is also higher than for normal satellite imagery because it has to be in stereo. This project funded by industry partner addresses the scalability of single-scene SAR imagery and VGI building footprints; and the comprehensibility of historical building attribute data and land use data for the creation of 3D city models.

Mixed Reality-based Indoor Navigation and Spatial Learning

Duration: 2019 - 2022

Contact: Bing Liu

Modern people spend most of their time indoors, and they move a lot within closed spaces. Indoor navigation is an integral part of our life. The indoor navigation applications are much more limited compared with outdoor. The main reason is the difficulty of getting stable GNSS signals. The quickly developing mixed reality (MR) technology performs well in indoor spatial mapping and indoor positioning. Head-mounted MR is highly potential in indoor navigation, there are risks that the wrong virtual information misleads the users or the perception of the physical world is decreased. The users’ perception and usage preferences would improve usability and accelerate the maturing of MR-based indoor navigation. In this ongoing project funded by China Scholarship Council, we collect the ordinary users’ attitude toward using head-mounted MR for navigation, explore how MR-based navigation influence spatial learning and find ways to improve its usability.

Map-based Dashboard

Duration: 2018 - 2022

Contact: Chenyu Zuo

Map-based dashboards are among the most popular tools that support the viewing and understanding of a large amount of geo-data with complex relations. In spite of many existing design examples, little is known about their impacts on users and whether they match the information demand and expectations of target users. We first designed a novel map-based dashboard to support their target users' spatiotemporal knowledge acquisition and analysis, and then conducted an experiment to assess the feasibility of the proposed dashboard. The experiment consists of eye-tracking, benchmark tasks, and interviews. Forty participants were recruited for the experiment. The results have verified the effectiveness and efficiency of the proposed map-based dashboard in supporting the given tasks. At the same time, the experiment has revealed a number of aspects for improvement related to the layout design, the labeling of multiple panels and the integration of visual analytical elements in map-based dashboards, as well as future user studies.

Semantically Enriched and User Orientated Multi-Modal Navigation

Duration: 2016 - 2020

Contact: Christian Murphy, Edyta Bogucka, Linfang Ding

The Federal Ministry of Transport and Digital Infrastructure (BMVI) leads a data-oriented R&D- funding programme for the period 2016 to 2020 in a form of the modernity fund (mFUND). Within this programme, Chair of Cartography TUM conducts a research on early developments of digital innovations in mobility. Ongoing project aims to show the added value of extending the existing traffic data with semantic information. The pre-study examines the possibilities of (1) the enrichment of the multimodal traffic database with semantic information to be gained from user behaviour data, Volunteered Geographic Information (VGI) and social media and (2) development of the user-oriented multimodal navigation service. As a result, a demo app version will be developed to present the following scenarios relevant for the urban and mobility design: multiscale representation of transportation nodes, automatic detection of the smombie danger and managing hotspots of negative events in the cities. Semantically enriched, multimodal and user-oriented navigation service will be evaluated in two test areas of Berlin and Munich.

Geospatial Information Services for Smart Cities Driven by Big Data

Durtaion: 2017 - 2020

Contact: Edyta Bogucka, Linfang Ding

Partners: Yangtze River Delta Science Data Center, Changshu Fengfan Power Equipment Co., Ltd

Within this cooperation, Chair of Cartography TUM conducts a research on integrating multiple sources of geo-referenced urban data to derive valuable geospatial services for smart city applications. As a result, an open geo-collaborative portal with a set of interactive services will be developed. The portal will contain an interactive user interface, an extendable visualization toolkit and an analytical toolkit. The anticipated components will serve as descriptive, diagnostic, predictive and prescriptive tools for city management. The functionalities of the portal will be developed during the series of workshops and user tests with three target groups – urban citizens, domain experts and decision makers. Each target group will participate in the knowledge construction process in a two-fold role – as information receiver and data contributor.

A Visual Computing Platform for the Industrial Innovation Environment in Yangtze River Delta

Duration: 2018 - 2020

Contact: Chenyu Zuo, Linfang Ding

Partners: Jiangsu Industrial Technology Research Institute (JITRI), Yangtze River Delta Science Data Center, School of Geography - Nanjing Normal University

The aim of the joint project is to develop a visual computing platform dedicated to monitoring the dynamic innovation and investment ecosystem in Yangtze River Delta. The platform combines the power of intuitive human vision with that of analytical computing, thus serves as an enabler for users to explore the interactions between the regularly updated geo-infrastructure data and the continuously evolving geo-economic data, and to preview the complex influence factors of industrial innovation. The anticipated platform will be prototypically implemented with three extendable components – a geovisualization toolkit, a geospatial analytical computing toolkit and a geo-economic event collector. These components will be tailored to the needs and knowledge profiles of three target groups who are involved or interested in the innovation ecosystem in Yangtze River Delta – governmental agencies, industrial enterprises and investors.

Sense-Making Image Mapping from Remotely Sensed GLC30m

Contact: Ekaterina, Chuprikova

This joint project between the Lehrstuhl für Kartographie at TUM (TUM-LfK) and the National Geomatics Centre of China (NGCC) aims to visually empower the uniquely available GLC30m (global land cover of 30m resolution) at NGCC with the uniquely available attentionguiding design framework of concise image maps at TUM-LfK. Both partners are committed to developing a globally accessible open-source platform for GLC30m. The platform will provide a metadata catalogue and (semi)automatic value-adding services to enable the continuous validation, updating and efficient use of GLC30m incl. visual query, visual narratives of query results and creation of web-based image maps for selected applications and target groups. Across the fields of remote sensing, geoinformatics, visual perception and neuropsychology and cartography, innovative methods are anticipated to (a) interlace the first-hand information from raster images with the second-hand information from map symbols and labels at multiple visual levels rather than just a figure-ground composition; (b) unite the pixel resolution reflecting the degree of ground-truth with the map scale reflecting the cognitive abstraction in the visual storytelling; (c) convert the globally accessible land cover types into personalized visualizations for efficient data understanding.

Event detection and visualization of Volunteered Geographic Information (VGI)

Contact: Polous, Khatereh

With rapid spread concept that uses web as “participatory platform”, assessing spatio-temporal processes and detailed change mapping, which highly demand accurate and up-to-date data, has become more affordable. Many studies have been already conducted for detection, monitoring and visualization of changes from multi-temporal, multi-spectral and multi-sensor data. But, it is less discussed how the detected changes should be decomposed and formulated to reveal an event. The aim of this study is to detect location-based events from Volunteered Geographic Information (VGI) in Munich. The work concentrates on detection and pattern recognition of events, which are bound to a specific time and place from delivered information by internet users.

Analysis and Conflation of Road Networks in Digital Maps for Automotive Applications

Contact: Andreas, Hacklöer

A joint effort with BMW Research and Technology, this project investigates methods for the analysis and conflation of road networks. Modern car navigation services rely on vector-oriented road models embedded in digital maps. In order to identify a given road network structure across multiple maps, a matching and conflation process is required which determines a projection from one road network to another, thereby providing a map-agnostic means of identifying geographical references to entities describing road networks. In the project, we develop, investigate and evaluate road network matching techniques for digital vector maps which analyze geometrical, topological and semantic information to derive assignments between road networks on several abstraction levels, ranging from point-to-point correspondencies to structural high-level matchings. The resulting algorithms represent an enabling technology for comparison, fusion, attribute transfer and quality evaluation of digital maps.

Spatial Optimization of Railway Infrastructure Maintenance for Travel Time Saving

Contact: Jian Yang

In this ongoing project, we study the problem by how can we reduce minute-level running time of passenger trains on a given railway route through maintenance instead of costly construction work like changing the existing railway alignment. This interdisciplinary project leverages the expertise not only from cartography but also from railway infrastructure construction, transportation planning and computer science. So far, we’ve established a five-step semi-automated process including data preprocessing, timesaving potential estimation, maintenance site selection, site inspection and planning result reporting and also developed a prototype program tool based on our method to facilitate decision makers on maintenance planning.

Comparative study of thematic mapping and scientific visualization

Contact: Linfang, Ding

Visualization of 4-D Building Deformation from High-resolution SAR Data — The project is dedicated to visualize 4-D space-time building deformation datasets retrieved from high resolution satellite SAR images, which are crucial to monitor building behavior as well as detect potential damages in urban areas. Based on the data characteristics, appropriate design methods and visualization techniques should be identified from disciplines like scientific visualization and thematic mapping, which may allow users to explore detailed deformation information as well as to perceive the overall building deformation patterns immediately.

Lightning data analysis

Contact: Stefan, Peters

This research project  focus on the visual analysis of lightning data. Starting point are 3D coordinates and the exact occurrence time of lightning data. In a first step lightning cells are identified and tracked. Then an interactive graphic user interface is developed to investigate the dynamics of the lightning cells: e.g. changes of cell density, location, extension as well as merging and splitting in 3D over time. Furthermore a statistical analysis is provided. The second part of this research contains a short term forecasting of lightning cells and the visualization of its uncertainty. The visual exploring tools are investigated for two determined user groups: lightning experts and interested lay public.

Multi-dimensional visualization of spatio-temporal data

Contact: Christian Murphy

Space-Time Mapping of Mass Event Data ­­— To simultaneously visualize thematic data in space and time a third dimension must be added. In this work conventional cartographic symbolization meets the space-time cube to create a holistic three dimensional spatio-temporal visualisation model. The two dimensional proportional symbol mapping technique is adopted and extruded into the third dimension to model the temporal factor. Kernel density estimation is performed on the time line to create a temporal continuous model from discrete points in time. The resulting visualization model is implemented into an earth viewer to enable the user to freely navigate and animate the phenomenon and visually detect spatio-temporal anomalies without losing the overall view. This tool is evaluated by visualizing the events of a mobile phone location dataset over space and time in one single model.

Multi-dimensional visualization of spatio-temporal data

Contact: Mathias Jahnke

To support experts as well as non-experts in their decisions is one main goal of the geospatial domain. The non-photorealistic visualization offers a new way of a user oriented cartographic communication on small mobile displays. In particular 3D city models presentations can profit from this approach because most of the city model visualizations do not tap the full potential of a combined geometric, semantic information presentation. The non-photorealistic approach of abstracted information reduced visualization seems to be feasible for a combination with cartographic design concepts and offers more degrees of freedom to add semantic information particularly on small mobile devices. To reach this goal on one hand the theories and method from traditional 2D cartography have to be expanded for the usage in the third dimension on the other hand the user have to be taken into account. The user is of main interest when developing new visualization styles because he is the only person who can give valid feedback about the usability of such new visualization styles.

2D and 3D Thermal facade data visualization

Contact: Holger Kumke

The project is dedicated to the visual enrichment of thermal data on building facades in urban area. Heat radiation or thermogram which can be detected and measured by physical instruments shows invisible thermal information and indicates the state of the building surface. It serves as raw data for the design of thermal façade maps on planar display surface as well as 3D space-based map-related depictions. Similar to the digital thematic map design, the thermal maps are conceived for the output on screens. Their special characteristics, however, have opened up many innovative visualization alternatives.

Location-Based Services & indoor navigation

Contact person: Jukka Krisp

Location-Based Services (LBS) are investigated from different perspectives that include mobile positioning and tracking technologies, data capturing and computing devices, integrated software engineering, user studies for various applications. The enabling sensory technologies and interactive open-source platforms are continuously changing the way of our thinking and living and reshaping the research scope of LBS. LBS is an integrative discipline that unites research ideas from related fields. Technical challenges still occur on the indoor navigation data acquisition, the path computation and the communication of a potential indoor path to the user.

A Congruent Hybrid Model for Conflation of Geo-Referenced Image and Road Network

Contact person: Jiantong Zhang  

In the project, we investigate a novel Congruent Hybrid Model(CHM) to rectify the misalignments between road vectors and geo-referenced images. The matching cost between the extracted road centerlines and the prior road network was optimized using Sparse Matching Algorithm (SMA) to get the optimal correspondence, and then the road segments were transformed to its partner using two frequently transformation functions — the piecewise Rubber-Sheeting (RUB) approach and the Thin Plate Splines (TPS) approach. The experiments with synthesized data as well as the real spatial data sets have verified the efficiency of CHM. The snake-based approach is a natural subsequence of the presented model. However, the CHM can be also directly employed for geospatial visualization applications