Semantic Data Mining for OSM Building Layer
Begin: since 2022.04
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.
Data-driven exploration and explanation of ethics cases in business world
Begin: since 2021.09
Contact: Chuan Chen / Mengyi Wei
This project funded by industry partner addresses the ethical issues in business world from a data scientific perspective. The ethics cases in big data are mainly reflected in narratives of conflicts, negotiation contents, trial processes and results, professional comments, public opinions, analytical judgments, and constantly updated lists of "should do" and "should not do". However, without AI support, the human brain alone cannot see through the intricate and varying strengths and weaknesses of connections among the elements of a case or among different cases. In this project we attempt to develop an interactive visual analytical platform that can combine the data-driven deep learning ability with knowledge-driven human's reasoning and interpretation ability. The platform will enable us to perform tasks along a value chain such as automatic collection of ethics cases from the globally accessible public media and social media, analysis and clustering of collected data, creation of a specific ontology and knowledge graph, and visual explanation of the knowledge graph. Being driven by big data, the platform may serve the general objectives for different stakeholders in the business world.
Situated Geovisualization Based on Mixed Reality
Begin: since 2020.12
Contact: Shengkai Wang
Limited spatial information obtained from the real world leads to poor human spatial memory and navigation performance. Mixed reality (MR) enriches accessible information and improves the interaction experience of users' surroundings, reflecting great potential in improving human spatial cognition in recognizing, understanding, memorizing, retrieving, and representing. However, factors like spatial scale and information complexity may have impacts on the usability of MR-based visualizations and thus cause distortion and loss of spatial memory. This project addresses the human spatial cognition issues from the perspective of visualization based on MR. Our research is to explore the behaviors of users, uncover the impacts of visual and spatial factors on usability, and develop an MR-based visualization and visual analysis platform for improving human spatial cognition.
Spatial association and GeoAI
Begin: since 2020.10
Contact: Peng Luo
This project aims to deepen our understanding of spatial association and develop the new Geospatial Artificial Intelligence model (GeoAI). First, this project explores a new way to understand spatial association apart from spatial heterogeneity and spatial autocorrelation. Second, this project attempt to introduce a new understanding of spatial association into AI algorithms. The developed new GeoAI models will be applied to urban computing, social sensing, spatial optimization, population mapping, and other fields.
Mixed Reality-based Indoor Navigation and Spatial Learning
Begin: since 2019.01
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.
A climate event portal for knowledge discovery
Begin: since 2018.5
Partners: Bayerische Klimaforschungsnetzwerk (BayKlif): Prof. Dr. Annette Menzel, Technische Universität München, Prof. Dr. Dieter Kranzlmüller, Leibniz-Rechenzentrum, Prof. Dr. Susanne Jochner-Oette, Katholische Universität Eichstätt-Ingolstadt, Prof. Dr. Jörg Ewald, Hochschule Weihenstephan-Triesdorf, Prof. Dr. Wolfgang W. Weisser, Technische Universität München, Prof. Dr. Ulrike Ohl, Universität Augsburg, Prof. Dr. Arne Dittmer, Universität Regensburg, Prof. Dr. Henrike Rau, Ludwig-Maximilians-Universität München
The PhD or post doc researcher will be working with multidisciplinary teams in a Research Cluster on “Bavarian Synthesis Information Citizen Science Portal for Climate research and Scientific Communication” (BAYSICS - Bayerisches Synthese-Informations-Citizen Science Portal für Klimaforschung und Wissenschaftskommunikation).
Project webpage: https://www.bayklif.de/verbundprojekte/baysics/teilprojekt-3/