Tag: indoor mapping

  • Inferencing indoor room semantics using random forest and relational graph convolutional networks (deep learning)

    Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but often still neglect room usage. To mitigate the issue, a new published paper […]

  • Feasibility of Using Grammars to Infer Indoor Room Semantics

    Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. A recently published paper investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute […]

  • GIScience group members at the ISPRS Geospatial Week 2017 in Wuhan, China

    Last week (Sept. 18-22, 2017), our six colleagues, Prof. Alexander Zipf, Doctoral Candidate Xuke Hu, Dr. Hongchao Fan, Dr. Martin Hämmerle, Dr. Zhiyong Wang, and Dr. Wei Huang, participated in the ISPRS Geospatial Week 2017 held in Wuhan, China. In the opening ceremony on Sept. 18, 2017, the U.V. Helava Award was presented to Dr. […]