End of last week Dr. Xuke Hu successfully defended his PhD thesis entitled “Building Semantics Reasoning by Using Rules based on Available Geospatial Information“.
His dissertation investigated the potential of inferring distinct indoor and outdoor spatial (specifically building) elements based on existing or available spatial elements on OSM or provided by sensing equipment, leveraging the association relationship between the spatial elements. Furthermore, this dissertation compared and explored the applicability of two kinds of reasoning mechanisms using manually defined explicit rules (Knowledge driven) and learned implicit rules (data driven) in this context, respectively. Four representative indoor and outdoor building elements (i.e., roof shape, room usage, main entrance, and landmark salience) were taken as examples to explore how and why the four building elements can be inferred by explicit and/or implicit rules. Finally, the results of the four studies were combined to answer the questions related to the research objectives.
The PhD consists of a introduction and synopsis and the following five journal paper:
- Hu, X., Fan, H., Noskov, A. (2018): Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints. International Journal of Digital Earth. Vol. 11(10), pp.1039-1063, Doi: 10.1080/17538947.2017.1373867.
- Hu, X., Noskov, A., Fan, H., Novack, T., Gu, F., Li, H., Shang, J.: Tagging the Buildings’ Main Entrance based on OpenStreetMap and Binary Imbalanced Learning. (revision)
- Hu, X., Fan, H., Noskov, A., Zipf, A., Wang, Z., Shang, J. (2019). Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing. vol. 11(13), p.1535. Doi: 10.3390/rs11131535.
- Hu, X., Fan, H., Noskov, A., Wang, Z., Zipf, A., Gu, F., Shang, J. (2020 accepted). Room Semantics Inference Using Random Forest and Relational Graph Convolutional Network: A Case Study of Research Building. Transactions in GIS. (accepted)
- Hu, X., Ding, L., Shang, J., Fan, H., Novack, T., Noskov, A., Zipf, A. (2019). A Data- driven Approach to Learning Saliency Model of Indoor Landmarks by Using Genetic Programming. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2019.1701109
Because of the corona pandemic the external reviewer Prof. Dr. WenWen Li from Arizona State University unfortunately could only participate virtually. Originally she had planned to stay her sabbatical this semester with us at the GIScience Research Group Heidelberg University.
We congratulate Xuke most cordially for this achievement! We are looking forward to further joint work. All the best at your new job at DLR Data Science Institute, Jena!
