Dr. Hao Li

Research Interests

• volunteered geographic information (VGI)
• geo-semantics
• deep learning and machine learning
• remote sensing

Curriculum Vitae

• 09/2015-02/2018: Master of Science in Geomatics Engineering, University of Stuttgart, Germany
• 07/2017-01/2018: Master thesis student, Signal Processing in Earth Observation Group, Techinical University of Munich & German Aerospace Center (DLR), Germany
• 09/2012-07/2015: Bachelor of Science (double degree) in Computer Science, Wuhan University, China
• 09/2011-07/2015: Bachelor of Science in Geographic Information System, Wuhan University, China

Projects

• DeepVGI: Deep Learning with Volunteered Geographic Information

Publications

• Li, H., Zech, J., Hong, D., Ghamisi, P., Schultz, M., Zipf, A. (2022): Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection. International Journal of Applied Earth Observation and Geoinformation, Volume 110, June 2022, 102804, https://doi.org/10.1016/j.jag.2022.102804
• Hu, X., Noskov, A., Fan, H., Novack, T., Li, H., Gu, F., Shang, J. & Zipf, A. (2021): Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2020.1861282
• Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A.(2020): A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 2020, 12, 2067. DOI: https://doi.org/10.3390/rs12122067
• Wu, Zhaoyan, Li, Hao, & Zipf, Alexander. (2020): From Historical OpenStreetMap data to customized training samples for geospatial machine learning. In proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. DOI: http://doi.org/10.5281/zenodo.3923040