Dr. techn. Jie Zhao
| Room: | 020 (TUM Ottobrunn Campus) |
| E-mail: | jie.zhao(at)tum.de |
| More information: | Google Scholar Research Gate |
Curriculum Vitae
- 12/2022 - present, TUM, Post-doc
- 01/2022 - 09/2022, Technische Universität Wien, Project Assistant
- 09/2017 - 12/2021, Luxembourg Institute of Science and Technology, PhD student
- 04/2016 - 10/2016, DLR, Research Assistant
Research Interests
- SAR
- Deep Learning
- Large-scale Flood Mapping
- Change Detection
- Flood Resilience
Key Publications
- Zhao, J., Pelich, R., Hostache, R., Matgen, P., Wagner, W., & Chini, M. (2021). A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case. Remote Sensing of Environment, 256, 112338.
- Zhao, J., Pelich, R., Hostache, R., Matgen, P., Cao, S., Wagner, W., & Chini, M. (2021). Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping. Remote Sensing of Environment, 265, 112668.
- Zhao, J., Li, Y., Matgen, P., Pelich, R., Hostache, R., Wagner, W., & Chini, M. (2022). Urban-aware U-Net for large-scale urban flood mapping using multitemporal Sentinel-1 intensity and interferometric coherence. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-21.
- Zhao, J., Xiong, Z., & Zhu, X. X. (2024). UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 419-429).
- Zhao, J., Li, M., Li, Y., Matgen, P., & Chini, M. (2025). Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A review of characteristics, approaches, and datasets. IEEE Geoscience and Remote Sensing Magazine, 13(1), 237-268.