Dr. Hongjie He

Postdoctoral Fellow
dr hongjie he

Email: h69he@uwaterloo.ca

Office: EV1-345

Research Interest

Data-driven Modeling and Analysis of Urban Infrastructure in Response to Climate Change

Climate change exacerbates concerns related to sustainable urban infrastructure, impacting the economy, quality of life for the population, and the environment. Urban infrastructure extends beyond the realm of engineered facilities, utilities, and systems. It serves as a nexus for both local and global governance, intricately linking aspects of economic development, climate change, and the management of municipal waste. Climate change exacerbates concerns related to sustainable urban infrastructure, impacting the economy, quality of life for the population, and the environment. Urban infrastructure serves as a nexus for both local and global governance, intricately linking aspects of economic development, climate change, and the management of municipal waste. For urban infrastructure monitoring and urban management, as well as city planning, the use of automated techniques to generate high-definition (HD) maps of modern urban infrastructures with precision at the cm-level is immensely beneficial in managing the increasing complexity of these systems. Another notable instance involves the development of HD maps tailored for autonomous driving.

My research focuses on the development of AI-based mapping technologies for the generation of HD maps using high-resolution earth observation (EO) imagery and AI technology to support autonomous driving and urban digital twins. Specifically, my research will involve in the development of high-quality urban scene datasets, advanced single-image super-resolution methods to enhance the spatial resolution of EO imagery, and novel deep learning networks for multi-class classification capable of improving performance on inter-class boundary classification and mitigating scale variance challenges.

Education:

  • PhD in geography specializing in remote sensing, University of Waterloo, 2023  
  • MSc in Cartography and Geographic Information Systems, Lanzhou University, 2019
  • BEng in Geomatics, China University of Petroleum, 2016

Selected Publications:

  1. He, H., Gao, K., Tan, W., Wang, L., Chen, N., Ma, L. and Li, J., 2022. Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network. International Journal of Applied Earth Observation and Geoinformation, 111, 102826.
  2. He, H., Xu, H., Zhang, Y., Gao, K., Li, H., Ma, L. and Li, J., 2022. Mask R-CNN based automated identification and extraction of oil well sites. International Journal of Applied Earth Observation and Geoinformation, 112, 102875.
  3. He, H., Jiang, Z., Gao, K., Fatholahi, S., Tan, W., Hu, B., Xu, H., Chapman, M.A. and Li, J., 2022. Waterloo building dataset: a city-scale vector building dataset for mapping building footprints using aerial orthoimagery. Geomatica, 75(3), 99-115.
  4. He, H., Yang, K., Wang, S., Petrosians, H.A., Liu, M., *Li, J., Marcato Junior, J., Gonçalves, W.N., Wang, L. and Li, J., 2021. Deep learning approaches to spatial downscaling of GRACE terrestrial water storage products using EALCO model over Canada. Canadian Journal of Remote Sensing, 47(4), 657-675.
  5. He, H., Yang, K., Cai, Y., Jiang, Z., Yu, Q., Zhao, K., Wang, J., Fatholahi, S.N., Liu, Y., Petrosians, H.A. and Hu, B., 2021. The impact of data volume on performance of deep learning-based building rooftop extraction using very high spatial resolution aerial images. In: 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1343-1346.
  6. He, H., Xu, H., Zhang, Y., Chapman, M.A., Chen, Y. and Li, J., 2022. Automated detection of oil/gas well sites detection from multi-source high spatial resolution images. In: 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4615-4618.
  7. He, H., Gao, K., Tan, W., Wang, L., Fatholahi, S.N., Chen, N., Chapman, M.A. and Li, J., 2022. Impact of deep learning-based super-resolution on building footprint extraction. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, pp. 31-37.