Book Chapters

• ESCAP United Nations. (2023). Geospatial practices for sustainable development in Asia and the Pacific 2022: A compendium. United Nations publication. (Research support) [Link]

Journal Publications 

* -- indicates the corresponding author

# -- indicates advisor as first author, myself as second author

• Ma, P., Yu, C., Jiao, Z., Zheng, Y., Wu, Z.*, Mao, W., & Lin, H. (2024). Improving time-series InSAR deformation estimation for city clusters by deep learning-based atmospheric delay correction. Remote Sensing of Environment, 304: 114004. [Link]

• Wu, Z., Ma, P., Zhang, X., & Ye, G. (2024). Efficient management and processing of massive InSAR images using an HPC-based cloud platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 2866-2876. [Link]

• Ma, P., Wu, Z.#, Zhang, Z., & Au, F. T. (2024). SAR-Transformer-based decomposition and geophysical interpretation of InSAR time-series deformations for the Hong Kong-Zhuhai-Macao Bridge. Remote Sensing of Environment, 302: 113962. [Link]

• Zhu, Q., Guo, H., Zhang, L., Liang, D., Wu, Z., de Roda Husman, S., & Du, X. (2024). Automated surface melt detection over the Antarctic from Sentinel-1 imagery using deep learning. International Journal of Applied Earth Observation and Geoinformation, 130: 103895. [Link]

• Ma, P., Yu, C., Wu, Z., Wang, Z., & Chen, J. (2024). Mining-Related Subsidence Measurements Using a Robust Multi-Temporal InSAR Method and Logistic Model. IEEE Journal on Miniaturization for Air and Space Systems. [Link]

• Wu, Z., Ma, P., Zheng, Y., Gu, F., Liu, L., & Lin, H. (2023). Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN. Remote Sensing of Environment, 290: 113545. [Link] [ESI Highly Cited Paper]

• Wu, Z., Zhang, X., Cai, J., Ma, P., & Kwan, M.-P. (2023). Understanding spatially non-stationary effects of natural and human-induced factors on land subsidence based on InSAR and multi-source geospatial data: A case study in the Guangdong-Hong Kong-Macao Greater Bay Area. International Journal of Digital Earth, 16(2): 4404-4427. [Link]

• Wu, Z., Zhang, X., Ma, P., Kwan, M.-P, & Liu, Y. (2023). How did urban environmental characteristics influence land surface temperature in Hong Kong from 2017 to 2022? Evidence from remote sensing and land use data. Sustainability, 15(21):15511. [Link]

• Zhu, Q., Guo, H., Zhang, L., Liang, D., Wu, Z., & Gou, Y. (2023). GLA-STDeepLab: SAR enhancing glacier and ice shelf fronts detection using Swin-TransDeepLab with Global-Local attention. IEEE Transactions on Geoscience and Remote Sensing, 61: 1-13. [Link]

• Ma, P., Zheng, Y.,Zhang, Z., Wu, Z., & Yu, C. (2022). Building risk monitoring and prediction using integrated multi-temporal InSAR and numerical modeling techniques. International Journal of Applied Earth Observation and Geoinformation, 114: 103076. [Link]

• Liu,Y., Kwan, M.-P., & Wu, Z. (2022). Visualizing and quantifying the spatiotemporal expansion of the Blue Lentic Belt in Alabama and Mississippi. Water Research, 217: 118444. [Link]

• Wu, Z., Zhao, Z., Ma, P., & Huang, B. (2021). Real-world DEM super-resolution based on generative adversarial networks for improving InSAR topographic phase simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 8373-8385. [Link]

• Zhao,Z., Wu, Z., Zheng, Y., & Ma, P. (2021). Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values. ISPRS Journal of Photogrammetry and Remote Sensing, 180: 227-237. [Link]

Peer-reviewed Conference Papers

• Wu, Z., & Ma, P. (2023). Deep learning of InSAR time-series signals for assessing the impacts of geotechnical, meteorological, and marine conditions on cross-sea bridge deformations. In American Geophysical Union (AGU) Fall Meeting 2023, San Francisco, United States, 11-15 December 2023. [Link]

• Wu, Z., Zhao, Z., Zheng, Y., & Ma, P. (2021).Automatic detection of widely distributed local-scale subsidence bowls in rapidly urbanizing metropolitan region using time-series InSAR and deep learning methods. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11-16 July 2021. [Link]

• Wu, Z., & Ma, P. (2020). ESRGAN-based DEM super-resolution for enhanced slope deformation monitoring in Lantau island of Hong Kong. In the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43,351-356, Nice, France, 14–20 June 2020. [Link]

Patents

• Ma, P., Wu, Z., Zheng, Y., Complex scene deformation monitoring and classification method based on InSAR and deep learning self-attention model (No. CN202311557560.2)

• Ma,P., Wu, Z., DEM super-resolution methods and equipment (No. CN202011641326.4).

• Ma, P., Wu, Z., Zheng, Y., Yu, C., Automatic identification and classification method of urban ground subsidence (No. CN202310237618.9).

• Ma, P., Zheng, Y., Zhang, Z., Wu, Z., Yu, C., An InSAR Assessment and Prediction Method for Risk Levels of Urban Buildings (No. CN202211320799.3).