Hsiao-Jou (Amy) Hsu is a Ph.D. candidate in Geodetic Science at The Ohio State University. Her research focuses on developing physics-informed and explainable artificial intelligence methods for satellite-derived shallow-water bathymetry and coastal remote sensing. She integrates machine learning, multispectral satellite imagery, airborne LiDAR, and physical models of underwater light propagation to improve the transferability, interpretability, and reliability of bathymetric mapping across diverse coastal environments.

Her broader research interests include geospatial AI, uncertainty quantification for geophysical inverse problems, multi-temporal satellite data fusion, bathymetric change detection, and open-source Earth observation tools. She holds degrees from The Ohio State University and National Central University in Taiwan and previously studied geography and geographic education at National Taiwan Normal University.

Her work has been published in leading remote sensing journals, including the ISPRS Journal of Photogrammetry and Remote Sensing and Remote Sensing. She has also presented her research at major international conferences, including the AGU Fall Meeting, AAAS Annual Meeting, EGU General Assembly, IAG Scientific Assembly, EARSeL Symposium, and the IUGG General Assembly. She is the recipient of the 2026 Michael Johnson Graduate Student Award and the 2024 Geodetic Science Book Award from The Ohio State University.

Amy is also affiliated with the BuckAI Observatory at The Ohio State University, where she contributes to interdisciplinary research at the intersection of artificial intelligence and Earth system science. In 2026, she was featured in an AGU TV segment highlighting applications of artificial intelligence in Earth and environmental sciences. She is the first Ph.D. student affiliated with the BuckAI Observatory, a new center of excellence in applied AI at The Ohio State University that promotes interdisciplinary research in artificial intelligence and scientific discovery. 
 

Peer-Reviewed Journal Articles

Hsu, H.-J., Tseng, K.-H., Tsai, F., Liu, C.-L., Lo, C.-C., & Moortgat, J. (2026). A novel approach to automated cloud removal and seamless multisensor satellite image mosaicking. Applied Computing and Geosciences, 31, 100387. https://doi.org/10.1016/j.acags.2026.100387

Hsu, H.-J., & Moortgat, J. (2026). From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry. Remote Sensing, 18(11), 1768. (SCIE)  [doi]

Roy Chowdhury, S., Radhakrishnan, A., Hsu, H.-J., Subramoni, H., & Moortgat, J. (2026). From Bands to Depth: Understanding Bathymetry Decisions on Sentinel-2. arXiv:2601.12636  [preprint]

Hsu, H.-J., Huang, C.-Y., Jasinski, M., Li, Y., Gao, H., Yamanokuchi, T., Wang, C.-G., Chang, T.-M., Ren, H., Kuo, C.-Y., & Tseng, K.-H. (2021). A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: A case study in the South China Sea. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 1–19. (SCIE, IF 2021: 11.77) DOI: 10.1016/j.isprsjprs.2021.05.012

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Advisor- Joachim Moortgat