At ScAI, we devise machine learning models to enhance our comprehension of the dynamic transformations occurring on our planet, including the intricate interplay of weather systems and land surface evolution. Our work is applied on a diverse set of applications, including urban mapping, agricultural forecasting, forestry, flood forecasting, and extreme weather event forecasting.
Earth Observation
ScAI researchers develop AI-based methods for various Earth observation applications, e.g., urban monitoring and forestry applications.
Flood forecasting
ScAI researchers combine AI and hydrology techniques to detect and forecast floods.
Weather/climate data analysis
ScAI researchers are working on extreme weather forecasting and generative modeling of climate data.
Faculty Members Currently Involved
Sudipan Saha
Hariprasad Kodamana
Manabendra Saharia
Sandeep Sukumaran
Key Publications
- Gawlikowski, J., Saha, S., Niebling, J. and Zhu, X.X., 2023. Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification. EURASIP Journal on Advances in Signal Processing, 2023(1), p.47.
- Kuzu, R.S., Bagaglini, L., Wang, Y., Dumitru, C.O., Braham, N.A.A., Pasquali, G., Santarelli, F., Trillo, F., Saha, S. and Zhu, X.X., 2023. Automatic Detection of Building Displacements through Unsupervised Learning from InSAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
- Kuntla, S.K., Saharia, M. and Kirstetter, P., 2022. Global-scale characterization of streamflow extremes. Journal of Hydrology, 615, p.128668.
- Saharia, M., Kirstetter, P.E., Vergara, H., Gourley, J.J., Emmanuel, I. and Andrieu, H., 2021. On the impact of rainfall spatial variability, geomorphology, and climatology on flash floods. Water Resources Research, 57(9), p.e2020WR029124.