Rohan Putatunda successfully defends his PhD Proposal

• Dr. Vandana P. Janeja, (Chair)
• Dr. Sanjay Purushotham, (Co-Chair)
• Dr. Rebecca Williams
• Dr. Zhiyuan Chen
• Dr. SUDIP CHAKRABORTY
  

Ice calving, the process where large ice masses detach from a glacier’s terminus, is a significant driver of ice mass loss and contributes to global sea-level rise. In ice calving, ice calving fronts refer to the terminus regions where these events occur, often producing icebergs as large as Manhattan, which eventually fragment into smaller pieces, or "chicklets." These chicklets, influenced by environmental forces such as ocean currents and winds, follow complex trajectories that can disrupt maritime routes. Although traditional methods for monitoring ice-calving fronts rely heavily on manual reviews of time series satellite imagery, they are time-intensive, prone to human error, and lack scalability. Recent advances in deep learning have introduced automation in segmenting ice-calving fronts, but critical challenges such as predicting future calving front positions and forecasting chicklet trajectories remain underexplored. This thesis addresses three fundamental challenges in understanding and predicting ice-calving processes. First, it tackles the challenge of accurate segmentation of ice calving fronts, characterized by sparse pixel representation, through SEATTNET, a novel hybrid attention model that combines squeeze-and-excitation (SE) blocks with spatial attention gates to enhance feature representation and segmentation accuracy. Second, it explores forecasting future ice-calving front position, where the absence of explicit spatiotemporal data and the nonlinear nature of latitude-longitude temporal sequences present significant challenges. To address these, a georeferenced dataset derived from segmentation masks is utilized alongside the GlaSpectra model, which employs spectral convolution layers along with FFT and IFFT to capture global and local spatial relationships, enabling precise trajectory forecasting. Finally, this thesis also proposes to tackle the challenge of predicting the movement of calved ice fragments, known as icebergs or "chicklets," whose trajectories are highly unpredictable due to the influence of ocean currents, winds, and other environmental factors. To address this, we used spatiotemporal data of iceberg movement over time and applied ConvLSTM layers to capture both the spatial patterns and temporal dynamics. To make predictions more accurate, we developed a custom drift loss function that focuses on two key aspects: how the size and shape of the chicklets change over time and how their movement patterns evolve. The loss function incorporates the principle that the larger the volume of the ice mass, the slower its movement pattern is likely to be, ensuring that the model captures this critical physical relationship. This approach enables our proposed deep learning based model to not only predict the trajectory of the icebergs but also understand the relationship between their motion and their changing dimensions. By addressing these complexities, this research not only advances the use of AI in glaciology, offering practical, and scalable, tools for studying and predicting ice calving and its after-effect of ice chicklets behavior, but also has potential applications in studying landslide movements, and coastal erosion movement.

• More Information about Rohan Putatunda successfully defends his PhD Proposal