Combining model-based and data driven approaches to study climate change via Amazon SageMaker

Ice loss in Greenland and Antarctica has accelerated in recent decades. Melting polar ice sheets and mountain glaciers have considerable influence on sea level rise (SLR) and ocean currents, potential floods in coastal regions could put millions of people around the world at risk. The Intergovernmental Panel on Climate Change (IPCC) estimates that sea level could increase by 26–98cm by the end of this century. This large range in predicted SLR can be partially attributed to an incomplete understanding of bed topography and basal conditions in fast-flowing regions of ice sheets in Greenland and Antarctica. Therefore, a precise calculation of ice thickness is very important for sea level and flood monitoring. Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of available arctic and Antarctic data is unlabeled, and the labeling process is both time-consuming and expensive and requires a significant amount of domain experts’ time that could otherwise be spent on high-level scientific discovery.

NASA scientists have provided some initial label data for some small datasets. In this project, we are planning to use Sagmeaker tools to predict labels for the remaining data and engage the scientists so they can verify and improve the labeling process. After generating a good label data, we will develop several hybrid models to improve the accuracy of tracking the internal layers of ice sheet and calculating thickness. We will also use the weather models as an initial predication for deep learning and then improve the prediction model with active learning using data-driven approaches.

This research is supported by Amazon Machine Learning Research Award .


Maryam Rahnemoonfar
Principal Investigator

Masoud Yari
Co-Principal Investigator

Debvrat Varshhney
PhD Student

Tashnim Chowdhury
PhD Student

Argho Sarkar
PhD Student

Tanvi Kulkarni
Master Student

Mattew Lyan
Undergraduate Student
• Maryam Rahnemoonfar, "AI Solutions for Navigating Big Data from the Arctic and Antarctic", EarthCube meeting, 2021  [video]
• Debvrat Varshney, Maryam Rahnemoonfar, Masoud Yari, John Paden, "Deep Radiostratigraphy of the Greenland Ice sheet through Deep Learning on Airborne Snow Radar Images (2009-2017)", IS poster Day, May 2021  [poster]
• Anjali Pare, John Paden, Reece Mathews, Victor Berger, Maryam Rahnemoonfar, Masoud Yari and Geoffrey Fox, "Cluster interface for 2D layer tracking", October 2020 
• Maryam Rahnemoonfar, "Deep Learning in Remote sensing: Challenges and Opportunities", Edinburgh University, UK, October 2020 
• Maryam Rahnemoonfar, "Sustainability Challenges in Arctic", NSF EarthCube PI meeting, September 2020 
• Maryam Rahnemoonfar, "Navigating the Arctic through Deep Learning", Earth Day, Maryland, April 2020 
• Debvrat Varshney, Maryam Rahnemoonfar, Masoud Yari, John Paden, "Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks", IEEE BigData, 2020  [video]
• Masoud Yari, Maryam Rahnemoonfar, John Paden, L. Koenig, L. Montgomery, I. Oluwanisola, "Multi-Scale and Temporal Transfer Learning for Automatic Tracking of Internal Ice Layers", IGARSS, 2020 
• Maryam Rahnemoonfar, Masoud Yari, John Paden, "Radar Sensor Simulation with Generative Adversarial Network", IGARSS, 2020  [video]
• Ibikunle Oluwanisola, John Paden, Maryam Rahnemoonfar, David Crandall, Masoud Yari, "Snow Radar Layer Tracking Using Iterative Neural Network Approach", IGARSS, 2020 
• Maryam Rahnemoonfar, "Ice internal-layer tracking with deep multi-scale neural network, International", Symposium on Five Decades of Radioglaciology, Stanford University, July 2019 
• Maryam Rahnemoonfar, "Challenges and Opportunities of Data-Driven Machine Learning for Multi-dimensional Signals with and beyond the Visible Spectrum", Princeton University, 2019 
• Maryam Rahnemoonfar, "Intelligent Solutions for Navigating the Big Data from the Arctic and Antarctic", Rochester Institute of Technology, 2019 
• Masoud Yari, Maryam Rahnemoonfar, John Paden, Ibikunle Oluwanisola, Lora Koening, Lynn Montgomery, "Smart Tracking of Internal Layers of Ice in Radar Data via Multi-Scale Learning", IEEE BigData, 2019 
• Hamid Kamangir, Maryam Rahnemoonfar, Dugan Dobbs, John Paden, Geoffrey Fox, "Deep Hybrid Wavelet Networks for Detecting Ice Layers in Radar Images", IGARSS, 2018 
Acknowledgement
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