Glacier Movement Prediction with Attention-based Recurrent Neural Networks and Satellite Data (Papers Track)

Jonas Müller (University of Tübingen); Raphael Braun (University of Tübingen); Hendrik P. A. Lensch (University of Tübingen); Nicole Ludwig (University of Tübingen)

Paper PDF Poster File NeurIPS 2023 Poster Cite
Earth Observation & Monitoring Computer Vision & Remote Sensing


Studying glacier movements is crucial because of their indications for global climate change and its effects on local land masses. Building on established methods for glacier movement prediction from Landsat-8 satellite imaging data, we develop an attention-based deep learning model for time series data prediction of glacier movements. In our approach, the Normalized Difference Snow Index is calculated from the Landsat-8 spectral reflectance bands for data of the Parvati Glacier (India) to quantify snow and ice in the scene images, which is then used for time series prediction. Based on this data, a newly developed Long-Short Term Memory Encoder-decoder neural network model is trained, incorporating a Multi-head Self Attention mechanism in the decoder. The model shows promising results, making the prediction of optical flow vectors from pure model predictions possible.