# Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks

arXiv: https://arxiv.org/abs/2009.00191 · HTML: https://mlsanity.com/paper/2009.00191/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar](https://mlsanity.com/paper/2302.00817/) (2023)
- [Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach](https://mlsanity.com/paper/2306.13181/) (2023)
- [Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks](https://mlsanity.com/paper/2306.13690/) (2023)
- [Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network](https://mlsanity.com/paper/2406.15299/) (2024)
- [Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction](https://mlsanity.com/paper/2411.04055/) (2024)
- [GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction](https://mlsanity.com/paper/2507.07388/) (2025)
- [Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms](https://mlsanity.com/paper/2310.19574/) (2023)
- [Regression Networks For Calculating Englacial Layer Thickness](https://mlsanity.com/paper/2104.04654/) (2021)
- [AI-ready Snow Radar Echogram Dataset (SRED) for climate change monitoring](https://mlsanity.com/paper/2505.00786/) (2025)
- [K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data](https://mlsanity.com/paper/2604.09922/) (2026)
- [Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region](https://mlsanity.com/paper/2303.07403/) (2023)
- [GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction](https://mlsanity.com/paper/2511.18716/) (2025)
- [Automatic Feature Highlighting in Noisy RES Data With CycleGAN](https://mlsanity.com/paper/2108.11283/) (2021)
- [Physics-Informed Machine Learning On Polar Ice: A Survey](https://mlsanity.com/paper/2404.19536/) (2024)
- [IceWatch: Forecasting Glacial Lake Outburst Floods (GLOFs) using Multimodal Deep Learning](https://mlsanity.com/paper/2601.12330/) (2026)

## Abstract (arXiv)

Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multi-class semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.