# Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data

arXiv: https://arxiv.org/abs/2509.08571 · HTML: https://mlsanity.com/paper/2509.08571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding](https://mlsanity.com/paper/2106.02795/) (2021)
- [Kriging Convolutional Networks](https://mlsanity.com/paper/2306.09463/) (2023)
- [Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction](https://mlsanity.com/paper/1908.07805/) (2019)
- [Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method](https://mlsanity.com/paper/2409.06638/) (2024)
- [DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets](https://mlsanity.com/paper/2505.23980/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals](https://mlsanity.com/paper/2511.14473/) (2025)

## Abstract (arXiv)

Accurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning framework that fuses heterogeneous supervision and explicitly models uncertainty via Monte Carlo dropout. Spatial graphs built from surface observables (elevation, velocity, mass balance) are augmented with gradient features and polynomial trends to capture both local variability and broad structure. To handle data gaps, we employ a hybrid loss that combines confidence-weighted radar supervision with dynamically balanced regularization. Applied to three Greenland subregions, GraphTopoNet outperforms interpolation, convolutional, and graph-based baselines, reducing error by up to 60 percent while preserving fine-scale glacial features. The resulting bed maps improve reliability for operational modeling, supporting agencies engaged in climate forecasting and policy. More broadly, GraphTopoNet shows how graph machine learning can convert sparse, uncertain geophysical observations into actionable knowledge at continental scale.