mlsanityData to 7 Oct 2026

Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar

arXiv 2302.00817 ↗First posted 2 Feb 2023cs.LG eess.SPcited by 9

Not on the map. Why?

Abstract (arXiv)

The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temperatures. Airborne radar sensors, such as the Snow Radar, are able to measure accumulation rate patterns at a large-scale and monitor the effects of ongoing climate change on Greenland's precipitation and run-off. The Snow Radar's use of an ultra-wide bandwidth enables a fine vertical resolution that helps in capturing internal ice layers. Given the amount of snow accumulation in previous years using the radar data, in this paper, we propose a machine learning model based on recurrent graph convolutional networks to predict the snow accumulation in recent consecutive years at a certain location. We found that the model performs better and with more consistency than equivalent nongeometric and nontemporal models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

11 of 31 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2019Searching for MobileNetV3cited by 1,984
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2017Scene Graph Generation by Iterative Message Passingcited by 462
  7. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  8. 2018Graph R-CNN for Scene Graph Generationcited by 296
  9. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  10. 2020Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networkscited by 15
  11. 2021Multistream Graph Attention Networks for Wind Speed Forecastingcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 3.

9 papers.

  1. 2023Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approachcited by 8
  2. 2023Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networkscited by 8
  3. 2024Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Networkcited by 5
  4. 2024Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Predictioncited by 5
  5. 2025GRIT: Graph Transformer For Internal Ice Layer Thickness Predictioncited by 4
  6. 2026K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Datacited by 2
  7. 2025GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Predictioncited by 1
  8. 2026PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulationcited by 1
  9. 2026Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Tracescited by 0