SSA-UNet: Advanced Precipitation Nowcasting via Channel Shuffling
Not on the map. Why?
Abstract (arXiv)
Weather forecasting is essential for facilitating diverse socio-economic activity and environmental conservation initiatives. Deep learning techniques are increasingly being explored as complementary approaches to Numerical Weather Prediction (NWP) models, offering potential benefits such as reduced complexity and enhanced adaptability in specific applications. This work presents a novel design, Small Shuffled Attention UNet (SSA-UNet), which enhances SmaAt-UNet's architecture by including a shuffle channeling mechanism to optimize performance and diminish complexity. To assess its efficacy, this architecture and its reduced variant are examined and trained on two datasets: a Dutch precipitation dataset from 2016 to 2019, and a French cloud cover dataset containing radar images from 2017 to 2018. Three output configurations of the proposed architecture are evaluated, yielding outputs of 1, 6, and 12 precipitation maps, respectively. To better understand how this model operates and produces its predictions, a gradient-based approach called Grad-CAM is used to analyze the outputs generated. The analysis of heatmaps generated by Grad-CAM facilitated the identification of regions within the input maps that the model considers most informative for generating its predictions. The implementation of SSA-UNet can be found on our Githubfootnotehrefhttps://github.com/MarcoTurzi/SSA-UNethttps://github.com/MarcoTurzi/SSA-UNet
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 25 Apr 2025.
Builds on
20 of 36 references.
References · 20
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2018Deep learning in agriculture: A surveycited by 160
8 more
- 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
- 2021SA-Net: Shuffle Attention for Deep Convolutional Neural Networkscited by 54
- 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
- 2022AA-TransUNet: Attention Augmented TransUNet For Nowcasting Taskscited by 22
- 2020Wind speed prediction using multidimensional convolutional neural networkscited by 17
- 2024GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcastingcited by 9
- 2024Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcastingcited by 7
- 2023SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Taskscited by 6
Built on by
Citing papers per year: 2026: 2.
2 papers.