# Recognition of polar lows in Sentinel-1 SAR images with deep learning

arXiv: https://arxiv.org/abs/2203.16401 · HTML: https://mlsanity.com/paper/2203.16401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 23 of 23)

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- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
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- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
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- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets](https://mlsanity.com/paper/1605.01156/) (2016)
- [Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection](https://mlsanity.com/paper/2001.04271/) (2020)
- [Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data](https://mlsanity.com/paper/1910.10566/) (2019)
- [Tropical and Extratropical Cyclone Detection Using Deep Learning](https://mlsanity.com/paper/2005.09056/) (2020)
- [Large-scale detection and categorization of oil spills from SAR images with deep learning](https://mlsanity.com/paper/2006.13575/) (2020)
- [Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1910.05411/) (2019)

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

- [Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery](https://mlsanity.com/paper/2608.14366/) (2026)

## Abstract (arXiv)

In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ERA5 dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an F-1 score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: (i) such features are significantly cropped due to the limited swath width of the SAR, (ii) the features are partly covered by sea ice and (iii) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500m, 1km and 2km), it is found that higher resolution yield the best performance. This emphasises the potential of using high resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.