# Multi-scale Residual Transformer for VLF Lightning Transients Classification

arXiv: https://arxiv.org/abs/2312.04163 · HTML: https://mlsanity.com/paper/2312.04163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [GiT: Graph Interactive Transformer for Vehicle Re-identification](https://mlsanity.com/paper/2107.05475/) (2021)
- [HSGNet: Object Re-identification with Hierarchical Similarity Graph Network](https://mlsanity.com/paper/2211.05486/) (2022)
- [A Competitive Method for Dog Nose-print Re-identification](https://mlsanity.com/paper/2205.15934/) (2022)
- [Machine Learning Techniques to Detect and Characterise Whistler Radio Waves](https://mlsanity.com/paper/2002.01244/) (2020)

## Abstract (arXiv)

The utilization of Very Low Frequency (VLF) electromagnetic signals in navigation systems is widespread. However, the non-stationary behavior of lightning signals can affect VLF electromagnetic signal transmission. Accurately classifying lightning signals is important for reducing interference and noise in VLF, thereby improving the reliability and overall performance of navigation systems. In recent years, the evolution of deep learning, specifically Convolutional Neural Network (CNNs), has sparked a transformation in lightning classification, surpassing traditional statistical methodologies. Existing CNN models have limitations as they overlook the diverse attributes of lightning signals across different scales and neglect the significance of temporal sequencing in sequential signals. This study introduces an innovative multi-scale residual transform (MRTransformer) that not only has the ability to discern intricate fine-grained patterns while also weighing the significance of different aspects within the input lightning signal sequence. This model performs the attributes of the lightning signal across different scales and the level of accuracy reached 90% in the classification. In future work, this model has the potential applied to a comprehensive understanding of the localization and waveform characteristics of lightning signals.