# gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method

arXiv: https://arxiv.org/abs/2408.14674 · HTML: https://mlsanity.com/paper/2408.14674/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://mlsanity.com/paper/1802.00036/) (2018)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [YOLO based Ocean Eddy Localization with AWS SageMaker](https://mlsanity.com/paper/2404.06744/) (2024)

## Built on by (most cited first, 5 of 5)

- [Joint Retrieval of Cloud properties using Attention-based Deep Learning Models](https://mlsanity.com/paper/2504.03133/) (2025)
- [Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling](https://mlsanity.com/paper/2505.05599/) (2025)
- [Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis](https://mlsanity.com/paper/2508.04573/) (2025)
- [Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism](https://mlsanity.com/paper/2505.21316/) (2025)
- [SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimization](https://mlsanity.com/paper/2508.03509/) (2025)

## Abstract (arXiv)

Atmospheric gravity waves occur in the Earths atmosphere caused by an interplay between gravity and buoyancy forces. These waves have profound impacts on various aspects of the atmosphere, including the patterns of precipitation, cloud formation, ozone distribution, aerosols, and pollutant dispersion. Therefore, understanding gravity waves is essential to comprehend and monitor changes in a wide range of atmospheric behaviors. Limited studies have been conducted to identify gravity waves from satellite data using machine learning techniques. Particularly, without applying noise removal techniques, it remains an underexplored area of research. This study presents a novel kernel design aimed at identifying gravity waves within satellite images. The proposed kernel is seamlessly integrated into a deep convolutional neural network, denoted as gWaveNet. Our proposed model exhibits impressive proficiency in detecting images containing gravity waves from noisy satellite data without any feature engineering. The empirical results show our model outperforms related approaches by achieving over 98% training accuracy and over 94% test accuracy which is known to be the best result for gravity waves detection up to the time of this work. We open sourced our code at https://rb.gy/qn68ku.