Research Article
SCM-Net: A Lightweight AI-Based Sea Ice Classification for Climate Change
@ARTICLE{10.4108/eetinis.132.12246, author={Nazanin Baramaki and Quang Le and Brad McNiven and Muhammad Fahim}, title={SCM-Net: A Lightweight AI-Based Sea Ice Classification for Climate Change}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={13}, number={2}, publisher={EAI}, journal_a={INIS}, year={2026}, month={6}, keywords={Light-weight deep learning models, real-time applications, SCM-Net, sea ice classification, swin transformer}, doi={10.4108/eetinis.132.12246} }- Nazanin Baramaki
Quang Le
Brad McNiven
Muhammad Fahim
Year: 2026
SCM-Net: A Lightweight AI-Based Sea Ice Classification for Climate Change
INIS
EAI
DOI: 10.4108/eetinis.132.12246
Abstract
Sea ice is considered one of the most valuable sources of information for maintaining the balance of Earth’s climate system and preventing excessive warming. This study introduces a lightweight yet accurate model designed for real-time sea ice classification to provide an accessible and practical tool for operational use. We propose the SCM-Net, a deep learning model for sea ice classification applications, and compare its performance against other state-of-the-art models, including MobileNet, Residual Network (ResNet), Visual Geometry Group Network (VGGNet), Vision Transformers (ViT), and Shifted Window Transformers (SwinT). The Swin Transformer Convolutional Hybrid model (SCM-Net) is a lightweight model with around 45 times less parameters, enabling usage in real time applications. The results demonstrate that the proposed SCMNet model achieves a comparable and even better accuracy in comparison to other models. Moreover, the proposed model significantly reduces the number of parameters while improving inference efficiency. These results show that the proposed model is well suited for real time sea ice classification applications.
Copyright © 2026 Nazanin Baramaki et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
