Classification and understanding of cloud structures via satellite images with EfficientUNet

TL;DR

This paper introduces a novel EfficientUNet model combining EfficientNet and UNet architectures for classifying cloud structures from satellite images, aiding climate understanding and prediction.

Contribution

It presents a new deep learning approach using EfficientNet as encoder and UNet as decoder for cloud classification, improving climate model accuracy.

Findings

Achieved Dice coefficient of 66.26% on public leaderboard

Demonstrated effective segmentation for cloud pattern classification

Showed potential for climate modeling improvements

Abstract

Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years. Shallow clouds play a significant role in understanding the Earth's climate, but they are challenging to interpret and represent in a climate model. By classifying these cloud structures, there is a better possibility of understanding the physical structures of the clouds, which would improve the climate model generation, resulting in a better prediction of climate change or forecasting weather update. Clouds organise in many forms, which makes it challenging to build traditional rule-based algorithms to separate cloud features. In this paper, classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN) named as EfficientNet as the encoder and UNet as decoder where they worked as feature extractor and reconstructor of fine grained feature map and was used as a classifier, which will help experts to understand how clouds will shape the future climate. By using a segmentation model in a classification task, it was shown that with a good encoder alongside UNet, it is possible to obtain good performance from this dataset. Dice coefficient has been used for the final evaluation metric, which gave the score of 66.26\% and 66.02\% for public and private (test set) leaderboard on Kaggle competition respectively.