使用EfficientUNet通过卫星图像对云结构进行分类和理解

多年来，气候变化一直是人们的共同利益，也是至关重要的政治讨论和决策的最前沿。浅云在理解地球的气候中起着重要作用，但是要解释和表示气候模型则具有挑战性。..

Classification and understanding of cloud structures via satellite images with EfficientUNet

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 leaderboard on Kaggle competition respectively.