Abstract
Shallow clouds play a significant role in the earth’s radiation balance, but
they’re still poorly represented in climatic models. Our project analyzes the cloud
images taken from satellites and attempts to build a deep learning model to classify
cloud patterns. This will help us to identify the cloud formations and help improve the
earth’s climate understanding. We will use various deep learning and image
segmentation techniques like UNet to produce a model which can classify the shallow
layers of clouds into various labels (fish, flower, gravel, and sugar). Various data
augmentation techniques are implemented to improve the proposed model.
Additionally, transfer learning is implemented by using ResNet backbones to improve
the performance of the segmentation model. This will help gain insights into the matter
of shallow cloud effects on the earth’s climate, there by helping in the development of
next-gen climate models without having to go through the tedious task of classifying
the clouds present in the images first.
Keywords: Deep Learning, Image Segmentation, RAdam, Shallow Clouds, UNet.
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