ColudDetect – An automated solution to classify Ground-based Cloud Images

Abstract

"Understanding clouds and their patterns is crucial for meteorological research, but current groundbased cloud classification algorithms often fail to provide complete categorization. This study presents a multi-stage classification technique specifically designed to capture the subtle nuances of ground-based cloud formations, resulting in a more precise and detailed classification. Clouds are divided into high-level and low-level classes that include patterns like clear skies, patterned clouds, thick white or dark clouds, and veil clouds and then classified into 10 low-level classification Ci, Cc, Cs, Ac, Ns, As, Cu, St, Sc, Cb. The proposed method addresses these limitations by enhancing meteorological predictions and deepening our understanding of cloud dynamics. The research used a ground-based cloud categorization system that relied on Convolutional Neural Networks (CNNs). This system was revolutionary in detecting cloud patterns. The CNN architecture comprised convolutional, pooling, and fully connected layers, allowing automatic training of features from raw cloud image data. The research employed transfer learning with pretrained CNN models such MobileNetv2 to extract relevant cloud features, which further improved classification accuracy and efficiency. This reduced the need for manual feature engineering and improved classification accuracy. The system was also capable of detecting complex cloud patterns, textures, and structures by preprocessing the data for uniformity and augmenting the data for diversity. The ground-based cloud classification system has proven to be effective for both high-level and low-level cloud classification according to the final results of the study. The system outperformed known models in high-level cloud classification, with an accuracy rate of 98%, surpassing both EfficientNet and ResNet50. In low-level cloud categorization, the system continued to perform well with an accuracy rate of 57%, surpassing EfficientNet."