A Visual-Range Cloud Cover Image Dataset for Deep Learning Models

Abstract

Coastal and offshore oil and gas structures and operations are subject to continuous exposure to environmental conditions (ECs) such as varying air and water temperatures, rough sea conditions, strong winds, high humidity, rain, and varying cloud cover. To monitor ECs, weather and wave sensors are installed on these facilities. However, the capital expenditure (CAPEX) and operational expenses (OPEX) of these sensors are high, especially for offshore structures. For observable ECs, such as cloud cover, a cost-effective deep learning (DL) classification model can be employed as an alternative solution. However, to train and test a DL model, a cloud cover image dataset is required. In this paper, we present a novel visual-range cloud cover image dataset for cloud cover classification using a deep learning model. Various visual-range sky images are captured on nine different occasions, covering six cloud cover conditions. For each cloud cover condition, 100 images are manually classified. To increase the size and quality of images, multiple label-preserving data augmentation techniques are applied. As a result, the dataset is expanded to 9,600 images. Moreover, to evaluate the usefulness of the proposed dataset, three DL classification models, i.e., GoogLeNet, ResNet-50, and EfficientNet-B0, are trained, tested, and their results are presented. Even though EfficientNet-B0 had better generalization ability and marginally higher classification accuracy, it was discovered that ResNet-50 is the best choice for cloud cover classification due to its lower computational cost and competitive classification accuracy. Based on these results, it is concluded that the proposed dataset can be used in further research in DL-based cloud cover classification model development.