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

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 i...

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Main Authors: Umair, M., Hashmani, M.A.
Format: Article
Published: Science and Information Organization 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124000490&doi=10.14569%2fIJACSA.2022.0130166&partnerID=40&md5=9f8f50eb37bb18c9a45310e03e5c3d84
http://eprints.utp.edu.my/28979/
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spelling my.utp.eprints.289792022-03-17T03:02:02Z A Visual-Range Cloud Cover Image Dataset for Deep Learning Models Umair, M. Hashmani, M.A. 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 © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved Science and Information Organization 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124000490&doi=10.14569%2fIJACSA.2022.0130166&partnerID=40&md5=9f8f50eb37bb18c9a45310e03e5c3d84 Umair, M. and Hashmani, M.A. (2022) A Visual-Range Cloud Cover Image Dataset for Deep Learning Models. International Journal of Advanced Computer Science and Applications, 13 (1). pp. 534-541. http://eprints.utp.edu.my/28979/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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 © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved
format Article
author Umair, M.
Hashmani, M.A.
spellingShingle Umair, M.
Hashmani, M.A.
A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
author_facet Umair, M.
Hashmani, M.A.
author_sort Umair, M.
title A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
title_short A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
title_full A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
title_fullStr A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
title_full_unstemmed A Visual-Range Cloud Cover Image Dataset for Deep Learning Models
title_sort visual-range cloud cover image dataset for deep learning models
publisher Science and Information Organization
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124000490&doi=10.14569%2fIJACSA.2022.0130166&partnerID=40&md5=9f8f50eb37bb18c9a45310e03e5c3d84
http://eprints.utp.edu.my/28979/
_version_ 1738656908562137088
score 13.211869

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