Deep Learning-Based Atmospheric Visibility Detection
Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and comput...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/11/1394 |
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| author | Yawei Qu Yuxin Fang Shengxuan Ji Cheng Yuan Hao Wu Shengbo Zhu Haoran Qin Fan Que |
| author_facet | Yawei Qu Yuxin Fang Shengxuan Ji Cheng Yuan Hao Wu Shengbo Zhu Haoran Qin Fan Que |
| author_sort | Yawei Qu |
| collection | DOAJ |
| description | Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety. |
| format | Article |
| id | doaj-art-86b9631175ee45b693bde8a90015419e |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-86b9631175ee45b693bde8a90015419e2025-08-20T02:08:08ZengMDPI AGAtmosphere2073-44332024-11-011511139410.3390/atmos15111394Deep Learning-Based Atmospheric Visibility DetectionYawei Qu0Yuxin Fang1Shengxuan Ji2Cheng Yuan3Hao Wu4Shengbo Zhu5Haoran Qin6Fan Que7College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaYunce Technologies (Beijing) Co., Ltd., Beijing 100085, ChinaSchool of Emergency Management, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaAtmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety.https://www.mdpi.com/2073-4433/15/11/1394visibilitydeep learningCNNRNNGANTransformer |
| spellingShingle | Yawei Qu Yuxin Fang Shengxuan Ji Cheng Yuan Hao Wu Shengbo Zhu Haoran Qin Fan Que Deep Learning-Based Atmospheric Visibility Detection Atmosphere visibility deep learning CNN RNN GAN Transformer |
| title | Deep Learning-Based Atmospheric Visibility Detection |
| title_full | Deep Learning-Based Atmospheric Visibility Detection |
| title_fullStr | Deep Learning-Based Atmospheric Visibility Detection |
| title_full_unstemmed | Deep Learning-Based Atmospheric Visibility Detection |
| title_short | Deep Learning-Based Atmospheric Visibility Detection |
| title_sort | deep learning based atmospheric visibility detection |
| topic | visibility deep learning CNN RNN GAN Transformer |
| url | https://www.mdpi.com/2073-4433/15/11/1394 |
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