Deep Learning-Based Atmospheric Visibility Detection

Abstract

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.