An Effective Convolutional Neural Network for Visualized Understanding Transboundary Air Pollution Based on Himawari-8 Satellite Images

Lin, Fangzhou; Gao, Chenyang; Yamada, Kazunori

doi:10.1109/lgrs.2021.3102939

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…We prepare a dataset from Landsat 8 images. Datasets used in this work must be in a valid format; within range 0 to 10,000 [ 17 ]. Additionally, only dataset with low cloud confident are selected (cloud confidence refers to how likely cloud are present in the data), that way, outlier problem can be avoided.…”

Section: Data Analysis Experiments and Discussion On Resultsmentioning

confidence: 99%

“… 2020 Meteorological data SVM Provided a flowchart of the method, making it easy to understand Lack of an explanation about the datasets The organization of sections and sections are hard to follow Perak and Penang States, Malaysia [ 16 ] Y. Gu, B. Li, and Q. Meng, Hybrid interpretable predictive machine learning model for air pollution prediction, Neurocomputing 468 (2022): 123–136. 2022 Air pollution datasets HIP-ML (Hybrid Interpretable Predictive Machine Learning) Proposed a new approach of machine learning or deep learning The organization of paper is really hard to follow China [ 17 ] F. Lin, C. Gao, and K.D. Yamada, An Effective Convolutional Neural Network for Visualized Understanding Transboundary Air Pollution Based on Himawari-8 Satellite Images, IEEE Geoscience and Remote Sensing Letters 19 (2021): 1–5.…”

Section: Methodsmentioning

confidence: 99%

“…In 2022, Gu et al [ 16 ] used Hybrid interpretable Artificial Neural Network to predict air pollution. Lin et al [ 17 ] used Convolution Neural Networks for visualizing transboundary air pollution from Himawari-8 satellite images. Ji et al [ 18 ] adapted Weibo social media data as health sensing mechanism to predict and/or corrected estimation of air quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

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Section: Data Analysis Experiments and Discussion On Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Jitkajornwanich,

Vijaranakul,

Jaiyen

et al. 2024

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“…Similarly, Mao et al [54] have reported a deep learning method for predicting air quality. In another study, the researchers proposed an effective convolutional neural network (CNN) for visual understanding of transboundary air pollution based on Himawari-8 satellite images [55]. The CNN-based model was shown to accurately identify and classify different types of pollutants.…”

Section: Related Workmentioning

confidence: 99%

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Mumtaz,

Amin,

Khan

et al. 2023

Energies

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Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

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“…It is simpler because it does not need to perform image feature extraction. Subsequent analyses in image segmentation are more directed to the application and development of FCNBSS-based models [24],…”

Section: Introductionmentioning

confidence: 99%

Improving Convective Cloud Classification with Deep Learning: The CC-Unet Model

Rumapea,

Zarlis,

Efendy

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Analyzing and mitigating natural disasters can be a challenging task, which is why the field of computer science, specifically artificial intelligence (AI) is necessary to aid in the complexity of disaster management. AI provides the tools and analytical models to help solve the intricacies of handling natural disasters. Convective clouds, closely related to rain and can lead to large-scale, prolonged hydrometeorological disasters, are a crucial component to consider. To improve the classification of these clouds, a predictive-analytical model based on deep learning, called the CC-Unet model, was developed. This model utilizes a U-Net architecture and is trained using a dataset of convective cloud images. The researchers used satellite image data from the Himawari 8 satellite collected in May and October 2021. The images were pre-processed and verified using observational data. The model was tested using a random train-test split method, showing that the CC-Unet model had a higher accuracy of 97.29% compared to the U-Net model, which had an accuracy of 94.17%. Additionally, the significance test using the Wilcoxon method showed that the CC-Unet model had significantly different performance results from the U-Net model. The ground truth image was also compared with the predicted image, showing a low root mean square error value of 0.0218, indicating a high level of similarity between the two. Overall, this research demonstrates the potential of AI and deep learning in classifying convective clouds to aid in natural disaster management.

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An Effective Convolutional Neural Network for Visualized Understanding Transboundary Air Pollution Based on Himawari-8 Satellite Images

Cited by 3 publications

References 26 publications

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Improving Convective Cloud Classification with Deep Learning: The CC-Unet Model

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