Air pollution prediction with satellite imagery: deep learning approach

Abstract

This study addresses the urgent global concern of climate change, focusing on air pollution as a significant contributor. Human activities, particularly the burning of fossil fuels for transportation and energy generation, release substantial amounts of harmful air pollutants, including greenhouse gases (GHGs). To effectively mitigate air pollution and its environmental impacts, accurate prediction of its spatio-temporal patterns is crucial. This study uses the Convolutional Long Short-Term Memory (ConvLSTM) deep learning model to analyze and forecast air pollution using high-resolution, multi-spectral time-series imagery from the Sentinel-2 satellite and meteorological data. By combining the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing strengths of LSTM networks, ConvLSTMs effectively capture both spectral and temporal characteristics inherent in the satellite data. Additionally, temporal meteorological data from air quality monitoring stations is analyzed using LSTM models to enhance the temporal dimension of the analysis. The strategic combination of ConvLSTM for spatial and spectral analysis and LSTM for temporal analysis offers a comprehensive methodology for environmental monitoring and forecasting, with significant implications for urban planning and public health initiatives. In analyzing nitrogen dioxide spatial distribution, the ConvLSTM model demonstrated consistent improvement in training and validation loss, indicating enhanced generalization capabilities without significant overfitting. However, challenges arise due to the variability in the Structural Similarity Index (SSIM) which was averaging +-0.30 across predicted frames, stemming from the quality variation in the data. The ConvLSTM model performs better with meteorological data, as evidenced by convincing heatmaps and effective avoidance of overfitting where the loss: was 0.1237 and val_loss: of 0.1458 with the 100 epochs. The predicted results are showed in this paper.