Data Cleaning to fine-tune a Transfer Learning approach for Air Quality Prediction

Abstract

Air pollution is a serious environmental danger to people, specifically those who live in urbanised regions. Air pollution is also responsible for the climate crisis. Latest researches have shown the efficiency of early alert procedures that permits citizens to decrease their exposure to air pollution. Hence, monitoring air quality has turned into an essential need in most cities. Circulation, electricity, combustible uses, and various factors contribute to air pollution. Air quality ground stations are placed across most countries to record diverse air pollutants (including NO2), but they have a limited number, constraining therefore the accuracy of ground-level NO2 at high temporal and spatial resolutions. Conversely, satellite remote sensing data measures NO2 densities at a global scale. This paper presents a Data Cleaning technique for satellite images so Transfer Learning could be applied in a further step to estimate NO2 concentrations at Luxembourg with high spatial resolutions based on a pretrained Residual Network 50 (ResNet-50).