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

Marie Njaime, Fahed Abdallah Olivier, Hichem Snoussi, Judy Akl, Charbel Chahla, Hichem Omrani

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collectionChapterRevue par des pairs


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).

langue originaleAnglais
titreISC2 2022 - 8th IEEE International Smart Cities Conference
EditeurInstitute of Electrical and Electronics Engineers Inc.
Nombre de pages5
ISBN (Electronique)9781665485616
ISBN (imprimé)9781665485616
Les DOIs
étatPublié - 2022
Evénement8th IEEE International Smart Cities Conference, ISC2 2022 - Pafos, Chypre
Durée: 26 sept. 202229 sept. 2022

Série de publications

NomISC2 2022 - 8th IEEE International Smart Cities Conference

Une conférence

Une conférence8th IEEE International Smart Cities Conference, ISC2 2022
La villePafos

Une note bibliographique

Publisher Copyright:
© 2022 IEEE.

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