Mining atmospheric data
| Autor: | Jerome Riedi, Chaabane Djeraba |
|---|---|
| Přispěvatelé: | Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancé - USR 3380 (IRCICA), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Pollution
Ground truth [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] business.industry Computer science media_common.quotation_subject Big data Data classification Computer Science - Computer Vision and Pattern Recognition Air pollution [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Atmospheric model medicine.disease_cause Data science [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 13. Climate action [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] 11. Sustainability medicine Scale (map) business Air quality index ComputingMilieux_MISCELLANEOUS media_common |
| Zdroj: | 2021 International Conference on Content-Based Multimedia Indexing (CBMI) 2021 International Conference on Content-Based Multimedia Indexing (CBMI), Jun 2021, Lille, France. pp.1-5, ⟨10.1109/CBMI50038.2021.9461902⟩ CBMI |
| Popis: | This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates the building new public datasets and benchmarks, which are hot priority of the remote sensing community. The second issue is the investigation of deep learning methodologies for atmospheric data classification based on vast amount of data without annotations and with localized annotated data provided by sparse observing networks at the surface. The targeted application is air quality assessment and prediction. Air quality is defined as the pollution level linked with several atmospheric constituents such as gases and aerosols. There are dependency relationships between the bad air quality, caused by air pollution, and the public health. The target application is the development of a fast prediction model for local and regional air quality assessment and tracking. The results of mining data will have significant implication for citizen and decision makers by providing a fast prediction and reliable air quality monitoring system able to cover the local and regional scale through intelligent extrapolation of sparse ground-based in situ measurement networks. Comment: 5 pages, 1 figure |
| Databáze: | OpenAIRE |
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