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  1. Satellites turn "concrete"
    tracking cement with satellite data and neural networks
    Autor*in: D'Aspremont, Alexandre; Arous, Simon Ben; Bricongne, Jean-Charles; Lietti, Benjamin; Meunier, Baptiste
    Erschienen: [2024]
    Verlag:  European Central Bank, Frankfurt am Main, Germany

    This paper exploits daily infrared images taken from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean, and exploit satellite images. Our algorithm uses the laws of physics (Planck's... mehr

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 534
    Fernleihe:
    keine Fernleihe

     

    This paper exploits daily infrared images taken from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean, and exploit satellite images. Our algorithm uses the laws of physics (Planck's law) and machine learning to detect the heat produced by cement plants in activity. This allows us to monitor in real-time whether a cement plant is working. Using this information on around 500 plants, we construct a satellite-based index tracking activity. We show that using this satellite index outperforms benchmark models and alternative indicators for nowcasting the production of the cement industry as well as the activity in the construction sector. Comparing across methods, we find neural networks yields significantly more accurate predictions as they allow to exploit the granularity of our daily and plant-level data. Overall, we show that combining satellite images and machine learning allows to track economic activity accurately.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9789289963800
    Weitere Identifier:
    Schriftenreihe: Working paper series / European Central Bank ; no 2900
    Schlagworte: Big data; data science; machine learning; construction; high-frequency data
    Umfang: 1 Online-Ressource (circa 47 Seiten), Illustrationen