IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-05104995.html
   My bibliography  Save this paper

Satellites turn “concrete”: Tracking cement with satellite data and neural networks

Author

Listed:
  • Alexandre d'Aspremont

    (LIENS - Laboratoire d'informatique de l'école normale supérieure - DI-ENS - Département d'informatique - ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique, SIERRA - Statistical Machine Learning and Parsimony - DI-ENS - Département d'informatique - ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique, Kayrros)

  • Simon Ben Arous

    (Kayrros)

  • Jean-Charles Bricongne

    (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne, Centre de recherche de la Banque de France - Banque de France)

  • Benjamin Lietti

    (EPEE - Centre d'Etudes des Politiques Economiques - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay)

  • Baptiste Meunier

    (Centre de recherche de la Banque Centrale européenne - Banque Centrale Européenne, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

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 on around 1,000 plants, we construct a satellitebased index. 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, neural networks appear to yield more accurate predictions as they allow to exploit the granularity of our dataset. Overall, combining satellite images and machine learning can help policymakers to take informed and swift economic policy decisions by nowcasting accurately and in real-time economic activity.

Suggested Citation

  • Alexandre d'Aspremont & Simon Ben Arous & Jean-Charles Bricongne & Benjamin Lietti & Baptiste Meunier, 2024. "Satellites turn “concrete”: Tracking cement with satellite data and neural networks," Post-Print hal-05104995, HAL.
    • Handle: RePEc:hal:journl:hal-05104995
    DOI: 10.1016/j.jeconom.2024.105923
    Note: View the original document on HAL open archive server: https://hal.science/hal-05104995v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-05104995v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jeconom.2024.105923?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-05104995. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.