Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297340 
Year of Publication: 
2024
Series/Report no.: 
ECB Working Paper No. 2900
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
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 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.
Subjects: 
Big data
data science
machine learning
construction
high-frequency data
JEL: 
C51
C81
E23
E37
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-6380-0
Document Type: 
Working Paper

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