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Remote Sensing for Short-Term Economic Forecasts

Author

Listed:
  • Carsten Juergens

    (Geomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, Germany)

  • Fabian M. Meyer-Heß

    (Geomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, Germany)

  • Marcus Goebel

    (Geomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, Germany)

  • Torsten Schmidt

    (RWI—Leibniz-Institut für Wirtschaftsforschung e.V., 45128 Essen, Germany)

Abstract

Economic forecasts are an important instrument to judge the nation-wide economic situation. Such forecasts are mainly based on data from statistical offices. However, there is a time lag between the end of the reporting period and the release of the statistical data that arises for instance from the time needed to collect and process the data. To improve the forecasts by reducing the delay, it is of interest to find alternative data sources that provide information on economic activity without significant delays. Among others, satellite images are thought to assist here. This paper addresses the potential of earth observation imagery for short-term economic forecasts. The study is focused on the estimation of investments in the construction sector based on high resolution (HR) (10–20 m) and very high resolution (VHR) (0.3–0.5 m) images as well as on the estimation of investments in agricultural machinery based on orthophotos (0.1 m) simulating VHR satellite imagery. By applying machine learning it is possible to extract the objects of interest to a certain extent. For the detection of construction areas, VHR satellite images are much better suited than HR satellite images. VHR satellite images with a ground resolution of 30–50 cm are able to identify agricultural machinery. These results are promising and provide new and unconventional input for economic forecasting models.

Suggested Citation

  • Carsten Juergens & Fabian M. Meyer-Heß & Marcus Goebel & Torsten Schmidt, 2021. "Remote Sensing for Short-Term Economic Forecasts," Sustainability, MDPI, vol. 13(17), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9593-:d:622200
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    References listed on IDEAS

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    Cited by:

    1. Mayank Dixit & Kuldeep Chaurasia & Vipul Kumar Mishra & Dilbag Singh & Heung-No Lee, 2022. "6+: A Novel Approach for Building Extraction from a Medium Resolution Multi-Spectral Satellite," Sustainability, MDPI, vol. 14(3), pages 1-15, January.

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