Remote Sensing for Short-Term Economic Forecasts

Carsten Juergens, Fabian M. Meyer-Heß, Marcus Goebel and Torsten Schmidt
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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

Sustainability, 2021, vol. 13, issue 17, 1-23

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.

Keywords: economic forecast; earth observation; machine learning; Sentinel-2; WorldView; post- classification comparison; template matching; change detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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