Remote Sensing for Short-Term Economic Forecasts

• Published in: Sustainability 2021-09, Vol.13 (17), p.9593
• Main Authors: Juergens, Carsten, Meyer-Heß, Fabian M., Goebel, Marcus, Schmidt, Torsten
• Format: Article
• Language: English
• Subjects: Agricultural equipment; Building construction; Business cycles; Classification; Construction industry; Consumption; Economic activity; Economic analysis; Economic conditions; Economic crisis; Economic development; Economic forecasting; Economic forecasts; Economic growth; Economic statistics; Farm machinery; Feasibility studies; Financial services; GDP; Gross Domestic Product; High resolution; Housing; International organizations; Investments; Learning algorithms; Machine learning; Remote sensing; Response time; Satellite imagery; Satellites; Statistics; Sustainability; Time lag; Time series; Variables
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13179593

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.