Remote Sensing for Short-Term Economic Forecasts

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13179593