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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doaj-0c45d3b111c24be7a92fa00ff84e83b72021-09-09T13:57:37ZengMDPI AGSustainability2071-10502021-08-01139593959310.3390/su13179593Remote Sensing for Short-Term Economic ForecastsCarsten Juergens0Fabian M. Meyer-Heß1Marcus Goebel2Torsten Schmidt3Geomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, GermanyGeomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, GermanyGeomatics Group, Geography Department, Ruhr University Bochum, 44801 Bochum, GermanyRWI—Leibniz-Institut für Wirtschaftsforschung e.V., 45128 Essen, GermanyEconomic 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.https://www.mdpi.com/2071-1050/13/17/9593economic forecastearth observationmachine learningSentinel-2WorldViewpost- classification comparison |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carsten Juergens Fabian M. Meyer-Heß Marcus Goebel Torsten Schmidt |
spellingShingle |
Carsten Juergens Fabian M. Meyer-Heß Marcus Goebel Torsten Schmidt Remote Sensing for Short-Term Economic Forecasts Sustainability economic forecast earth observation machine learning Sentinel-2 WorldView post- classification comparison |
author_facet |
Carsten Juergens Fabian M. Meyer-Heß Marcus Goebel Torsten Schmidt |
author_sort |
Carsten Juergens |
title |
Remote Sensing for Short-Term Economic Forecasts |
title_short |
Remote Sensing for Short-Term Economic Forecasts |
title_full |
Remote Sensing for Short-Term Economic Forecasts |
title_fullStr |
Remote Sensing for Short-Term Economic Forecasts |
title_full_unstemmed |
Remote Sensing for Short-Term Economic Forecasts |
title_sort |
remote sensing for short-term economic forecasts |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-08-01 |
description |
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. |
topic |
economic forecast earth observation machine learning Sentinel-2 WorldView post- classification comparison |
url |
https://www.mdpi.com/2071-1050/13/17/9593 |
work_keys_str_mv |
AT carstenjuergens remotesensingforshorttermeconomicforecasts AT fabianmmeyerheß remotesensingforshorttermeconomicforecasts AT marcusgoebel remotesensingforshorttermeconomicforecasts AT torstenschmidt remotesensingforshorttermeconomicforecasts |
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