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.
Highlights
Jarmila ZimmermannováEconomic investment is a key factor for a country’s economic development both in the long-term and in the short-term
This paper investigates the potential of earth observation imagery for short-term economic forecasting
Quantitative classification results for all very high resolution (VHR) scenes are given for every class in Figure 10 and Table 9
Summary
Jarmila ZimmermannováEconomic investment is a key factor for a country’s economic development both in the long-term and in the short-term. An appropriate development of public infrastructure is an important prerequisite for private investments and sustainable economic growth In this context, Alaloul et al (2021) [1] highlight the importance of a country’s construction sector for the development because it is closely intertwined with other sectors of the economy. The identification of sealed surfaces is limited due to the size of the construction areas and the spatial resolution of the image data. This leads to the result that in many cases individual Sentinel-2 images are suited to reliably identify conHowever, multi-temporal comparisons of not subsequent image classification results struction activities in a city like Berlin
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