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    The shadow economy and sustainable development: The role of financial development
    (John Wiley and Sons Ltd, 2020-08-01)
    Abstract This paper provides fresh evidence concerning the threshold relationship between the shadow economy and financial development. Shadow economy is quoted as an obstacle to sustainable development and therefore the role of financial development is examined in this paper to tackle the issue of shadow economy. It is based on panel data of 29 developed and developing countries over the period of 1975–2015 and use of panel threshold model. Three proxies for financial development—liquid liabilities, private credit to deposit money banks, and stock market capitalisation—were utilised to obtain the threshold value of US$33,600 Gross Domestic Product (GDP) per capita. This threshold helps to determine the impact of financial development on the size of the shadow economy. Empirical results suggest that above this threshold, financial development significantly contributes to the reduction in size of the shadow economy while it has no impact for countries that have per capita income below this threshold. This implies that, countries with lower per capita income (below $33,600) should implement policies to improve accessibility to finance and credit market which leads to a sufficiently higher per capita income that in turn allows for a reduction in the size of the shadow economy.
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    CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
    (Center for Open Science, 2022-09-24)
    Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples. The dataset is made publicly available at https://cloudsen12.github.io/.