Cloud segmentation property extraction from total sky image repositories using Python
Article
Article Title | Cloud segmentation property extraction from total sky image repositories using Python |
---|---|
ERA Journal ID | 1631 |
Article Category | Article |
Authors | Igoe, Damien P. (Author), Parisi, Alfio V. (Author) and Downs, Nathan J. (Author) |
Journal Title | Instrumentation Science and Technology |
Journal Citation | 47 (5), pp. 522-534 |
Number of Pages | 13 |
Year | 2019 |
Publisher | Taylor & Francis |
Place of Publication | United States |
ISSN | 0733-4680 |
1073-9149 | |
1525-6030 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/10739149.2019.1603996 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/10739149.2019.1603996 |
Abstract | Acquiring the reflectance, radiance and related structural cloud properties from repositories of historical sky images can be a challenging and a computationally intensive task, especially when performed manually or by means of non-automated approaches. In this paper, a quick and efficient, self-adaptive Python tool for the acquisition and analysis of cloud segmentation properties that is applicable to images from all-sky image repositories is presented and a case study demonstrating its usage and the overall efficacy of the technique is demonstrated. The proposed Python tool aims to build a new data extraction technique and to improve the accessibility of data to future researchers, utilizing the freely available libraries in the Python programming language with the ability to be translated into other programming languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analysed in a relatively short time of just under 40 minutes, with an average execution time of about 0.06 seconds to complete each image analysis. |
Keywords | atmospheric composition analysis; sky imagers; Python; cloud cover; UV |
ANZSRC Field of Research 2020 | 370199. Atmospheric sciences not elsewhere classified |
419999. Other environmental sciences not elsewhere classified | |
519999. Other physical sciences not elsewhere classified | |
Public Notes | Submitted version made available in accordance with the copyright policy of the publisher. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5482/cloud-segmentation-property-extraction-from-total-sky-image-repositories-using-python
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