Cloud segmentation property extraction from total sky image repositories using Python

Igoe, Damien P., Parisi, Alfio, and Downs, Nathan J. (2019) Cloud segmentation property extraction from total sky image repositories using Python. Instrumentation Science and Technology, 47 (5). pp. 522-534.

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Acquiring the reflectance, radiance, and related structural cloud properties from repositories of historical sky images is a challenging and a computationally intensive task, especially when performed manually or by means of nonautomated approaches. In this article, 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 programing language with the ability to be translated into other programing languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analyzed in a relatively short time of just under 40min, with an average execution time of about 0.06s to complete each image analysis.

Item ID: 60073
Item Type: Article (Research - C1)
ISSN: 1525-6030
Keywords: Atmospheric composition analysis, sky imagers, Python, cloud cover, UV
Copyright Information: (C) 2019 Taylor & Francis Group.
Date Deposited: 14 Aug 2019 07:39
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 50%
37 EARTH SCIENCES > 3701 Atmospheric sciences > 370103 Atmospheric aerosols @ 50%
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