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Cloud Detection Based on Machine Learning Methods

Student: Yana Savva

Supervisor: Stanislav N. Fedotov

Faculty: Faculty of Computer Science

Educational Programme: Software Engineering (Bachelor)

Final Grade: 8

Year of Graduation: 2020

Even in the modern world, urban residents are dependent on weather conditions outside their homes. A major part of the population is influenced in different ways by temperature, rain and, what is important, cloudiness. Cloud detection is an important task in nowadays meteorology and can be a significant step toward more accurate and precise estimation and weather forecast. Satellite observations are the main source of global cloudiness and precipitation estimates due to their high spatio-temporal resolutions and coverage. In this case, satellite frames can be considered as a key part of input data, which, in this study, will be used in different machine learning applications to cloud detection problem. The purpose of this study is to explore existing approaches in cloud detection, making a comparison of them and relying on this research results, propose an alternative algorithm, which can be then embedded in production code of Yandex.Weather service. Such practices and improvements of weather forecasting might be quite valuable for the users of weather services like Yandex.Weather – one of the main weather forecasting providers in Russia. Having mentioned these facts, we come to the following statements: more precise forecast leads to the increase of daily and monthly active users, which, consequently, evolves into service profit growth and, never less important, users happiness and loyalty.

Full text (added May 27, 2020)

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