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Machine Learning Methods for Automated Analysis of Remote Sensing Imagery

Student: Vsevolod Gusev

Supervisor: Alexander Sirotkin

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Big Data Analysis for Business, Economy, and Society (Master)

Year of Graduation: 2020

One of the important aspects of oil production is geospatial data processing. Its traditional methods include manual segmentation of satellite imagery. These methods are labor and resource intensive. The emerging alternative to standard practices is the utilization of machine learning algorithms, which can drastically reduce costs of such operations. Systems built upon these algorithms can be used in building process monitoring and oil field operation. Such systems are not present in today oil industry, so the aim of this work is to prototype one and its underlying machine learning algorithms.

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