Abstract
The inference quality of machine learning solutions based on neural networks is highly related with the quality and quantity of training data. This paper proposes and experiments a set of algorithms and techniques to enhance the quality and quantity of training data based on satellite images. The results show that the algorithms can eliminate noise from different areas of interest, providing viable data for further processing. The achievements are assessed by the soil classification techniques on local Transylvanian areas.
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