Abstract
There are 44 classes of landscape covers in Slovakia, which are grouped in the Corine Landcover list. Accurate detection of classes of agricultural areas and wetlands (2.3.1. Permanent grasslands, meadows and pastures, 3.2.1 Natural meadows, 4.1. Inland wetlands) is important, but the division of these classes with the help of common classification methods is not possible, because they have the same reflectivity as grasslands. The use of deep learning methods can be one of the solutions to the problem of detection. To this day, the differentiation of 2.3.1 subcategories in Slovakia is detectable only empirically. The goal of this article is to compare supervised learning methods such as decision tree, neural networks, support vector machine, which are used to detect land cover in different countries. We will analyze the used methods and propose a suitable method for solving detection in the above-mentioned areas in Slovak republic.
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This publication is the result of implementation of the project VEGA 1/0176/22: “Proactive control of hybrid production systems using simulation-based digital twin” supported by the VEGA.
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Vasova, S., Benka, D., Kebisek, M., Stremy, M. (2023). Land Cover Detection in Slovak Republic Using Machine Learning. In: Silhavy, R., Silhavy, P. (eds) Artificial Intelligence Application in Networks and Systems. CSOC 2023. Lecture Notes in Networks and Systems, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-031-35314-7_58
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