rus
Issue #4/2025
A. Zalischuk, V. Nenashev, A. Sentsov, M. Belikov
RESEARCH OF NEURAL NETWORK METHODS FOR CLASSIFICATION OF THE EARTH’S SURFACE
RESEARCH OF NEURAL NETWORK METHODS FOR CLASSIFICATION OF THE EARTH’S SURFACE
DOI: 10.22184/1992-4178.2025.245.4.84.88
The article considers five different convolutional neural network models which were trained and tested. The input data was a set of satellite images RSI-CB256 of the Earth’s surface. The results of the study are applicable in such areas as mapping, urban planning, protection of population and territories from emergency situations.
Tags: automated onboard control convolutional neural networks deep learning land cover classification автоматизированный бортовой контроль глубокое обучение классификация зон земной поверхности сверточные нейронные сети
Subscribe to the journal Electronics: STB to read the full article.
The article considers five different convolutional neural network models which were trained and tested. The input data was a set of satellite images RSI-CB256 of the Earth’s surface. The results of the study are applicable in such areas as mapping, urban planning, protection of population and territories from emergency situations.
Tags: automated onboard control convolutional neural networks deep learning land cover classification автоматизированный бортовой контроль глубокое обучение классификация зон земной поверхности сверточные нейронные сети
Subscribe to the journal Electronics: STB to read the full article.
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