Application of Semantic Technologies in a Model of Automatic Recognition and Analysis of Elements in Images of the Earth’s Surface

Abstract

This research investigates the impact of neural network model training parameters in the context of object classification within the framework of semantic technologies, specifically for the analysis of Earth’s surface imagery. It is shown how semantic technologies and their application to the task of classifying objects in earth surface images improve the ability of the model to process and classify objects. A detailed analysis of the influence of key training parameters on the accuracy and stability of the model is carried out. These factors were assessed within the framework of the semantic technologies approach. The findings highlight the effectiveness of the neural network model in semantic technology applications for Earth imagery analysis. These results can be applied to improve the performance of neural network-based object classification systems within the domain of semantic technologies.