The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume XLVIII-2/W3-2023
https://doi.org/10.5194/isprs-archives-XLVIII-2-W3-2023-109-2023
https://doi.org/10.5194/isprs-archives-XLVIII-2-W3-2023-109-2023
12 May 2023
 | 12 May 2023

TRAINING OF NEURAL NETWORKS TO DECIPHER THE ROAD NETWORK ACCORDING TO SPACE IMAGERY RECEIVED BY THE ”RESURS-P”

N. N. Kasatikov, S. M. Umarov, A. D. Fadeeva, and S. A. Tolmachev

Keywords: Neural networks, Special processing of satellite images, ”Resurs-P”, GroMobile development, Geoportal, Digital modern map, GCPs

Abstract. Our team has developed a neural network for road recognition on our digital twin, aimed at enhancing transportation-related applications. The neural network is trained on large datasets of road images and utilizes various deep learning architectures and techniques to improve its accuracy and reliability. The embedded neural network can recognize different road features, such as lane markings, road signs, and obstacles, and can identify the location and direction of the road. The integration of this neural network in our digital twin can help optimize transportation-related operations, reduce accidents, and improve overall traffic flow. The developed neural network architecture and training methodology, as well as its performance evaluation on various datasets, are presented in this paper. Additionally, the paper discusses the future directions for research in this area and the potential of the developed neural network for other applications in the digital twin domain.