Application of artificial intelligence for monitoring construction equipment in the context of integrated territorial development

Automating equipment fleet monitoring is a key challenge for managing large construction sites and integrated territorial development projects. This study demonstrates the feasibility of machine and deep learning methods for solving this problem. Algorithms, including neural networks, were configured to identify and track construction equipment using video streams from construction sites. A practical test of the system was conducted in the Akademichesky residential area in Yekaterinburg, one of the largest projects in Russia. The results confirmed the high accuracy of recognizing various types of construction equipment in construction conditions. The developed approach improves safety at construction sites, optimizes logistics, and manages resources, opening new opportunities for monitoring large projects.