Identification of Urban Power Distribution Network Elements using a Hybrid Mobile Mapping System

• Grant number: 25/09727-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2025
• End date: July 31, 2026
• Field of knowledge: Engineering - Civil Engineering - Transportation Infrastructure
• Principal Investigator: Henrique Cândido de Oliveira
• Grantee: Mateus Serracchiani Ananias
• Host Institution: Faculdade de Engenharia Civil, Arquitetura e Urbanismo (FEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The rapid expansion of urban centers and the increasing complexity of infrastructure management demand innovative approaches to geospatial data acquisition. In this context, this research proposes the use of a hybrid mobile mapping system that integrates terrestrial and aerial data collection to enhance urban mapping in smart city applications. By combining Mobile Mapping Systems (MMS) with Unmanned Aerial Vehicles (UAVs), the study aims to improve the completeness, accuracy, and automation of geospatial feature extraction. The methodology includes the development of a customized application for UAV flight execution, specifically designed for integration within hybrid mapping systems. Additionally, an artificial intelligence algorithm will be developed for the automatic identification and classification of urban elements of interest. As a case study, features of the electrical distribution network will be used, enabling validation of the proposed techniques in a real-world environment. Expected outcomes include the establishment of methodologies for UAV flight execution in hybrid mapping models, the application of artificial intelligence in urban feature recognition, and the organization of a spatial database containing the georeferenced information extracted throughout the process. By ensuring optimized aerial data acquisition, its integration with MMS data, and the use of deep learning techniques, this project aims to address existing technical gaps in hybrid mapping systems. The results are also expected to contribute to the advancement of geospatial technologies, supporting urban planning, infrastructure monitoring, and the development of smart and sustainable cities.