Public Works Inspection: A Low-Cost, Ai-Enhanced Drone Methodology for Remote Communities

Abstract

Infrastructure inspection is crucial for ensuring the safety and operational efficiency of public works. However, many public agencies in northern Brazil lack the necessary procedures, equipment, and qualified personnel for effective supervision. This study proposes and validates a standardized, cost-effective methodology integrating low-cost drones, consumer-grade cameras, and open-source software with AI diagnostics. By using scale bars instead of expensive RTK GPS systems, we reduce equipment costs to approximately $500 up to 80% less than conventional setups while achieving sub-centimeter accuracy and detecting structural defects and anomalies. Case studies on urban pavement, bridges, buildings, and dams in Gold Coast, Australia, validate its precision across diverse infrastructure, rivaling high-cost systems. This accessible framework empowers public agencies, particularly in remote and economically challenged regions, to conduct efficient, safe inspections without financial strain. By democratizing advanced technology, this approach enhances infrastructure management, offering a scalable, replicable model for global adoption in construction oversight.

Keywords: Drones, Artificial Intelligence, Public Works, Infrastructure, Pathological Manifestation, Structural Defects.