UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM

TL;DR

This work surveys UAV-based infrastructure inspections in the AEC+FM domain, drawing on over 150 studies to map data acquisition, photogrammetric modeling, defect detection, and decision-support frameworks. It identifies advances in path planning, thermal integration, and multimodal ML, while highlighting gaps in real-time processing and cross-sensor generalizability. The authors propose an integrated UAV inspection framework that fuses RGB, LiDAR, and thermal data via transformer-based architectures (e.g., MMSFormer) and demonstrate its feasibility through a case study at the University of Virginia, including Voronoi-based path planning and BIM/CAD integration. The paper concludes with actionable directions for lightweight AI, adaptive flight planning, synthetic datasets, and richer modality fusion to enhance accuracy, reliability, and operational efficiency in modern infrastructure inspections.

Abstract

Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain. By synthesizing insights from over 150 studies, this review paper highlights UAV-based methodologies for data acquisition, photogrammetric modeling, defect detection, and decision-making support. Key innovations include path optimization, thermal integration, and advanced machine learning (ML) models such as YOLO and Faster R-CNN for anomaly detection. UAVs have demonstrated value in structural health monitoring (SHM), disaster response, urban infrastructure management, energy efficiency evaluations, and cultural heritage preservation. Despite these advancements, challenges in real-time processing, multimodal data fusion, and generalizability remain. A proposed workflow framework, informed by literature and a case study, integrates RGB imagery, LiDAR, and thermal sensing with transformer-based architectures to improve accuracy and reliability in detecting structural defects, thermal anomalies, and geometric inconsistencies. The proposed framework ensures precise and actionable insights by fusing multimodal data and dynamically adapting path planning for complex environments, presented as a comprehensive step-by-step guide to address these challenges effectively. This paper concludes with future research directions emphasizing lightweight AI models, adaptive flight planning, synthetic datasets, and richer modality fusion to streamline modern infrastructure inspections.

Figures (2)

• Figure 1: Proposed UAV-Based Inspection Framework: (a) Path optimization, (b) data processing with MMSFormer Transformer model, and (c) output use-cases and applications.
• Figure 2: Distribution of UAV-based applications in infrastructure inspections.