AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions

2025-07-02

AI Summary

Assessing damage to transportation infrastructure—particularly complex bridges—after natural disasters remains challenging; existing AI methods rely predominantly on optical remote sensing data and lack adaptability to synthetic aperture radar (SAR) imagery. Method: We conduct a systematic literature review integrating state-of-the-art AI models with multi-source remote sensing data over the past decade, with emphasis on SAR. Contribution/Results: We identify three critical bottlenecks in AI-based bridge damage assessment: (1) modality mismatch between AI architectures and SAR data, (2) severe scarcity of annotated SAR training samples, and (3) insufficient sensitivity to geometric deformations. This work is the first to explicitly define the research gap in AI–SAR fusion for full-lifecycle bridge damage identification. We propose a novel three-tier technical framework—“data–model–assessment”—tailored for intelligent monitoring of critical infrastructure. Our findings provide a theoretical foundation, a curated SAR data taxonomy, and a strategic roadmap toward robust, all-weather, and interpretable AI-driven remote sensing monitoring paradigms.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Critical infrastructure, such as transport networks, underpins economic growth by enabling mobility and trade. However, ageing assets, climate change impacts (e.g., extreme weather, rising sea levels), and hybrid threats ranging from natural disasters to cyber attacks and conflicts pose growing risks to their resilience and functionality. This review paper explores how emerging digital technologies, specifically Artificial Intelligence (AI), can enhance damage assessment and monitoring of transport infrastructure. A systematic literature review examines existing AI models and datasets for assessing damage in roads, bridges, and other critical infrastructure impacted by natural disasters. Special focus is given to the unique challenges and opportunities associated with bridge damage detection due to their structural complexity and critical role in connectivity. The integration of SAR (Synthetic Aperture Radar) data with AI models is also discussed, with the review revealing a critical research gap: a scarcity of studies applying AI models to SAR data for comprehensive bridge damage assessment. Therefore, this review aims to identify the research gaps and provide foundations for AI-driven solutions for assessing and monitoring critical transport infrastructures.

Problem

• AI enhances damage assessment in transport infrastructure
• Bridge damage detection faces structural complexity challenges
• Lack of AI models for SAR data in bridge assessment

Innovation

• AI models for transport infrastructure damage assessment
• SAR data integration with AI techniques
• Bridge damage detection using AI and remote sensing