mlsanityData to 7 Oct 2026

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

arXiv 2507.01547 ↗First posted 2 Jul 2025cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 138 references that are in the map. First posted on arXiv 2 Jul 2025.

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26 of 138 references.

References · 26

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  2. 2023An Overview of Catastrophic AI Riskscited by 214
  3. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 138
  4. 2019Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detectioncited by 80
  5. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
  6. 2018Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphonecited by 44
  7. 2025BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responsecited by 39
  8. 2019Building Damage Detection in Satellite Imagery Using Convolutional Neural Networkscited by 34
  9. 2022RDD2022: A multi-national image dataset for automatic Road Damage Detectioncited by 32
  10. 2020Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusioncited by 27
  11. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 25
  12. 2020RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagerycited by 24
14 more
  1. 2022Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Imagescited by 24
  2. 2019Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Datasetcited by 21
  3. 2015BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysiscited by 16
  4. 2024Data Readiness for AI: A 360-Degree Surveycited by 15
  5. 2018Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networkscited by 12
  6. 2019Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distressescited by 10
  7. 2022Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)cited by 9
  8. 2019Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Imagescited by 6
  9. 2023dacl10k: Benchmark for Semantic Bridge Damage Segmentationcited by 6
  10. 2019Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Managementcited by 4
  11. 2021A Novel Disaster Image Dataset and Characteristics Analysis using Attention Modelcited by 4
  12. 2023Learning Efficient Unsupervised Satellite Image-based Building Damage Detectioncited by 3
  13. 2019DeepInSAR: A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimationcited by 1
  14. 2024Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approachcited by 1

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