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

arXiv: https://arxiv.org/abs/2507.01547 · HTML: https://mlsanity.com/paper/2507.01547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 138 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.06444/) (2019)
- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion](https://mlsanity.com/paper/2004.05525/) (2020)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2004.07312/) (2020)
- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset](https://mlsanity.com/paper/1904.08486/) (2019)
- [BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis](https://mlsanity.com/paper/1506.03495/) (2015)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks](https://mlsanity.com/paper/1802.01445/) (2018)
- [Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses](https://mlsanity.com/paper/1910.11123/) (2019)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images](https://mlsanity.com/paper/1908.03361/) (2019)
- [dacl10k: Benchmark for Semantic Bridge Damage Segmentation](https://mlsanity.com/paper/2309.00460/) (2023)
- [Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management](https://mlsanity.com/paper/1909.08991/) (2019)
- [A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model](https://mlsanity.com/paper/2107.01284/) (2021)
- [Learning Efficient Unsupervised Satellite Image-based Building Damage Detection](https://mlsanity.com/paper/2312.01576/) (2023)
- [DeepInSAR: A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation](https://mlsanity.com/paper/1909.03120/) (2019)
- [Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach](https://mlsanity.com/paper/2401.17759/) (2024)

## 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.