AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions
Ubada El Joulani
Department of Electronic and Electrical Engineering
Brunel University of London
[Ubada.Eljoulani@brunel.ac.uk](mailto:Ubada.Eljoulani@brunel.ac.uk)
Stergios-Aristoteles Mitoulis
The Bartlett School of Sustainable Construction,
University College London
S.Mitoulis@ucl.ac.uk
Tatiana Kalganova
Department of Electronic and Electrical Engineering
Brunel University of London
[Tatiana.Kalganova@brunel.ac.uk](mailto:Tatiana.Kalganova@brunel.ac.uk)
Sotirios Argyroudis
Department of Civil and Environmental Engineering
Brunel University of London
[Sotirios.Argyroudis@brunel.ac.uk](mailto:Sotirios.Argyroudis@brunel)
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. Keywords - damage assessment, machine learning, artificial intelligence, critical infrastructures, natural disasters. 1. Introduction
Transport networks are crucial for the integrity of the economy and social health of any region in the world, thus maintaining them in good condition is of high importance. Climate change is having a big impact on transport networks as well, as common climate threats include large
precipitations, high temperatures and rising sea levels, which lead then to biophysical impacts such as floodings, erosion, and urban heat islands, which reduces road safety and durability. There are also direct impacts that refers to the actual damage to the infrastructures and indirect damages due to the cascading events. Consequently, fostering climate-resilient infrastructure is becoming essential for the economic
(https://sdgs.un.org/goals)
(https://sdgs.un.org/goals)
Given these threats, critical infrastructures require quick damage assessment to enable informed decision making and on time restoration avoiding cascading impacts. This need is especially highlighted in challenging zones, such as areas under war or other disruptive events. The use of remote sensing technologies and satellites is crucial here, as data collection in these areas is often defined by security risks and restricted access making on-ground data hard to obtain. Key methods to address this challenge involve damage assessment using satellite images, which can be sourced from open-access platforms or commercial providers. A prominent example is the ESA (European Space Agency) Sentinel mission, which provides valuable data through radar imaging and multispectral high-resolution imaging. For instance, researchers have developed multi-scale approaches that integrate
Sentinel-1 SAR images with high-resolution imagery and deep learning for rapid post-disaster infrastructure damage detection. While there have been previous studies on damage detection, they have often focused on single transport infrastructures such as roads, bridges or buildings in
isolation. Existing literature reviews have also covered related topics, for example, provided a systematic review of Machine Learning for general civil infrastructure damage using methods like vibration and image analysis, while presented a broader overview of AI applications across the transport sector without a specific focus on structural damage. However, a comprehensive review is needed to compare current AI models and datasets for assessing road and bridge damage, as this is missing from the literature. Crucially, this review is motivated by another identified gap in the literature: while satellite technology like Synthetic Aperture Radar (SAR, a satellite-radar imaging technique that uses motion of a radar antenna to create high resolution images of the earth surface) is used for monitoring, its integration with advanced AI models for holistic bridge damage assessment remains largely unexplored. This review aims to address this gap. It will synthesize the latest emerging technologies and AI models,
from the detection of localized road potholes to wider regional damage assessments, providing a foundation for developing AI-driven solutions that enhance the monitoring and resilience of critical transport infrastructures. The rapid adoption of these technologies necessitates a careful consideration of ethical AI
principles. These concerns include fairness,
transparency, privacy, and accountability. AI models for damage assessment could cause societal inequalities if trained on biased datasets. An AI system trained predominantly on urban or affluent area imagery might underperform in rural regions, leading to inequitable allocation of repair resources and marginalization of vulnerable groups. This raises the question for accountability, which demands mechanism to ensure responsibility for an AI system’s outcome and provide compensation when its decision cause harm. Furthermore, the system must be transparent and explainable, making their functionality clear and understandable to build and maintain user trust. The use of high-resolution satellite imagery also introduces significant privacy and data governance concerns that must be addressed to protect individuals and ensure data is used responsibly. The increasing use of AI in managing critical infrastructure demands significant policy and risk management reform, as some current regulations are inadequate, where rather than a single,
universally accepted legal framework, countries are adopting varied approaches. The European Union has passed a comprehensive EU AI Act, while the UK is pursuing a more flexible and principlesbased approach.
There is need for new policies that standardize data quality, model validation and operational transparency, as poor data quality leads to flawed and unreliable AI models. In terms of transparency, there is a global push to make AI systems more transparent and explainable so that they can be trusted and held accountable( What Is Explainable AI (XAI)? | IBM, 2025). It is also crucial to address the emerging landscape of AI-generated threats. The same technology can be used for malicious ends, and citing few examples, this includes Bioterrorism,
Unleashing AI agents, persuasive AIs and Concentration of Power and other found in this publication. Therefore, the successful application of these technologies for infrastructure assessment requires navigating the challenges of ensuring ethical performance, establishing robust governance and policy, and safeguarding the assessment process from digital interference. 2. Methodology
This review paper employs a systematic approach to evaluate existing research and compare the different findings and applications of AI models and datasets availability. While the literature demonstrates significant literature, particularly in road damage detection, our initial analysis confirmed a scarcity of research combining AI, SAR, and bridge damage assessment.
Therefore, this review aims to provide a comprehensive evaluation of current findings and highlight directions for future research. Articles were included based on a series of criteria which includes the relevance of AI models and their application for damage assessment on transport infrastructures (roads, bridges, etc.),
availability of the datasets that correspond to the tables’ columns (i.e. for AI models, the
accuracy). As for the eligibility criteria for article searching, we considered the most recent
articles, including articles up to 10 years old, except for same cases where articles were scarce. The language of these articles is English, for ease of comparison and readability. Both Scopus and Google Scholar were utilised for the identification and review of relevant articles. The search terms are the titles identified for each table corresponding to a specific research question. We excluded articles that are not relevant to the research question identified for each table.
Figure 1. Review process diagram methodology
For wider damage assessment, we looked into technologies that use satellite imagery as well, and more specifically SAR. This technology has been used with varied methodology depending on applications, such as
MTInSAR (Multi-Temporal Interferometric SAR), InSAR (Interferometric SAR) and D-TomoSAR (Differential Tomographic SAR). For instance, MTInSAR has been used for monitoring of structural deformation in bridge portfolios, as demonstrated by. InSAR has also been used for similar applications, such as long-term deflection and thermal dilation of bridges. D-TomoSAR is similar to the previously mentioned methods, but it uses multiple radar images acquired from different viewing angles to create 3D model of the deformed infrastructure. In, the authors have used D-TomoSAR for monitoring landmark bridge using displacement products deformation trends. Another application of SAR
includes using coherence products (correlation of radar signal between two or more acquisitions of the same area) for assessing the damage on infrastructures, as demonstrated by and, where Coherence Change Detection (CCD) has been used, in which two temporal high-resolution SAR images are compared to detect and measure changes to a specific geographic area, as described in Figure 2. Table 1. SAR RGB decomposition
Small contribution to Colour Band Polarization pixel indicates
Large contribution to pixel indicates
Surface scattering Red Co-Pol (VV) Smooth surface Rough surface (polarized/simple)
High volume (trees,
buildings, mature crops,
built up areas)
Co-Pol backscatter
values near -24dB
(smooth water, roads)
Cross-Pol Green
(VH)
Low volume (water,
roads, plowed or newly planted fields)
Scattering measurable in red channel, no value
Volume scattering
(depolarized/random)
Surface scattering when volume scattering is very low
Blue
C-Pol when
Cross-Pol
near 0dB
Figure 2. SAR acquisition for CCD
Some applications of SAR for have used RGB composite images using various SAR data parameters, where different polarizations and frequencies of radar signals are combined into a multi-band visualization. These RGB composite images can be used in AI
models using computer vision algorithms, improving the capabilities of the system to assess damage for transport infrastructures. In Table 1, the difference between colours is shown, with what features can be identified in each channel.
3. Results and Discussion
In this section, an analysis of current available AI models and datasets in this field are carried out along with an investigation on robust solutions for data collection. Through the literature review, the emerging digital technologies and system resilience will be also explored. The findings are presented in table style, along with a discussion regarding the damages on civil infrastructures that have been studied, along with available datasets and AI models deployed. Consequently, an analysis on the use of SAR for damage detection is presented, and lastly a discussion on natural hazards related to damaged civil infrastructures. 3.1 Damages in critical civil infrastructure used for analysis in AI approaches
Table 2 summarises the most common types of civil infrastructure damage that have been identified, which allows us to have a clear view of what the most common ones are. Although some have used different criteria, such as good, fair and poor (referring to the state of the road) as performed by. When searching for publications, we found that most of the damage detection models were applied on road infrastructure instead of buildings. When searching on Google Scholar “Road Damage Detection” since 2012 to 2025 the search gives us more papers compared to when searching “Building Damage Detection”. This explains that there has been more research and applications on road infrastructures, detecting cracks and potholes, compared to buildings. The above table tells us that most of the cracks identified are classified into lateral, longitudinal, alligator, and other general cracks. Table 2. Damages in critical civil infrastructure used for analysis in AI approaches
Author / Year Roads
Longitudinal
Alligator Lateral cracks cracks cracks
Alligator Other road
Cracks cracks components
Mesh cracks, pothole, longitudinal and lateral construction joint, crosswalk blur, white line
blur. Patches, scratches, bleeding, manholes, curb, cobblestone, drill
holes, vegetation, joints,
water drains. Patches, nets, xxxx
manholes
Reflective crack, block
crack, sealed reflective crack, lane
long. crack, sealed
long. crack
Buildings
Minor Major Destroyed Ruin Other
dama e
damage
xxx Undamaged
undamaged
xxx Undamaged
Debris, Spalling, x 2022) Crackin
Cracking
UNOSAT 5-level
2019) scale
, as well as more advanced deep learning models, for instance CNNs (Convolutional Neural Network),,. In Table 3, a series of these AI models are presented and compared. The references are presented in the first column, the name of the model in the second column, and lastly the source of the data, commonly Terrestrial or Satellite. Here the infrastructures are roads, buildings, bridges, and other (which is only a steel structural model, to show an example of simulation of damage detection). Table 3. Overview of AI approaches for damage detection in buildings, bridges and roads, including algorithms, datasets, and data sources. Abbreviations
Author / Year Algorithm Dataset Source
Roads
YOLOv9s-Fusion RDD2022 Terrestrial
Potholes or Cracks on Road Image YOLOtinyv4 Terrestrial
Dataset
YOLOv11+FPN+Crad-CAM Multiple publicly available Terrestrial
Faster R-CNN, YOLOv5, SSD RDD2022 Terrestrial MobileNet V1, EfficientDet D1
Pothole dataset, Road Damage LRDD-YOLO Terrestrial
Dataset
Faster R-CNN Custom Terrestrial
RDD-YOLO RDD2022 Terrestrial
YOLOv8-PD RDD2022 Terrestrial
LAG-YOLO RDD2020 Terrestrial
YOLOv7 RDD2022 Terrestrial
YOLOv5s RDD2020 Terrestrial
YOLOv3 LIST dataset Terrestrial
u-YOLO with EM&EP GRDDC Terrestrial
CRACK500, GAPs384, Cracktree200, FPHBN Terrestrial
CFD, (Aigle-RN & ESAR & LCMS)
ConnCrack (GANs) EdmCrack600 Terrestrial
YOLOv2, Faster RCNN PID (pavement image dataset) Terrestrial
RetinaNet Custom Terrestrial
Edge detector and Custom Terrestrial segmentation
ResNet34 160x160 GAPs V2 Terrestrial
AlexNet DCNN SDNET2018 Terrestrial
SSD-Inception v2 RoadDamageDetector Terrestrial
1 EM: Ensemble Model, EP: Ensemble Prediction, ASPP: Atrous Spatial Pyramid Pooling, U-BDD++: Improved unsupervised building damage detection, FV: Fisher vector, FPN: Feature Pyramid Network, RSF: Random Structured Forests, RDF: Random Decision Forests, BPNN: Back-Propagation NN, FPHBN: feature pyramid and hierarchical boosting network, SCWT: Synchrosqueezing Continuous Wavelet Transform, YOLO: You Only Look Once, CNN: Convolutional Neural Network, GAN: Generative Adversarial Network, SSD: Single Shot Detector Fuzzy c-means Custom Terrestrial
FV-CNN Custom – Google street view Terrestrial
CrackForest (RSF+RDF) CFD, AigleRN Terrestrial
Convnets Custom Terrestrial
BPNN ARAN dataset Terrestrial
Buildings
ResNet, U-Net Custom Satellite
RNN Custom Satellite
CNN xBD Satellite
Bayesian networks xBD Satellite
U-Net Custom Satellite
Hierarchical Transformer xBD, Ida-BD, LEVIR-CD Satellite
U-BDD++ xBD Satellite
DNN xBD Satellite
LA-YOLOv5 GDBDA Terrestrial
Mask R-CNN with FPN xBD Satellite
ResNet50, CNN xBD Satellite
AlexNet CNN Custom Satellite
Bridges
Xception CODEBRIM Terrestrial
Vanilla
SCWT & ResNet with signal Z24 bridge Terrestrial splitting
GoogleNet Crack-detection Terrestrial
YOLOv7 RDD2022
Mask R-CNN RC-bridge Terrestrial
MetaQNN and ENAS CODEBRIM Terrestrial
CNN with ASPP Crack-detection Terrestrial
In the case of buildings, most of these publications are related to building damage related to natural disasters, as shown in for earthquake and,, and for hurricanes. These recent publications relied mainly on the publicly available dataset xBD. have presented an innovative model, where the authors have achieved an F1 score of 0.582 for the tasks of localization and segmentation, and an F1 score of 0.638 for the tasks of damage classification.
Here the data used consisted of unlabelled pre and post disaster satellite images pairs. Using satellite is not always the optimal solution due to complexity, so the authors have implemented a novel self-supervised framework, named U-BDD++. Other findings,, show higher accuracy on a different dataset, such as the GDBDA(Ground-level Detection in Building Damage Assessment), where an average (between different classes) F1 score of 0.911 was achieved, using a improved version of YOLOv5 object detection model. A term has been found for the application of Artificial Intelligence to geospatial data from remote sensors such as satellites, aerial drones, and this is GeoAI. GeoAI brings a big potential for Rapid and scaled-up building damage assessment. As for bridges, we observe that most of the publications used CNNs, deep learning models, for damage identification. Some authors have used an improved version of Convolutional Neural Networks, such as Xception, a spatial architecture that is more powerful with less over-fitting problems than current popular CNNs. The authors have utilized Xception model and Vanilla model, achieving respectively an accuracy of 0.9495 and 0.8571 for defect classification of concrete bridges. Other authors have experimented with different models, such as Meta-QNN, a meta-modelling algorithm based on reinforcement learning that generated higher performance architectures automatically, and Synchrosqueezing Continuous Wavelet Transform with deep learning, using acceleration responses for multi-class damage detection. When it comes to roads, there has been a lot of competitions, such as the Global Road Damage Detection, which happened on multiple occasions, like in 2020 and 2022. In fact, we have presented the relative datasets in the below tables, under RDD2020 and RDD2022. There has been some variation to these the Crowdsensing-based Road Damage Detection Challenge (CRDDC). From the table we understand that most of the models used are based on YOLO (You Only Look Once) models, which are two stage detectors: in the first pass it generates the potential object locations, and in the second pass it refines these proposals. A recent study presents a model specifically developed for road damage detection, where the authors based on a previous object detection model YOLOv8n, have proposed an improved version, YOLOv8-PD for Pavement Distress, demonstrating lower computational load and higher detection accuracy. Most recent versions have also been used such as YOLOv11 and YOLOv9, and recently, as the weight of the models are being considered more and more, particularly for edge applications, lighter versions are also being considered, such as YOLOtinyv4. An experiment have been conducted on simulated structures, such as an eight-level steel frame structure, where in, a two-stage structural damage detection method is used, and achieved a high accuracy of 0.9988. However, it has not yet been applied to real world infrastructure, where additional factors influence the performance. Lastly, the majority of these papers have relied on terrestrial data, with limited use of satellite imagery, despite its value in scenarios where access to transport infrastructure is
restricted. 3.3 Datasets used for infrastructure damage detection
In the previous sections, the AI models have been presented, along with what datasets have been used. In this part, these datasets are more deeply analysed. In Table 4 the datasets for the different infrastructures are presented. We can observe how the section for roads is bigger compared to buildings and bridges. This is because the datasets for roads are easier to create compared to buildings and bridges, which requires more sophisticated and advanced acquisition
techniques, as demonstrated later in the table about technologies used for data collection. We can observe that to create a road dataset, a smartphone with a camera is sufficient. Furthermore, there have been numerous competitions for road damage detection like the RDD2020 and
RDD2022, which had a huge success and motivated for more advanced datasets, i.e. including other countries’ roads to improve the model. For instance, in RDD2020 dataset, the data was collected from three different countries: India, Japan and Czech Republic. However, in RDD2022 dataset, the data was collected from six countries: India, Japan, Czech Republic, Norway, the United States and China,
with more than 55,000 instances of road damage. Table 4. Datasets of damaged infrastructures used for detection.
500 2,000x1500
EdmCrack600 Crack. 600 1,920x1080
Block, Lane longitudinal, Longitudinal, Sealed Longitudinal, Pothole, Alligator, Sealed reflective,
Reflective, Transverse. 7,237 640x640
Intact, Cracks, Applied patches, Inlaid patches, GAPs v2 2,468 1,920x1080
Potholes, Open joints. Modified RDD2018
Wheel mark, Construction joint long., Equal interval, Construction joint lat., Partial/Overall pavement, Bump/Rutting, Crosswalk blur, White line blur. 18,034 600x600
G45 Transverse, Longitudinal, Block, Alligator 217 2,048x1,536
NYCDT Poor, Fair, Good. 711,520 640x640
Illumination and light intensity variations, Background asphalt variations, Cracks, Oil stains, Patches, Pebbles, Shadows, other. 75 1,080x1,920
CFD Crack, Non-crack. 118 480x320
Custom Crack, Non-crack. 500 3,264x2,448
Alligator crack, Linear crack: Custom 400 n/a
Transversal crack). BUILDINGS
Collapsed, Heavily damaged, Needs demolished, Custom 13 n/a
Slightly damaged
Custom No Damage, Minor, Moderate, Severe, Destroyed 2,472 n/a
Custom Damaged, undamaged 50 512x512
GDBDA Debris, Collapse, Spalling, Crack.
8,340 800x800
No damage, Minor damage, Major damage, xBD 22,068 1,024x1,024
Destroyed, Unclassified. No damage, Possible Damage, Moderate Custom 75,468 0.3 GSD
Damage, Severe Damage, Destroyed
BRIDGES
Max: 6,000x5,152 displacement. 1,422 Time-series displacement. 2 Void, Inlaid patch, Applied patch, Scaled crack, Crack, Open joint, Pothole, Raveling, Scratch, Bleeding, Road marking, Surface water drain, Manhole, Expansion joint, Curb, Cobblestone, Drill hole, Object mobile, Object fixed, Joint, Road verge, Vegetation, Induction loop, Normal. 3 Crack, Alligator crack, Efflorescence, Rockpocket, Washouts concrete corrosion, Hollowareas, Spalling, Restformwork.,
Wetspot, Rust, Graffity, Weathering, ExposedRebars, Bearing, Expansion joint, Drainage, Protective equipment, Joint tape. Crack-detection Crack, Non-crack. 6,069 224x224
Crack, Spallation. Efflorescence, Exposed Bars, CODEBRIM
2,592x1,944 to 1,590 Corrosion.
6,000x4,000
Corrosion. 6,000x4,000
From the table we can observe the disparity between image sizes across datasets. Some images were collected using specific advanced systems with very high images resolution, such as “Mobile mapping system” named S.T.I.E.R. and RoadSTAR, which have been used in Austria, Switzerland and Germany. For buildings there are fewer datasets, but there is one dataset that used satellite images that is very extensive, including approximately 22 thousand images over 45 kilometres squared of polygon labelled pre and post disaster imagery, the xBD dataset. The custom datasets are collected from the xBD dataset, for specific damages and specific locations, depending on the area of interest. Now there is a recent published one called “Bright” which has data about damaged buildings related to natural disasters. In the context of bridges, there is a noticeable scarcity of publicly available image datasets specifically capturing overall structural damage. This scarcity is particularly acute for datasets suitable for advanced remote sensing techniques like SAR, which directly hinders the development and validation of corresponding AI models. However, several datasets focused on localised defects, particularly concrete cracks in bridge components, are available, such as the widely used CODEBRIM dataset. Vibration based approaches have also been investigated for bridge damage assessment. For example, applied deep neural networks to time-frequency representations of vibration signals to detect structural damage. Their study utilized the Z24 dataset, a well-known benchmark in the field; however, this dataset is not publicly accessible, limiting its broader use in comparative studies. Another notable dataset for bridge damage detection is DACL10, a comprehensive dataset comprising 9,920 images collected from real-world bridge inspections. It supports multi label semantic segmentation and includes annotations for 12 damage types across 6 distinct bridge components, making it a valuable resource for developing and evaluating deep learning models in realistic inspection scenarios. We iterate here the importance of monitoring these structures, like bridges and roads, and identifying the right dataset and model is crucial for efficient restoration works, traffic load management and avoiding disruptions on major routes. Table 5 shows some samples of the data/images in the different roads datasets here showed in Table 4. The images showed are taken randomly from different classes. In the GAPs 10m dataset by, a system of high-resolution imaging was used, and we can see the sample images in Table 5. Another example is the building dataset xBD, which by looking at the table of images, we can understand that the authors have used multi-band
satellite imagery. In summary, this table shows some samples of how the data looks like, without the need of searching the dataset and looking at the images. In Table 6, samples of the bridge datasets used for damage detection are presented as well. Table 5.
Samples images from road datasets and aerial/satellite
Author/Year Open-source Dataset Name Samples
LIST
RDD2020
EdmCrack600
CFD
Table 6. Samples of images from bridge datasets
Author/Year Classes No. of images Samples
DOTA
Multiple classes, including
11,268 Bridge
Bridges 500 Bridge Dataset
AID
Dacl10k
Crack
detection
CODEBRIM
Crack, Spallation, Efflorescence, Exposed Bars,
Corrosion
Multiple classes, including
10,000 Bridge
12 classes
Crack, Non-crack 6,069
1,590
Cracked, Non-cracked 230 SDNET2018
We looked at what datasets about general transport infrastructures are available previously in
Table 4 however here in Table 6 we are visualizing sample images of the damaged bridges datasets we previously saw. As shown in the table, most of these datasets concern concrete cracks on bridges, but not analysing the bridge as a whole or from a wide perspective. The “Image
Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS)” created a centralized platform where researchers can find and explore datasets collected using remote sensing imagery for various applications, such as agriculture,
disaster monitoring and climate change analysis, and the three datasets at the top (DOTA, Bridge Dataset, AID) are taken from this platform, but they don’t have damage information. This is therefore useful for an analysis of transport infrastructures too, as these are open source labelled aerial dataset (satellite view). As these datasets have been analysed, we need to look at what technologies have been used to collect these data, understanding what is the most used one and which one is more restricted. 3.4 Data collection technologies used for infrastructure damage detection
The data collection technologies are presented in Table 7, where we can see that for roads, most of the datasets have been collected using normal smartphones camera, which means collecting data about roads is generally easier compared to collecting data about bridges and other transport infrastructures, and that is because any person could use their devices with camera to capture the status of the roads. In fact, the RDD dataset as we saw in Table 4, it increased from
26,336 images in the 2020 version, to 47,420 in the 2022 version, which also included more
countries. Table 7. Data collection technologies used for damage datasets
Google
Mobile mapping Author/Year Smartphones High-res cameras Optical Device Camera
system
Roads
Street
view
API
x
x
Buildings
Smartphones Satellite
Bridges
Vibration sensors Camera Satellite
DOTA
Bridge
Dataset
AID
x
x
x
x
x
x
x
Natural Disasters
Social media News portals Google API Satellite
We can observe from Table 7 that for buildings and bridges there aren’t many methods for data
collection, as satellites are usually the easier way to get imagery data for these infrastructures. Therefore, for buildings the data collection primarily relies on aerial images and satellite imagery, where this last one is noted for the high efficiency of capturing building damage, especially where access is restricted like in warzones. As for bridges, data collection is also limited, where technologies used are images or vibration sensor, which suggests the reliance on more specialized equipment to capture structural data and suggests the critical importance of bridge structural health as it is a more fragile infrastructure compared to roads. Here there is also data collected from satellite, but it hasn’t been used for damage detection yet. In the case of Natural
Disasters, data collection in this context includes the use of social media (crowdsourcing), news portals and google API. These sources are particularly useful for rapid data gathering, where for a specific study case, the data collector will most likely not be near the disaster, compared to people posting on social media and news journalists. More recently, anew dataset “BRIGHT” has been created collecting many recent natural disasters event, collected using satellite technology
.
In summary, while buildings and bridges require more sophisticated equipment for data
collection, for roads damages even smartphones are enough to gather data, and in the case of natural disasters, unconventional sources like social media and crowdsourcing plays an important role. 3.5 Types of bridge damages
In the analysis of bridge damages, we identified the common terminology used in recent studies. The primary damage types found, which are detailed in the table below, include deflection, deformation, and displacement.
Table 9. Methodologies used to detect bridge damages
Author/Year Method Scope Findings
Classifying damage type,
location, and severity
Detecting stiffness reduction on a numerical bridge model and validating on the real KW51 railway bridge using only train induced responses
Damage quantification on a lab scale continuous beam and real-world steel truss
bridge
Real-time detection of an abrupt brittle cracking event
Monitoring of structural deformations in bridge
portfolios
Real-time dynamic deflection detection
Railway bridge damage detection and classification
CNN framework that uses a self calibrating Vehicle-Bridge Interaction (VBI) model to generate its own labeled training
data
Probabilistic Temporal Autoencoder (PTAE) using CNN and LSTM layers to analyze train induced vibrations, paired with an EWMA control chart for damage assessment
CNN with ASAPSO
hyperparameter optimization and data augmentation for scarce data scenario
Unsupervised anomaly detection
algorithms
MTInSAR: Multi-Temporal
satellite-based differential
interferometry
Multi-point Chain Laser
Reference
GoogLeNet CNN using spectrograms of train-borne
acceleration
Accurately identifies the presence, type, and severity of seized bearings. Overestimates the severity of cracking and is less accurate at locating cracks at low damage levels
Effectively detects subtle,
progressive damage by automatically extracting features from multi-sensor data. Successfully detected structural changes on the KW51 bridge
High accuracy and robust against noise, maintaining low error at a low
Signal-to-Noise Ratio (SNR)
Isolation Forest and OCSVM were most robust for prompt, real-time
detection of the crack. Bridge with ongoing deformations have been identified and prioritized for inspection
Measuring accuracy can reach 1 mm, and the dynamic response is
good
Accurately detected and classified simulated damage with 100% accuracy using measurements from moving train. Markogiannaki et
Monitoring of landmark
bridge
Damage Assessment of
Bridge after flood
Analysis of bridge deformation using SAR
Deflection measurement with Digital Image
Correlation
New measurement method based on inclination of two
points
Long-term deflection and thermal dilation of bridges
Different measurements have been
taken, such as displacement products deformation trends. Four washed-away bridges were identified successfully. Three were missed due to location in radar
shadow. Promising results when postprocessing is correctly applied,
extraction of horizontal & vertical
deformations, results aggregated.
Further research is needed to test
transferability to other
infrastructures. The full-field image displacement maps can be efficiently and accurately calculated
Error of proposed methos is less than 1%
Downward movements at mid spans, implying need for periodic
monitoring
The method is accurate with deflection estimation, also provides rough damage localization
Advanced video deflectometer is developed and can be used for field measurement of bridge deflection
On bridges, using 6 [th] deg. Polynomial function, can predict vertical displacement
al., 2022
D-TomoSAR with engineering data and forensics
Using two temporal SAR images and verifying using satellite optical image. Time-series analysis (Persistent
Scatter Interferometry)
Off axis Digital Image Correlation
Secant inclination
InSAR with Sentinel-1 SAR and
COSMO-SkyMed data
Finite-element model with partial
Bridge deflection estimation least-square regression
Real-Time measurement of Off-axis digital image correlation vertical deflection
Strain and rotation Analysis of bridge deflection measuements, inclinometer Drive-by monitoring is advanced by using CNNs trained with data from self-calibrating numerical models to classify damage type and location. For direct
monitoring, approaches include using probabilistic autoencoders on train-induced vibrations to detect progressive damage or unsupervised algorithms on live sensor data to find abrupt real-world cracks. To address data scarcity, another method uses data augmentation with an adaptively optimized CNN, proving effective with fewshot training samples. MTInSAR leverages multi-temporal satellite data to detect changes over time, and similarly InSAR is applied for long term deflection and thermal dilation analysis, focusing on continuous monitoring. D-TomoSAR is the Differential Tomographic Synthetic Aperture Radar, and it’s used to monitor ground deformation by analysing the differences in radar images taken at different times. A study has used two temporal SAR images to assess bridge damage due to a flood and verified the result using satellite optical imagery. In the case of image-based methods, Digital Image Correlation and Off-axis DIC have been utilized. This is used for deflection measurement by analyzing image displacement maps. The Off-axis DIC uses a video deflectometer to measure this. As for sensor-based methods, an inclinometer has been used to analyze deflection using polynomial functions to predict vertical displacement. Also, secantinclination is also used, which measures inclination between two points to estimate deflection, achieving an error of less than 1%. Satellite-based methods like InSAR and D-TomoSAR are valuable for inaccessible or large-scale
infrastructures, which aligns with remote sensor for challenging environments such as warzones, whereas image-based offer also high-precision for specific damage types such as deflection. The data generated from these satellite-based methods can be further analysed using AI model to classify and quantify damage, which is mentioned in the next tables. The main difference between satellite-based and image-based is the time of monitoring, since methods like Multi chain laser reference and DIC can get immediate response to structural issues, which makes them near real-time, whereas for satellite-based, some processing steps are required to be able to analyze and visualize the results, making them far from real-time, therefore more for long-term
monitoring. Table 10. technologies used for detection of bridge damage
Author/Year Type of data used
Media Sensor SAR Laser
SHM system Accelerometer Accelerometer
MTInSAR
Chain Laser beam
D TomoSAR
SAR
SAR
Video deflectometer side of Rangefinder bridge
InSAR
Inclinometer
Video deflectometer side of Rangefinder bridge
Inclinometer
3.7 Applications of satellite data methods and Synthetic Aperture Radar (SAR)
Satellite imagery and Synthetic Aperture Radar (SAR) have been analysed and seen in the previous tables, however this Table 11 summarises some applications of SAR and the integration with AI where possible. The table is divided into three sections, including General SAR
applications, SAR with Coherence and long-term monitoring. Table 11. applications of satellite SAR methods and uses of AI models
Author/Year Application AI Satellite
Bayesian Building damage assessment Networks
Bayesian Maxar Building damage assessment Networks Sentinel-1
Sentinel-1 Marine oil spill detection Faster R-CNN Radarsat-2
Ship-Iceberg classification Sentinel-1 SVM & CNN
(multispectral images) Sentinel-2
Mapping glacial lakes Landsat 8 (opt) CNN (with optical satellite) Sentinel-1A
Rapid flood segmentation FCNN Sentinel-1
Sentinel-1A Regional glacier mapping No 2018) Radarsat-2
Road segmentation in satellite images FCNN TerraSAR-X
Detection, mapping and analysis of flood propagation with
No Radarsat GIS
Surface water mapping Sentinel-1 No
(with optical satellite) Landsat (opt)
Nationwide Railway monitoring No Radarsat-2
With Coherence product
Rapid post-disaster infrastructure damage characterization enabled by remote sensing and deep learning technologies
SAM (Segment Anything Model)
Maxar
Sentinel-1
Building damage assessment RNN Sentinel-1
Damage detection using SAR coherence statistical analysis,
No Sentinel-1 application to Beirut, Lebanon
Multi-Annual Evaluation of Time Series of Sentinel-1 Inter. No Sentinel-1 Coherence as a tool for Crop Monitoring Deep Learning Framework for SAR Interferometric Phase CNN TerraSAR-X
Restoration and Coherence Estimation
Earthquake Damage Visualization for Rapid Detection of No JAXA ALOS-2
Earthquake-Induced damage
COSMO
Rapid Damage Mapping for 2015 Gorkha Earthquake No
Rapid Damage Mapping for 2015 Gorkha Earthquake No SkyMed,
JAXA ALOS-2
COSMONo Detection SkyMed
SkyMed
DSTO Ingara X Detection of scene changes with Change in Coherence No
Band SAR
Long Term Monitoring
Interpretation of Bridge Health Monitoring Data from Satellite
COSMONo InSAR SkyMed
SkyMed
Multi-Temporal satellite-based differential interferometry for No monitoring structural deformations of bridge portfolios
Sentinel-1
COSMO
SkyMed
Long-Term Deflection Monitoring for Bridges Using X and C
Long-Term Deflection Monitoring for Bridges Using X and C- COSMO No Band Time-Series SAR Interferometry SkyMed
SkyMed
In the first section it’s presented how SAR is useful when it comes to detecting marine oil spills, ship-iceberg detection, glacial lake mappings, road segmentation and water/flood mapping. Here the satellites that have been used include two missions from ESA (European Space Agency),
Sentinel-1 and Sentinel-2, TerraSAR-X, Landsat and Radarsat.
Some of these cases have utilised
AI models, such as Faster R-CNN, Support Vector Machine (SVM) and Convolutional Networks for automated detection and classification. In SAR interferometry, coherence indicates a measure of correlation between two SAR images at different times, where high coherence indicates better interferences and therefore more accurate phase measurements. This is here used for rapid-post disaster infrastructure damage characterization, crop monitoring, earthquake damage visualization and scene
change. The coherence product is mainly taken from Sentinel-1 mission, but also from the German TerraSAR-X, the
Japanese JAXA ALOS-2 and the Italian COSMO-SkyMed mission. Some AI models have been used here as well, but less frequent compared to general SAR application. In this case, SAM
(Segment Anything Model) and CNN are used for tasks like phase restoration and coherence
estimation. Therefore, coherence product can be highly useful when comparing pre- and post event SAR images. Lastly, for Long-term monitoring, there are two cases of bridge health monitoring and multi temporal monitoring of structural deformations, using mainly Sentinel-1 and COSMO-SkyMed,
without any case of using AI models. The table shows the versatility of SAR and its usage across different domains. Also, AI integration shows the potential of machine learning to automate and scale SAR data analysis. The frequent use of Sentinel-1 mission from ESA shows the accessibility of high-quality radar imagery which is crucial for researchers. We also saw how the coherence product can be invaluable for post disaster assessment in challenging environments. The absence of AI usage for long-term monitoring suggests a gap in utilising Machine Learning for continuous infrastructure monitoring, possibly due to the fact that SAR requires long processing times and expertise. Therefore, while SAR offers unique advantages for infrastructure monitoring, it has some challenges, as mentioned above. The complexity of SAR data that arises from the multiple
dimensions, polarizations and frequency, impacts image resolution, sensitivity to surface features and penetration depth. Atmospheric conditions also further complicate it, with effects such as attenuation, ionospheric disturbances, and tropospheric distortions leading to signal loss and reducing image quality. It is also hard to interpret, due to its signal noise, speckle, distortion and scattering effects, presented in grayscale which requires advanced training.
Table 12. Available satellite data for monitoring infrastructures. GSD: Ground Sample Distance
Author Satellite data source Data resolution in GSD Features
MDA. MDA. High resolution imagery, multi-mode operation and COSMO-SkyMed 1m - 100m
dual polarization capability. Italian Space Agency. ,
JAXA ALOS-2 2017) 3m,6m,10m (strimap)
High resolution imagery, L-band SAR, Compact InfraRed Camera, Automatic Ship Identification System. Japanese Aerospace Exploration Agency. High resolution imaging, flexible polarization and left/right looking imaging capabilities. C-Band SAR. Canadian Space Agency. Landsat 8 has narrower spectral bands, improved calibration and signal-to-noise characteristics, high radiometric resolution and more precise geometry
Landsat 7/8 15/30m
compared to Landsat 7. NASA and US.
times, fast product delivery. ESA. Wide-swath, high resolution and multi spectral Sentinel-2, ESA 10m 2012b) imager for earth surface monitoring. ESA. 2012a) times, fast product delivery. ESA. Radar imagery, various imaging modes, high resolution, rapid switching between modes and
polarizations. German Aerospace Centre and
Airbus. Some of the studies above have used damage quantification methods, which have been listed here in Table 13, highlighting their application in real-world scenarios for assessing infrastructure
damage, especially in the context of natural disasters. We can see there are 3 methods used for roads infrastructures, such as PASER, PCI and SDI which are standardized visual survey methods that are crucial for systematic infrastructure maintenance planning. Methods like Hazus and
UNOST are relevant for post disaster assessment. Table 13. Damage quantification methodologies
Damage quantification Author/Year Data source used Infrastructure Case study applications methods
Maxar, Copernicus
Maxar, Copernicus
Natural Hurricane Ian disasters
Natural Hazus Fema Maxar xView2 competition disasters
Indonesia 2018, Mexico City UNOSAT UNITAR Buildings 2019) 2017, Haiti 2010
Weber et al.,
2022
xxxxxxx
Arif et al., 2020
xxxx
Giannakeris
et al., 2018
xx
Muhammad
et al., 2017
x
The hazards listed in Table 14 directly threaten the integrity of transport infrastructures, which are vital for economic and social connectivity. The table suggests that most of these natural hazards are floods and hurricanes, which can cause direct damage to roads and bridges, and as well as indirect impacts through cascading events like traffic disruptions. As presented in Table 13 a range of methods have been employed for damage quantification. In the context of natural disasters, methodologies such as StEER network, HAZUS (developed by FEMA) and UNOSAT are frequently adopted. Specifically, HAZUS-FEMA has been applied for multi-hazard damage classification, encompassing events such as floods, hurricanes, and earthquakes. These tools leverage geospatial data and standardized assessment protocols to support large-scale disaster impact evaluation. The table also includes entries for human-induced hazards, which connects to the discussion of using remote sensing in challenging situations such as warzones. Technologies such as SAR can operate in these areas and are valuable for assessing infrastructure damage in these contexts, as
in. The constraint here is the suitability of methodologies and data in these
contexts, i.e. the suitability of AI model for SAR based monitoring is limited by the slow acquisition and processing of SAR data, and therefore in the case of rapid-onset hazards like the ones above,
timely damage assessment is critical. In Table 15 and Table 16 the datasets and AI models used are presented. A further constraint is the resolution of the satellite technologies, where open-source satellite missions have worse resolution compared to commercial satellites, such as MAXAR, which can achieve a resolution of 0.3m GSD (Ground Sample Distance), as presented in Table 12. This has an impact on the accessibility of resources, especially in the field of research where these are
limited. The suitability of the technologies shown so far is constrained by practical challenged mentioned
before, like data unreadiness, need for faster processing, and better access to high-quality
resources. Addressing these gaps through extended datasets and innovative processing techniques will be crucial for advancing infrastructure resilience, in line with supporting sustainable and climate-aware transport networks.
4. Conclusion
Transport infrastructures are essential to the vitality of modern economies and societies, yet they are still vulnerable to impacts of climate change and natural disasters. Therefore, the demand for rapid damage assessment and monitoring systems is more urgent than ever. In this paper, we examined the transformative role of emerging digital technologies, focusing on AI models and on remote sensing (satellite technology) in strengthening the resilience of transport infrastructures,
such as roads and bridges, and with focus also on buildings. The potential of these technologies,
although remains constrained by practical and data-related challenges. In this review, we highlighted the AI models and datasets used for different infrastructures. A key finding is the significant disparity in research focus: while data and models for road damage detection are abundant, reflecting the ease of data acquisition, there is a distinct scarcity of studies integrating AI with SAR data for comprehensive bridge damage assessment.
Although models such as ResNet50, Attention U-Net and Mask R-CNN show promise, there is still lack of comparative studies especially for satellite imagery-based approaches, and therefore their effectiveness across varied contexts is not yet fully understood. SAR technology with its capabilities and variants (i.e. MTInSAR and D-TomoSAR), it excels in monitoring structural deformation with high precision, however it is still limited by complex data
structures, atmospheric distortions, interpretive challenges and big computational demand. Some initiatives, such as AI4SAR (ICEYE OY (FI), n.d.), are designing solutions by leveraging AI to streamline SAR data processing, hoping to offer more accessible and efficient monitoring. Some key direction to advance the field includes:
Comparative research of newer AI models to determine the most effective solutions for different infrastructure types and hazards, with emphasis on remote technologies such as satellite imagery
- Expand datasets to include underrepresented classes (hazards, infrastructure
categories)
- Multi-sensor integration that merges SAR, optical-imagery and ground-based sensors for a complete assessment of infrastructure health
- Use of AI to optimize SAR data analysis and reducing computational barriers, moving towards near-real-time monitoring
- Explore AI-driven approaches for continuous infrastructure monitoring, especially for critical ones such as bridges. In conclusion, this review covered the latest technologies, including latest AI models and datasets used for damage assessment for various transport infrastructures. Furthermore, we analysed the use of remote technologies, such as satellites, for data acquisition. However, these technologies are constrained by some limitations, as we saw above. As noted in a comprehensive review on data readiness for AI, poor quality data can compromise AI model accuracy, a challenge relevant to the complicated and unstructured nature of SAR data for example and mentioned that while metrics for assessing data readiness for AI are advancing, standardized approaches remain underdeveloped. Initiative like AI4SAR demonstrate progress in leveraging SAR effectively, yet future research must prioritize not only technological advancement, but also robust and standardized metrics for evaluating data readiness specific to transport infrastructures. This will ensure AI driven solutions deliver efficient, reliable and sustainable outcomes. Acknowledgments
This research received funding by the UK Research and Innovation (UKRI) under the UK
government’s Horizon Europe funding guarantee. This is the funding guarantee for the European Union HORIZON-MSCA-2021-SE 01 ReCharged - Climate-aware Resilience for Sustainable
Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies.