Despite the technological maturity reached by NDT and EO, important research gaps on standalone technologies and their integration are still unexplored. One challenging issue is the development of monitoring systems based on the integration of sensing technologies with advanced modelling, ICT and position/navigation topics up to IOT and the new concept of citizen engineer. The goal is to provide stakeholders with handy and user-friendly information to support maintenance and controlling major risks.
This Session primarily aims at disseminating contributions from state-of-the-art NDT and EO methods, promoting stand-alone technology and their integration for the development of new investigation/monitoring methods, applications, theoretical and numerical algorithms, and prototypes for sustainable and resilient infrastructure and built environments.
The followings are areas of interest and priority for this Session:
- sensor types, systems and working modes (acoustic/electric/electromagnetic/nuclear/radiography/thermal/optical/vibration sensors; remote and ground-based, embedded sensing systems; stand-alone and integrated multi-source sensing modes);
- advanced processing methods and information analysis techniques (multi-dimensional signal processing; image processing; data processing and information analysis; inversion approaches, AI);
- multi-sensor, multi-temporal and multi-modal data fusion and integration (image fusion; spatio-temporal data fusion; AI and machine learning for data fusion and integration);
- ICT for spatial data infrastructure, distributed computing and decision support systems;
- citizens as “sensors” for defect detection and data collection;
- new NDT applications and EO missions for downstream implementations;
- NDT and EO for new standards, policies and best practices;
- case studies relevant to built environment diagnostics and monitoring.
Railway network is one of the main pillars of modern transport systems, offering safety, energy efficiency and a limited environmental footprint. For this reason, great emphasis should be given on assessing the condition and performance of the railway infrastructure, especially after extreme weather events. Νon-destructive testing offers significant advantages in this context, as it enables continuous inspection without disrupting operation. This study presents the results of a Ground Penetrating Radar (GPR) investigation, carried out on a double-track railway line, following severe flood-related impacts. The aim was to assess the condition of the substructure and identify potential critical locations.
GPR surveys were conducted in both traffic directions using a combination of air-coupled and ground-coupled antennas operating at frequencies of 2.0 GHz, 1.0 GHz, and 400 MHz, allowing detailed characterization of ballast, sub-ballast, and underlying subgrade layers’ thickness. Data acquisition was conducted at operational speeds using a rail-mounted vehicle. The collected raw data were processed using signal enhancement and interpretation techniques, such as filtering, time-zero correction, and stratigraphic analysis. The results were calibrated using available geotechnical information from trial pits and dynamic cone penetration (DCP) tests.
The analysis provided continuous layer thickness estimates at 10 m intervals, revealing both overall structure and local irregularities along the line. While ballast thicknesses were generally consistent (53-59 cm), greater variability was observed in the sub-ballast and subgrade layers, with coefficients of variation exceeding 15-20% in particular sections. Several locations showed abrupt thickness reductions, disrupted stratigraphy, and signal attenuation, and were characterized as potentially critical zones.
The findings confirm that GPR can be effectively used as a non-destructive tool for railway infrastructure assessment, particularly in post-event conditions. The approach supports resilience-oriented asset management by allowing early detection of subsurface anomalies without service disruption and contributing prioritize targeted interventions for sustainable maintenance, and log-term infrastructure safety and performance.
How to cite: Plati, C., Kyriakou, C., and Loizos, A.: Assessment of Railway Substructure Integrity using Ground Penetrating Radar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3490, https://doi.org/10.5194/egusphere-egu26-3490, 2026.
During asphalt pavement construction, compaction degree is a key indicator of quality control, directly affecting pavement service life and long-term performance. When mounted on a roller, air-coupled ground-penetrating radar (GPR) enables real-time pavement compaction evaluation due to its high efficiency, continuous measurement, and large spatial coverage. However, under practical construction conditions, multiple factors jointly affect the propagation and reflection of electromagnetic waves at the pavement surface. Surface moisture from water sprayed onto roller drums, as well as antenna height variations induced by roller vibration, can cause significant fluctuations in reflection amplitude, thereby reducing the accuracy and stability of GPR-based density predictions.
This study develops a time-domain signal correction framework to improve the accuracy of GPR-based density predictions during pavement compaction. The framework was designed to support automated processing by extracting the pavement surface reflection from full GPR signals and mitigating amplitude distortions induced by construction-related disturbances. Specifically, a semi-blind source separation method based on independent component analysis (ICA) was employed to remove surface moisture–related electromagnetic interference. At the same time, an electromagnetic-empirical model relating antenna height to reflection amplitude was introduced to compensate for vibration-induced variations in antenna height. By jointly accounting for these coupled effects within a unified correction strategy, the proposed framework recovered pavement surface reflections representative of dry conditions at a reference height, thereby enhancing the stability and reliability of GPR-based density estimation.
The proposed framework is validated through FDTD-based numerical simulations and field experiments. The results demonstrate that surface moisture effects and roller-induced antenna height variations can be effectively corrected, whether acting individually or in combination, allowing pavement surface reflection amplitudes to be recovered to a dry state at a standard antenna height. This work provides a practical basis for developing real-time GPR-based pavement compaction evaluation methods under complex construction conditions.
How to cite: Yang, M., Ma, T., and Wang, S.: A Noise-Cancellation Pipeline for GPR-Based Asphalt Pavement Compaction Evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3679, https://doi.org/10.5194/egusphere-egu26-3679, 2026.
Ground penetrating radar is used in asphalt pavement condition assessment due to rapid acquisition and sensitivity to dielectric contrasts. For top down cracking, interpretation often relies on diffraction hyperbolas, while the relation between crack geometry and measurable hyperbola descriptors is frequently handled by visual inspection. This study defines a parametric physics based workflow that links detected hyperbolas to crack depth ratio and crack aperture using gprMax forward simulations and automatic hyperbola parameter extraction.
Two dimensional finite difference time domain simulations are performed in gprMax for a layered pavement composed of an asphalt layer over a granular base. Electromagnetic properties are prescribed by relative permittivity and effective conductivity, using relative permittivity 5.50 and conductivity one times ten to the minus four siemens per metre for asphalt, and relative permittivity 6.00 and conductivity one times ten to the minus four siemens per metre for the granular layer. The parametric space includes asphalt thickness between 0.05 and 0.30 m, crack aperture from 2 to 20 mm, crack depth ratio between 0.20 and 1.00 of the asphalt thickness, and antenna central frequencies of 1.6 GHz and 2.3 GHz. Representative configurations are selected from the full combination space.
Synthetic B scans are processed by time zero correction, dewow filtering, background subtraction using trace mean removal, and repeated moving average smoothing. Peak candidates are identified on a central trace defined by the maximum absolute amplitude at an early time sample. Each candidate is tracked laterally by a local maximum search within a symmetric vertical window around the previous pick, yielding a set of points that describe the diffraction trajectory. Each trajectory is parameterised by fitting a quadratic time squared versus offset squared model, with the apex position set by the estimated trajectory centre. The fit provides the apex time, the curvature parameter, and the asymptote slope derived from the curvature. The maximum absolute amplitude along each trajectory is extracted as an amplitude indicator with its space time coordinates. Upper and lower trajectories are assigned by ordering apex times.
The workflow outputs a frequency and thickness conditioned mapping between crack geometry and paired hyperbola descriptors for the upper and lower trajectories, including apex times, curvature based parameters, asymptote slopes, and amplitude indicators. The prediction model is expressed as a conversion from the detected upper and lower hyperbola descriptors, conditioned on frequency, asphalt thickness, and prescribed material properties, to the crack depth ratio and crack aperture. This formulation answers the guiding question by providing an explicit link between measured hyperbola parameters and quantitative crack characteristics under controlled acquisition and material conditions.
How to cite: Neto, G., Fernandes, F., Fontul, S., and Pais, J.: Parametric prediction of GPR diffraction hyperbolas from top-down pavement cracks using gprMax FDTD simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21528, https://doi.org/10.5194/egusphere-egu26-21528, 2026.
Ground-Penetrating Radar (GPR) is a non-invasive sensing technology [1] that exploits the propagation of electromagnetic pulses to investigate opaque media, such as soil, sand, ice, concrete, asphalt, and many others. GPR enables the detection and characterization of dielectric anomalies arising from interfaces, voids, cracks, moisture ingress, reinforcement corrosion, and variations in layer thickness. Owing to its non-destructive nature, GPR has become a key tool for the structural health monitoring of critical infrastructures (e.g. bridges, tunnels, roads, railways, and buildings) thereby contributing to the sustainability, safety, and resilience of the built environment.
Although GPR is a well-established technology employed in a wide range of operational contexts, its performance can be significantly degraded by the presence of noise and clutter, especially when operating in contactless mode and in complex scenarios, f.i. when mounted on mobile vehicles, unmanned aerial platforms, or robotic systems [2]. To overcome these limitations, advanced measurement configurations employing multiple transmitting and receiving antennas have recently been proposed [3]. At the state of art, multistatic radar technology represents a promising solution for mitigating signal disturbances and enhancing subsurface imaging capabilities [4]. However, this technology entails a substantial increase in data volume and computational complexity, thus requiring the development of efficient and robust signal processing and image reconstruction strategies. Within this framework, a key challenge lies in the identification of suitable measurement setups that achieve an optimal trade-off between imaging performance and computational cost.
In this contribution, a performance assessment of different multistatic antenna configurations operating in a three-dimensional free-space scenario and consisting of a single transmitting antenna and multiple receiving antennas is considered. First, a numerical analysis will be conducted to assess the imaging capabilities of the system. Then, experimental results obtained under controlled laboratory conditions will be presented to validate the proposed imaging approach and identify the configurations that provide the best compromise between reconstruction quality and computational cost.
References
- Daniels, David J., ed. Ground Penetrating Radar. Vol. 1. Iet, 2004.
- Catapano I., Gennarelli G., Ludeno G., Noviello C., Esposito G., Soldovieri F., "Contactless Ground Penetrating Radar Imaging: State of the art, challenges, and microwave tomography-based data processing," in IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 1, pp. 251-273, March 2022, doi: 10.1109/MGRS.2021.3082170.
- Masoodi, M.; Gennarelli, G.; Noviello, C.; Catapano, I.; Soldovieri, F. Performance Assessment of Multistatic/Multi-Frequency 3D GPR Imaging by Linear Microwave Tomography. Sensors 2025, 25, 6467.
- Noviello, C.; Braca, P.; Maresca, S. Chapter 5—Radar Networks. In Photonics for Radar Networks and Electronic Warfare Systems; SciTech Publishing, Inc.: Raleigh, NC, USA, 2019; p. 111.
How to cite: Noviello, C., Masoodi, M., Gennarelli, G., Ludeno, G., Esposito, G., Catapano, I., and Soldovieri, F.: Numerical and Experimental Analysis of Multistatic GPR Systems for Subsurface Inspection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12184, https://doi.org/10.5194/egusphere-egu26-12184, 2026.
Ground-penetrating radar (GPR) is at the forefront of nondestructive pavement evaluation techniques in the US, with common applications for pavements’ subsurface and surface investigations. Radar systems are effective in-depth estimation provided accurate dielectric constant is known. In this study, GPR is used for a thorough evaluation of seven different full-depth asphalt concrete (AC) segments at Illinois Certification and Research Track in Trenton, IL. The track includes three stone-matrix asphalt and four dense graded hot-mix asphalt segments with different surface characteristics namely, variation in texture and roughness. The track also includes embedded copper plates at different depths to validate GPR systems. A GPR system mounted on a vehicle was used to collect data at three different vehicular speeds (5, 10, and 20 mph). The evaluation focused on accurately estimating the dielectric constant for the different layers using their reflection amplitudes after various signal corrections. The dielectric constants are then used to predict layer thickness, AC density, and estimate depth of copper plates. The predicted results were compared to design thicknesses, core densities, and as-built copper plates layout, respectively. The results illustrate the importance of GPR signal processing and the power of GPR as a reliable evaluation tool for AC pavements.
How to cite: Chen, Y., Bhusal, G., Abufares, L., and Al-Qadi, I.: Asphalt Concrete Pavement Evaluation with GPR: A Case Study of Layer Thickness Validation and Density Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4095, https://doi.org/10.5194/egusphere-egu26-4095, 2026.
Ground Penetrating Radar (GPR) has been widely used for non-destructive testing to detect reinforcing bars (rebars) in concrete. However, the mechanism-level interpretation and quantitative characterization of GPR responses from corroded rebars remain at an early stage. Existing single-polarization GPR approaches mainly rely on echo amplitude and time-delay features to infer corrosion, yet these responses are highly sensitive to experimental conditions and environmental factors, leading to inconsistent trends [1]. With the advancement of polarimetric GPR, increasing attention has been paid to leveraging polarization information for rebar corrosion detection [2]. Nevertheless, existing polarimetric power decomposition methods often classify rebar returns as being dominated by surface scattering, whereas rebars exhibit a typical linear geometry and should theoretically present a pronounced dipole-scattering component.
To address this issue, we propose a four-component polarimetric decomposition method for rebar scattering characterization and corrosion-state evaluation. Building upon the Dey three-component decomposition [3] and inspired by the Huynen decomposition [4], the proposed method uses the real part of T12, i.e., R{T12} as a key indicator of dipole scattering. This term can be interpreted as a shape-related indicator that tends to be pronounced for line-like targets, enabling a physically interpretable decomposition of the total scattering power into four components: surface scattering, double-bounce scattering, volume scattering, and dipole scattering.
Experiments were conducted using a VNA-based full-polarimetric GPR system equipped with dual-polarized Vivaldi antennas operating from 0.7 to 6 GHz. Reinforced concrete specimens with a 12 mm diameter rebar and a 50 mm concrete cover were tested under an indoor accelerated corrosion setup over 20 days. For each corrosion day, the scattering powers of the four components were computed and normalized, and the mean values were extracted within a region of interest (ROI) centered on the rebar response. The decomposition results indicate that the rebar scattering is primarily governed by dipole and surface scattering. Moreover, the temporal evolution of the decomposed powers over the corrosion period reveals that the dipole scattering power is more sensitive to corrosion progression than the surface scattering component, suggesting it as an effective feature for evaluating corrosion stage and tracking corrosion development.
References
[1] Faris N, Zayed T, Abdelkader E M, et al. Corrosion assessment using ground penetrating radar in reinforced concrete structures: Influential factors and analysis methods[J]. Automation in Construction, 2023, 156: 105130.
[2] Liu H, Zhong J, Ding F, et al. Detection of early-stage rebar corrosion using a polarimetric ground penetrating radar system[J]. Construction and Building Materials, 2022, 317: 125768.
[3] Dey S, Bhattacharya A, Ratha D, et al. Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 3981-3998. DOI:10.1109/TGRS.2020.3010840.
[4] Huynen J R. Stokes matrix parameters and their interpretation in terms of physical target properties[C]//Polarimetry: Radar, infrared, visible, ultraviolet, and X-ray. SPIE, 1990, 1317: 195-207.
How to cite: Zhang, B., Liu, H., Wu, P., and Meng, X.: Rebar corrosion assessment using polarimetric ground penetrating radar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23142, https://doi.org/10.5194/egusphere-egu26-23142, 2026.
Current pavement survey protocols adopted by airport authorities mainly rely on non-destructive testing techniques and visual inspections. Although effective in quantitatively assessing the structural condition of paved assets, these approaches present several limitations: they do not enable direct, real-time measurement of the superstructure’s reactive behavior under thermal and/or mechanical loading; they lack spatial and temporal consistency due to inspections being scheduled around operational constraints; and they offer limited capability for synergistic integration of data derived from multiple inspection sources.
To address these limitations, this study evaluates the reliability of an alternative structural health monitoring (SHM) system embedded within the primary load-bearing concrete layer of rigid pavements. Specifically, Fiber Bragg Grating (FBG) optical sensors are employed to simultaneously measure strain and temperature in a scaled concrete slab. The main objective is to assess the mechanical and thermal performance of both bare and transduced fiber optic sensors bonded to the bottom surface of the slab.
First, a static bending test conducted at constant temperature on the instrumented laboratory specimen demonstrates sensor durability and good agreement with corresponding numerical simulations. Subsequently, a uniform thermal gradient test on the free slab highlights the sensors’ high responsiveness and produces results consistent with the expected elastic thermal expansion of concrete, while also revealing material limitations related to thermal conductivity and inertia. Finally, a thermal deconvolution algorithm is applied to compensate for temperature-induced wavelength shifts, allowing the isolation of mechanically induced strains.
Overall, the proposed SHM system represents a promising and viable preliminary alternative for real-time monitoring of mechanical load conditions and thermal gradients in rigid pavements, which are increasingly challenged by rising traffic demands and extreme climate conditions.
How to cite: Bianchini Ciampoli, L., Pinto, R., and Benedetto, A.: Experimental Evaluation of FBG Sensors for Real-Time Strain and Temperature Monitoring in Rigid Pavements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5977, https://doi.org/10.5194/egusphere-egu26-5977, 2026.
During the construction and maintenance of prefabricated box culverts in subsea tunnels, accurate assessment of the compactness of the ultra-thin bottom grouting layer is essential for ensuring overall structural stability. Ground Penetrating Radar (GPR) technology could allow non-destructive evaluation of grouting quality by capturing dielectric contrasts between media to identify hidden voids. However, real-time assessment of this 50-mm ultra-thin layer faces significant challenges, as traditional detection methods struggle to adapt to the drastic variations in dielectric properties during the rapid setting process. Furthermore, existing numerical simulations are typically based on preset defect sizes and locations, failing to reproduce the random defect features induced by grout rheology. This limitation results in a lack of high-fidelity training data for intelligent monitoring algorithms.
In this study, a physics-driven dynamic grouting scene-generation and assessment framework was proposed to address the scarcity of monitoring data for the quality of box culvert grouting. Based on the time-varying evolution laws of the grouting material from fluid to solid states, full-cycle electromagnetic characteristic parameters were obtained to establish a dynamic mapping mechanism between grouting age and radar response signals. To address the grouting diffusion mechanism in the heterogeneous structural environment of the culvert bottom, a defect scene reconstruction method was developed to consider the grouting process and the coupling between slurry rheology and gravity. This method simulates non-homogeneous and irregular void morphologies under realistic working conditions, overcoming the limitations of traditional regular geometric modeling. A high-fidelity GPR forward simulation framework was constructed to generate standardized datasets covering different setting sequences and interface contact states. Furthermore, a stepped frequency continuous wave (SFCW) simulation framework was developed to standardize data processing across different frequency bands, enabling rapid screening and localization of weak grouting zones through target-detection algorithms.
Results demonstrate that the synthetic data generated by this method effectively reflect the signal evolution patterns across different curing stages, resolving the issue of sample scarcity caused by sparse field data. Compared to static models, synthetic datasets incorporating time-varying features and rheological constraints better capture the authentic signal characteristics of early-stage defects. This indicates that improving the data generation paradigm is crucial for achieving intelligent, real-time monitoring of box culvert grouting quality.
How to cite: Zheng, W., Zhou, Y., and Wang, S.: GPR-Based Quality Assessment for Ultra-Thin Grouting Layers in Box Culverts Integrating Rheological Features and Time-Varying Electromagnetic Features , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3792, https://doi.org/10.5194/egusphere-egu26-3792, 2026.
The conservation of ancient architectural heritage remains a fundamental and persistent challenge in cultural heritage management. Wall paintings constitute a significant component of this heritage, representing early and highly valuable forms of artistic expression, particularly within religious and historical buildings from major historical periods such as the Renaissance. Among these works, the mural paintings attributed to Giotto, the founder of modern Western painting and one of the most influential figures in Italian art history, are of outstanding cultural and historical significance.
With the passage of time, wall paintings are increasingly affected by physical and environmental degradation, making their systematic assessment and preservation a critical priority. The identification and characterization of subsurface deterioration within masonry walls and wall paintings, structures that are inherently fragile and multilayered, require the application of reliable non-destructive testing (NDT) techniques. Such deterioration may manifest as subsurface moisture accumulation, voids, or delamination between layers, often induced by environmental factors such as diurnal and seasonal temperature fluctuations, humidity variations, and anthropogenic influences.
Recent advancements in NDT technologies have enabled more detailed investigation of the internal structure of heritage materials. Among these techniques, Ground Penetrating Radar (GPR) has emerged as a particularly effective tool due to its rapid data acquisition, cost-effectiveness compared to destructive methods, portability, and suitability for non-invasive time-lapse monitoring, as well as its capability to provide high-resolution two-dimensional and three-dimensional imaging of subsurface features. Despite its potential, the application of GPR to wall paintings remains limited, primarily due to challenges associated with data processing and interpretation in complex, thin-layered media [1, 2].
This study aims to address these limitations by developing and applying advanced GPR processing and interpretation strategies for improving the detection and characterization of subsurface defects and material heterogeneities within wall paintings. Considering the limited thickness of the plaster and painted layers, a 2 GHz GPR system with crossed polarized antennas was employed to maximize spatial resolution. Although the high operating frequency restricts penetration depth, it enables detailed imaging of near-surface features that are critical for the diagnostic assessment of wall paintings.
Keywords: Ground Penetrating Radar; Cultural Heritage; Wall Paintings; Non-Destructive Testing; Giotto
Acknowledgement: The authors would like to acknowledge the fruitful visiting scholar exchange between the University of West London (UWL) Faringdon Centre and the University of Florence, which significantly contributed to the successful completion of this study. Additionally, the authors would like to thank Dr. Maria Rosa Lanfranchi (OPD), the restorer, for the contribution to this work.
References
1. Napoli, A. F., Marchetti, E., Coli, M., Ciuffreda, A. L., Morandi, D., Papeschi, P., and Agostini, B.: Application of Ground Penetrating Radar (GPR) analysis on San Giovanni's Baptistery in Florence, EGU 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12917, https://doi.org/10.5194/egusphere-egu24-12917.
2. Ortega-Ramirez, M. Bano, L. A. Villa Alvarado, D. Medellin Martinez, R. Rivero-Chong, C. L. Motolinia-Temol, High-resolution 3D GPR applied in the diagnostic of the detachment and cracks in pre-Hispanic mural paintings at “Templo Rojo,” Cacaxtla, Tlaxcala, Mexico. Journal of Cultural Heritage 50 (2021) 61-72, doi:https://doi.org/10.1016/j.culher.2021.06.008.
How to cite: Napoli, A. F., Parnow, S., Marchetti, E., and Tosti, F.: A High-resolution Ground-Penetrating Radar Framework for Detecting Subsurface Discontinuities in Historic Wall Paintings: A Case Study of Giotto's Mural Paintings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20938, https://doi.org/10.5194/egusphere-egu26-20938, 2026.
Historical buildings’ preservation is one the toughest challenges addressed by geoscientist in the field of Cultural Heritage as edifices’ state of conservation should be frequently monitored. This procedure becomes even more urgent in regions characterized by seismic events, since medium-to-high magnitude earthquakes might determine significant and expensive damages. The most common effects of degradation can be investigated with geophysical methodologies since both chemical weathering and mechanical deterioration generally ensure sufficient contrasts of the surveyed physical parameters (e.g. electrical resistivity and relative permittivity). In particular, Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) methods are commonly selected for their logistical simplicity, still granting high resolution results relatively quickly [1] with a non-destructive approach. The case study here presented involves Castellina Museum, an important medieval building in the city of Norcia (Umbria region, Central Italy). Being situated in an active seismic area, this edifice has faced several reconstructions due to the medium-to-high magnitude earthquakes occurred in the last centuries like in the case of 2016-2017 Central Italy seismic sequence when a 6.5 Mw mainshock [2] caused fatalities and damages to the buildings around Norcia. Therefore, to evaluate both the state of conservation and potential aftershocks damages, multi-methodological non-destructive geophysical surveys were conducted over a significant internal masonry wall which location, according to historical documentations, suggests its correspondence to the façade of a previous building, named “Palazzo del Podestà”. An intensive GPR campaign was carried out to evaluate the geometrical arrangement of constructive elements forming the medium. First results provided a peculiar GPR signature, confirming the expected heterogeneous texture and size of such blocks, similarly to the exposed sectors of the wall. However, from the basal floor, electromagnetic signal attenuation occurred over a large portion of the wall. Therefore, ERT was then employed to investigate the variation of electrical parameters [3]. This survey confirmed that the area affected by strong electromagnetic attenuation, are also characterized by electrical resistivity values significantly lower than the ones of neighbouring zones. Therefore, further investigations are needed to better understand the reasons behind this process. This study underlines the importance of employing complementary geophysical methods to achieve a deeper understanding of the studied problem improving the quality of the interpretation needed to define strategic planes for preservation of Cultural Heritages buildings.
Reference
[1] Ercoli, M.; Brigante, R.; Radicioni, F.; Pauselli, C.; Mazzocca, M.; Centi, G.; Stoppini, A. Inside the Polygonal Walls of Amelia (Central Italy): A Multidisciplinary Data Integration, Encompassing Geodetic Monitoring and Geophysical Prospections. Journal of Applied Geophysics 2016, 127, 31–44, doi:10.1016/j.jappgeo.2016.02.003.
[2] Porreca, M.; Minelli, G.; Ercoli, M.; Brobia, A.; Mancinelli, P.; Cruciani, F.; Giorgetti, C.; Carboni, F.; Mirabella, F.; Cavinato, G.; et al. Seismic Reflection Profiles and Subsurface Geology of the Area Interested by the 2016–2017 Earthquake Sequence (Central Italy). Tectonics 2018, 37, 1116–1137, doi:10.1002/2017TC004915.
[3] Leucci, G. Ground Penetrating Radar: The Electromagnetic Signal Attenuation and Maximum Penetration Depth. Scholarly Research Exchange 2008, 2008, 1–7, doi:10.3814/2008/926091.
How to cite: Alaia, G., Ercoli, M., Mazzocca, M., and Cavalagli, N.: Multi-methodological geophysical characterization for the preservation of Cultural Heritage in seismic area: the case of Museo della Castellina in Norcia (Central Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11757, https://doi.org/10.5194/egusphere-egu26-11757, 2026.
Runways and taxiways constitute a vital component of an airport hub. They are engineered to endure for decades; however, there are reports indicating that they may deteriorate prior to the completion of their designated lifespan, as seen in cases where 30-year designs fail within the first decade. Such deterioration includes premature distress indicators such as rutting, mud pumping, concrete slab sinking, and reflective cracking, often linked to specific material factors such as 100% Ordinary Portland Cement (OPC) design mixes. These issues can be influenced by factors such as subgrade condition, drainage quality, seasonal moisture variations, and geotechnical stability. Furthermore, factors such as ambient temperature and construction workmanship can significantly reduce the design life; if the pavement is not cured properly or poured correctly, it becomes highly susceptible to early-stage failure.
Present monitoring practices are predominantly reliant on limited in-situ testing and visual surveys, which often fail to capture the rate of deterioration at a network-wide scale or the root cause of early pavement failure. The advancement of remote sensing technologies, including Synthetic Aperture Radar (SAR) time-series, provides a reliable instrument for the precise monitoring of millimetric-scale deformations across extensive airport areas [1]. However, a comprehensive linkage between SAR observations, pavement failure mechanics, surface distress evolution, and long-term asset management decision making is still at an early stage.
In this study, we propose a prototype multi-sensor framework designed for the early detection and characterisation of airport pavement failures. This framework integrates satellite InSAR technology for long-term and seasonal deformation time-series analysis, high-resolution optical imagery for surface distress mapping, and incorporates detailed in-situ pavement investigations. The methodology examines the correlation between seasonality of SAR displacement and ambient temperature, weather, or traffic records to separate consolidation, settlements, and thermoelastic responses. The framework evaluates the utility of these data streams to identify trend profiles that may help characterise the speed of deterioration in "difficult" sections, such as those experiencing mud pumping or sinking bays [2]. The distress metrics and their temporal evolution are extracted from co-registered optical products to track the progression of visible surface damage. In-situ observations serve to validate and provide structural and materials ground truth [3]. The combination of multi-source data facilitates deformation features relating to environmental drivers. These data sources are essential to better understand the pavement states and different scenarios of changes over time. This approach supports asset owners in moving toward data-driven pavement management and optimal budget allocation.
Keywords: InSAR Time-Series Analysis; Airport Asset Management; Pavement Deterioration Modelling; Multi-modal Remote Sensing; Infrastructure Resilience
References
[1] Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20.
[2] Gagliardi, V.; Bianchini Ciampoli, L.; Trevisani, S.; D’Amico, F.; Alani, A.M.; Benedetto, A.; Tosti, F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors 2021, 21, 5769.
[3] Asadollahkhan Vali, A. (2022). Airport Pavement Management System: Assessing current condition and estimating remaining life from aircraft demand. Spectrum Research Repository.
How to cite: Tessema, T., Ardakanian, A., Bolton, M., Fairley, R., Karastamati, A., Smith, R., Fernandez, J., and Tosti, F.: Multi-modal Remote Sensing and in-situ Sensors Integration for Advanced Airport Asset Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19237, https://doi.org/10.5194/egusphere-egu26-19237, 2026.
The efficiency and functionality of road networks deeply influence the economic and social development of a country. In contrast, when road infrastructure no longer fulfills the required standards, it could represent a serious concern in terms of safety of the transportations system. Indeed, during their lifetime, flexible pavements are subject to continuous aging and degradation due to both environmental factors and traffic loads. To maintain a high level of service and safety, it is essential to monitor this phenomenon to ensure timely scheduling of effective maintenance. Traditional monitoring techniques mainly consist in visual inspection of road segments along with different in situ measurements expressing the superficial condition and the bearing capacity of the pavement. Nevertheless, the final assessment of damage is often qualitative and limited to the observation points. In addition, these methods are expensive, labor intensive and time consuming and results inefficient at a large-scale level.
In this context, the use of remote sensing techniques has progressed in recent years as it offers a highly-productive nondestructive method for evaluating road conditions. These new techniques hold many advantages and provide an opportunity for frequent, comprehensive, and quantitative surveys of transportation infrastructures. Remote sensors can acquire the emitted and reflected energies of the target in different parts of the electric spectrum, and they can be used in the identification and characterization of distresses and aging processes of asphalt mixtures. These technologies can be implemented from various platforms, such as UAVs, airplanes and satellites, characterized by different resolutions and used for different applications.
In particular, the use of satellite imagery is remarkably promising as it enables continuous, large-scale observation of flexible pavement networks, with the possibility to have access to historical datasets, thereby allowing a long-term assessment of the pavement conditions. Despite the limited ground resolution characterizing most of multispectral and hyperspectral satellites, the collected imagery enables quantitative monitoring of flexible pavements through analysis of surface reflectance characteristics across visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) wavelengths. In fact, in these regions variations in asphalt reflectance spectra are directly associated with material aging, oxidation, asphalt content reduction and aggregate exposure. It has been found that the reflectance spectrum tends to generally increase over time due to the aging phenomenon. In addition, the loss of hydrocarbons causes the vanishing of the absorption properties at 1700 nm and 2300 nm, while the aggregate exposure results in the appearance of absorption features at 520, 670 and 870 nm. These changes are evaluated by using two main indicators: the VIS2 (830 nm-490 nm) and the SWIR range (2120 nm-2340 nm).
This study reports on the feasibility of using satellite spectral products to monitor the aging of flexible pavements. Following a pro & cons analysis of this survey methodology compared to other techniques, the promising results obtained by an application over three large, paved areas located inside an airfield is presented. This research was conducted within the framework of I4DP_SCIENCE RESCUE_SAT project (Agreement n. 2025-2-HB.0), in collaboration with the Italian Space Agency (ASI).
How to cite: Sanvitale, G., Bianchini Ciampoli, L., Gagliardi, V., Tapete, D., and Benedetto, A.: Potential of satellite spectral imagery applications for monitoring flexible pavement aging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5371, https://doi.org/10.5194/egusphere-egu26-5371, 2026.
Bridges are a core element of transport systems, enabling connectivity and supporting access to employment, education, medical care and emergency response. Despite this central role, they are also among the most fragile assets in these networks, as they are frequently exposed to natural hazards whose occurrence and intensity are expected to grow under changing climatic conditions. Although assessing geo-hazard risk to bridges is essential for meeting the United Nations Sustainable Development Goals, current risk evaluation practices rarely account for how structural vulnerability evolves over time. In particular, they overlook the contribution of continuous monitoring technologies such as Structural Health Monitoring (SHM) sensors and Interferometric Synthetic Aperture radar (InSAR), which can provide ongoing information on bridge condition. Furthermore, while SHM installations remain limited, the global capacity of InSAR to complement these systems for bridge surveillance has not yet been systematically quantified.
This study introduces a new framework for assessing bridge geo-hazard risk worldwide that explicitly incorporates the availability of both ground-based SHM and satellite-derived monitoring. The assessment integrates subsidence and landslide hazards with measures of exposure and structural vulnerability.
A global analysis of satellite monitoring coverage reveals a substantial shortfall in current observation capability. Only a small fraction of long-span bridges is equipped with SHM systems, whereas InSAR observations from Sentinel-1 could potentially cover a far larger share of the global bridge inventory. Expanding the use of this spaceborne data could therefore lower overall geo-hazard risk and reduce the number of bridges categorised as high risk. Many of the structures that would remain in the high-risk category are also well-suited to satellite-based monitoring, underlining the value of InSAR for improving safety and resilience, particularly in low-income and resource-constrained regions. By linking risk with monitoring suitability, the proposed framework highlights that the presence of SHM and InSAR sensors enables more dynamic and time-sensitive risk evaluation, providing practical guidance for prioritising satellite monitoring, SHM deployment, and on-site inspections within a risk-informed decision-making process.
How to cite: Malinowska, D., Milillo, P., Reale, C., Blenkinsopp, C., and Giardina, G.: Risk assessment of bridges affected by subsidence and landslides using ground and spaceborne monitoring: a global study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13903, https://doi.org/10.5194/egusphere-egu26-13903, 2026.
The increasing frequency and intensity of flood events driven by ongoing climatic changes are exerting substantial pressure on ecosystems, productive activities, and the resilience of critical infrastructure. As a result, climate-change adaptation strategies are progressively focusing on mitigating their impacts. Numerical hydraulic and hydrological forecasting remains the principal tool for supporting prevention and protection policies, relying on Digital Terrain Models (DTMs), including Digital Elevation Models (DEMs), and land-cover information. In this context, satellite remote sensing has the unique ability to cover large spatial extents with high spatial resolution (below the meter scale) and, at the same time, to provide updated data with each new orbital acquisition. In hydrological modelling, coarser terrain data (approximately 10 m) are generally sufficient for simulating rainfall-runoff dynamics, whereas hydraulic models that resolve flood propagation require substantially finer spatial detail, typically on the order of 1 m.
The RESCUE_SAT project (Agreement n. 2025-2-HB.0), funded by the Italian Space Agency (ASI) under the “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) programme, integrates advanced hydrological and hydraulic analyses from the RESCUE model [1] with multi-scale satellite Earth Observation (EO) data. Its primary objective is to enhance flood-modelling capabilities by assimilating high-resolution EO information into rainfall-runoff simulations, thereby enabling a unified framework capable of representing both large-scale hydrological behaviour and local hydraulic processes, including flow interactions with structures such as bridge piers and embankments. By integrating the computational efficiency of DEM-based analyses with advanced hydrological and hydraulic modelling, RESCUE_SAT aims to generate physically based flood maps while maintaining time-effective workflows [2].
To this purpose, ASI’s COSMO-SkyMed (CSK) SAR products are processed using an InSAR approach to derive DEMs with a spatial resolution of 3 m over selected case-study areas in the Latium Region, Italy. The resulting DEM is then compared with other elevation products, including the SRTM v3 DEM (3 arc seconds, with a 90 m spatial resolution) [3] and a LiDAR-derived DEMs from the National Geoportal - MASE [4] with a spatial resolutions of 1 m. The CSK DEM is expected to enhance the detection of flood-prone areas, particularly where natural flow paths interact with infrastructure. RESCUE_SAT also incorporates ground-based GNSS and UAV surveys, integrated during calibration and validation to characterize local-scale processes in settings where infrastructure influences surface-water dynamics, thereby highlighting the value of multi-source satellite data for medium to large‑scale flood-risk assessment and infrastructure resilience.
References
[1] Pavesi, L., et al., (2022). RESCUE: A geomorphology-based, hydrologic-hydraulic model for large-scale inundation mapping. Journal of Flood Risk Management, 15(4), e12841
[2] Gagliardi, V., et al., (2025). Enhancing hydraulic risk assessment using next-generation satellite remote sensing: the RESCUE_SAT project. Vol. 13671. SPIE, 2025
[3] Farr, T. G., & Kobrick, M. (2000). Shuttle Radar Topography Mission (SRTM) produces a near-global digital elevation model. Eos, Transactions of the American Geophysical Union, 81(48), 583–585.
[4] Ministero dell’Ambiente e della Sicurezza Energetica (MASE). LiDAR data from PST-Geoportale Nazionale
How to cite: Mwangi, R., Gagliardi, V., Sanvitale, G., Cipollini, S., Pavesi, L., Bianchini Ciampoli, L., D'amico, F., Tapete, D., Virelli, M., Ursi, A., Benedetto, A., and Volpi, E.: High-Resolution InSAR-based DEMs for Flood Hazard Analysis: Advances from the RESCUE_SAT Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8415, https://doi.org/10.5194/egusphere-egu26-8415, 2026.
The interconnectedness and complexity of subsurface structures present challenges for their identification and visualisation. To address this, geophysicists routinely integrate multiple non-destructive sensing techniques to map underground utilities. These resulting sensor outputs are predominantly two-dimensional (2D) products, typically visualised as maps and sections, using 2D Geographic Information System (GIS) software [1], or more recently, as three-dimensional (3D) objects that encode depth information. However, despite the use of 3D representations, these visualisations are still commonly viewed via 2D projection media, such as monitors or mobile screens. Since these visualisations directly inform professional interpretation, it is essential to understand how stakeholders, those responsible for analysing, validating, and acting on geophysical data, engage with these platforms in practice.
Advances in three-dimensional visualisation technologies, such as Extended Reality (XR), offer new opportunities to overcome these limitations. XR environments enable the integration of heterogeneous geophysical datasets within a single, interactive spatial framework, potentially enhancing spatial comprehension and interpretative accuracy. Recent studies have consequently begun exploring XR applications for subsurface and geophysical data visualisation [2]. A recent study [3] visualised drone-based Ground Penetrating Radar (GPR) and magnetometric data in a Virtual Reality (VR) prototype, identifying frame-rate instability and high GPU utilisation as key technical limitations.
However, technical performance alone does not determine the success of a visualisation tool; stakeholder perspectives are critical to ensuring XR outputs align with the analytical requirements and decision-making practices of geophysical professionals. Building on prior work, the present study extends this prototype for preliminary user testing with six expert geophysical stakeholders. These participants were selected based on their extensive professional experience, ensuring the evaluation reflects real-world interpretative conditions rather than abstract usability testing. Feedback collected through semi-structured interviews was analysed thematically, yielding four key insights: (1) the necessity of adjustable colour maps to enhance data intensity interpretation; (2) the requirement for interactive selection of colour values to reveal metadata; (3) the importance of stakeholder-centred visualisation design; and (4) the implementation of a data catalogue to allow selective dataset visualisation.
Future work will focus on refining the prototype based on these expert recommendations. This iterative process will involve a second round of evaluation to validate the updates, followed by pilot testing with broader stakeholder groups to evaluate the tool's effectiveness in real-world settings.
Keywords: Extended Reality; Multi-sensor Datasets; Human-in-the-loop; Data Visualisation
Acknowledgements: This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.
References
[1] QGIS.org, "QGIS Geographic Information System," Http://Www.Qgis.Org, vol. 2026, 2026.
[2] M. Janeras, J. Roca, J.A. Gili, O. Pedraza, G. Magnusson, M.A. Núñez-Andrés and K. Franklin, "Using Mixed Reality for the Visualization and Dissemination of Complex 3D Models in Geosciences—Application to the Montserrat Massif (Spain)," Geosciences, vol. 12, -10-07. 2022.
[3] E.D. Atsakpo, F. Mercogliano, S. Uzor, P. Saadati, A. Barone, F. Accomando, R. Castaldo, I. Catapano, P. Tizzani and F. Tosti, "Visualising Multi-Modal Geophysical Data in Extended Reality," 2025 6th International Conference on Computer Vision and Data Mining (ICCVDM), pp. 195, -09-12. 2025.
How to cite: Doe Atsakpo, E., Mercogliano, F., Uzor, S., Parnow, S., Ardakanian, A., Barone, A., Accomando, F., Castaldo, R., Catapano, I., Tizzani, P., and Tosti, F.: Evaluating an Extended Reality Prototype for Multi-Modal Geophysical Data Visualisation Through Expert Stakeholder Interviews, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15105, https://doi.org/10.5194/egusphere-egu26-15105, 2026.
The effective management of civil infrastructure and natural assets represents a significant challenge for local public administrations, particularly due to the need for real-time monitoring strategies to mitigate hydrogeological and structural risks. To bridge the gap between advanced remote-sensing capabilities and municipal governance, this work presents the framework of the “LAB_SAT” Project (Agreement N. 2025-9-HB.0), a pilot initiative promoted by the Italian Space Agency (ASI) under the “Innovation for Downstream Preparation for Public Administrations” (I4DP_PA) programme. The I4DP_PA programme was created with the aim of promoting the development of downstream services and applications and an active involvement of public administrations in their development and in the integration and use of satellite data within land management processes. The project is coordinated by the Municipality of Zagarolo as the lead Public Administration (PA), an Italian local authority in the Lazio Region, in partnership with the Italian Space Agency and in collaboration with two scientific partners, DICITA–Roma Tre University and the Fo.Cu.S.–Sapienza University. The main objective of the project is to establish a prototype of an operational laboratory dedicated to the assessment of environmental hazards and the monitoring of infrastructure, integrating multi-source data from satellite Earth Observation (EO), UAV platforms equipped with multispectral and LiDAR sensors, GNSS, and Terrestrial Laser Scanning (TLS). To this end, the project adopts a multi-sensor approach aimed at developing advanced downstream services, including displacement monitoring through MT-InSAR, change-detection analyses based on multispectral indices. The project integrates EO data by leveraging satellite missions, including SAR observations from the COSMO-SkyMed constellation and multispectral and hyperspectral products from the Sentinel-2 and PRISMA missions. All EO datasets will be analysed within a synergistic framework alongside ground-based information and terrestrial surveys, including GNSS monitoring, drone-based photogrammetry, and Terrestrial Laser Scanning reflectance analyses. Through the application of data-fusion algorithms and dedicated up-scaling and down-scaling techniques, the system generates environmental georeferenced composite indicators derived from multiple sources, fully interoperable with GIS environments and useful for urban planning. To assess the stability of local infrastructure, identify risks affecting strategic assets (e.g. viaducts, historical buildings) and monitor potential impacts from natural hazards such as landslides or soil degradation, specific indicators are employed, each capturing distinct dimensions of infrastructure conditions. A further key component of the project is the development of a Web-GIS digital platform that builds upon the existing framework and reaches full operational capability as one of the project’s final outputs, alongside the creation of a prototype Digital Twin. The LAB_SAT project provides an integrated platform to support planning, civil protection, and mobility decision-making in small municipalities, aiming to serve as a replicable model across Italy. By demonstrating the operational use of satellite-derived information, it enhances the capacity of PA to adopt proactive, evidence-based digital tools and to integrate remote-sensing and non-destructive testing methodologies for the sustainable management of built and natural environments.
Acknowledgements
This research is supported by the Project “LAB_SAT”, accepted and funded by the Italian Space Agency (Agreement N. 2025-9-HB.0) within the Innovation for Downstream Preparation - Public Administrations (I4DP_PA) program
How to cite: Khazaee, M., Manalo, J. R. D., Gagliardi, V., D’Amico, F., Benedetto, A., Panzironi, E., Iacovacci, A., Iacomoni, A., Monardo, B., D’Amato, L., and Candela, L.: The Project “LAB_SAT”: A Laboratory for the Remote Monitoring of Environmental Safety in Built and Natural Assets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10415, https://doi.org/10.5194/egusphere-egu26-10415, 2026.
Lake-Cuitzeo: Dual-Impact Stratigraphy; Cueva da Pedra-Pintada: Dual-Comet Celestial-Cartography.
Brazilian Pedra-Pintada site´s lowest stratum provided 14C and luminescence dates for End-Pleistocene, pre-pottery, Paleoindian middens, with soft sand, charcoal and red pigment layer. That same pigment, chemically identified, was used in cave-wall pictograms above, providing stratigraphic dating (Roosevelt,1996; Michab,1998). These pictograms, in celestial-paleo-cartography, depict two comets (Bujatti-Narbeshuber,1997a).
That lowest, dated, Pedra-Pintada stratigraphic-layer of 30 cm, associated with two comet-pictograms, is most tellingly followed by culturally sterile 30 cm soil, again finally followed by Holocene-middens (65 cm) of thriving pottery-age populations.
The anthropomorphic pictograms encode the concentric comet-nucleus-coma-halo threefold structure as descending head identically consisting of three concentric layers, only one with radiant hair. Dual comet-dust and plasma-tail is encoded as parallel legs extending upwards. Two different comet-stages and two different comet-trajectories are evident: one with concentric comet-head, radiant hair, descending, the other comet-head, without hair, not radiating, burnt out, as concentric impact-crater “fallen-dead”, with parallel legs, tied together, also forming “H”= ”fallen-dead”-symbol.
Magdalenian Impact Sequelae Symbolizations (MISS) by AO-KISS-impact-survivors in Göbelli-Tepe use “H” on T-pillars 43 and 18. The “SNAKES-((H-I-T))-SPOT” decoding formula means: (Prof.Klaus) Schmidt-Never-Assumed-KISS-Equates-Snakes (= Taurid impactors), that “H”= “Hurt” the “I”= “Intact-upright-alive”C “T”-pillar. “Hurt-fallen-dead” symbolization by “I”-column=T-pillar-symbol of ritual-centers-Mid-Atlantic-Plateau-MAP-Civilization, cartographic 90° (“I”=West-up to North-up=”H” ) Solar-Polar-Orientation-Transition, within two-step Mega-tsunami = ((double-brackets)). This abstract-composite-proto-writing symbolizes AO-KISS induced two-step Mid Atlantic Ridge & Plateau Lowering Events (MARPLES) within T-pillar-43-celestial-paleo-cartography (Bujatti-Narbeshuber,2022), precession dated 12.850-13.000 cal BP (Sweatman,2017, 2022).
Pedra-Pintada, dual-comet, pictogram-stratigraphy 14C dated 11.145 + - 135, calibrated to 13.300- 12.750 cal BP (95% confidence level) has mean calibrated age 13,010 cal BP. This fits AO-KISS-Bipolar-Sulfate-Impact-Volcanism-Heptaplet-proxi Laacher-See-Tephra isochrone of 13,006 + - 9 cal BP (Bujatti-Narbeshuber,1997a,Reinig,2021). It also fits earliest Mayan Codex Troano calendar date 13,124 cal BP and maximally 274 years later Continental-Ice (CI)-Carolina-Bays-KISS date 12.850 cal BP.
Dual CI-AO-KISS-MARPLES fit Mexican-Lake-Cuitzeo (MLC) stratigraphy consisting of three-layered-lacustrine “Black-White-Graphite-Mats”.
There 14C-“Dead-Old-Graphite” (DOG-)-Mats” mark P/H-boundary-AO-KISS-MARPLES with Impact-Catastrophic-Climate-Transition (ICCT):
MLC-stratigraphy-level below (2,55 m) confirms by “Black-Mats” from high humidity algal organic carbon, identity with North American “Black-Mats”, following from Continental-Ice-KISS with secondary meteoritic-ice-ejecta-impacts, shaping 500.000 mathematical ellipses, Carolina-Bays, into earlier AO-KISS-“White Mats” (Muck,1976;Davias,2007;Zamora,2015;Bujatti-Narbeshuber,2023).
MLC-stratigraphy-level above (2,70 m) confirms by “White-Mats” identity with North American precursor of Carolina-Bay-formation as bleached-sand A2-horizon, non-fossiliferous-sand Goldsboro-Ridge-Enigma, nearly pure silicate, industrial white glass-production-grade-sand (Daniels&Gamble,1969,1970,1972), K/Pg-like (Senel,2023), European Usselo Horizon (Andronikov,2016), all documenting aeolian-transport by AO-KISS-MARPLES-Megaplume of ocean-floor-magmatic-volcanic-clastic-material in meso-stratospheric Mega-Hunga-Tonga-(2022)-like steam-plume, raining down in torrential-acidic-bleaching waterfalls (Bujatti-Narbeshuber,2022,2023).
MLC-lowest-stratigraphy-level with Nano-Diamond-peak (2,75-2,8 m) as 14C-depleted “DOG-Mats". Disturbing MLC-core 14C dating, below “White Mat” (2,75 m), a 1 cm thick layer, that ”contains thin millimeter-sized-interbeds of black organic carbon…without form or structure” of almost pure elemental carbon, enigmatic, not “plant-derived kerogenous organic-matter”. “Currently, the source of this old carbon remains unclear” (Israde-Alcántara,2012).
Ocean-floor Black-Smokers, producing graphite with very old 14C-ages (Estes, Nature Communications, 2019), should fully account for “DOG-Mats” through AO-KISS-MARPLES-Megaplume aeolian-transport around Azores-Triple-Junction.
(Bujatti-Narbeshuber,1994,1995,1997a,b,2002,2008,2022,2023,2024a,b,c,2025).
How to cite: Bujatti-Narbeshuber, Dr. M.: Pleistocene/Holocene (P/H) boundary oceanic Koefels-comet Impact Series Scenario (KISS) of 12.850 yrs BP Global-warming Threshold Triad (GTT). Part VI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2154, https://doi.org/10.5194/egusphere-egu26-2154, 2026.
During the curing process of emulsified asphalt cold recycled mixtures (ECRM), mechanical strength gradually develops as internal moisture evaporates. However, the relationship between mechanical property evolution and internal moisture content during the curing of ECRM has not been sufficiently investigated. Moreover, accurate and effective non-destructive methods for monitoring internal moisture loss are still lacking. In this study, low-field nuclear magnetic resonance (LF-NMR) and electrochemical impedance spectroscopy (EIS) were employed as non-destructive techniques to characterize the internal moisture behavior of ECRM with different RAP contents and to analyze its correlation with mechanical performance. LF-NMR testing enables direct characterization of the content, spatial distribution, and migration behavior of internal moisture within a specimen. EIS measures the impedance spectra of materials containing conductive phases. Both techniques offer rapid, non-destructive, and continuous measurement capabilities, allowing visualization of moisture distribution within the material.
The study first evaluated the time-dependent evolution (up to 28 days of curing) of key mechanical properties—including abrasion resistance, Marshall stability, indirect tensile strength (ITS), and splitting tensile modulus (STM)—of ECRM with different RAP contents(0%, 30%, 50%, and 70%). Subsequently, LF-NMR was employed to investigate the content, spatial distribution, and time-dependent migration behavior of free moisture within ECRM containing different RAP contents over a 28-day curing period. The study fabricated working electrodes using epoxy resin and steel rods. The electrodes were buried in the center of ECRM Marshall specimens to measure the electrochemical impedance values of the samples. Given the presence and evolution of internal moisture in ECRM, EIS was then used to monitor the electrical resistance of ECRM with different RAP contents over the same curing period, enabling a quantitative analysis of free moisture evolution. Finally, correlations between the mechanical properties of ECRM and its internal moisture characteristics were established. LF-NMR results indicate that, regardless of RAP content, the internal free moisture in ECRM exhibits a similar distribution pattern: approximately 10% in mesopores (<0.01μm), about 10–30% in intermediate pores (between 0.01μm and 0.1μm), and roughly 60–70% in macropores (>0.1μm). As curing time increases, the internal free moisture in ECRM with different RAP contents consistently migrates from mesopores to intermediate and macropores. This migration behavior results from the combined effects of pore structure characteristics, moisture transport mechanisms, and physicochemical interactions. EIS results showed that impedance increased with curing time, and mechanical performance exhibited a positive correlation with moisture loss. The results demonstrate that LF-NMR and EIS measurements are effective methods for investigating internal moisture characteristics and the evolution of mechanical properties in ECRM. The results reveal the distribution characteristics and migration behavior of internal free moisture in ECRM. These features exhibit a universal pattern and are independent of RAP content.
The findings provide technical guidance for accurately determining the curing time and mechanical strength development of cold-mixed asphalt mixtures. The proposed methods offer significant advantages in non-destructive testing and in situ monitoring. The research conclusions provide a solid foundation for studying the performance of cold-mixed asphalt materials and offer effective solutions for non-destructive testing.
How to cite: Du, H., Zhu, J., Ma, T., Li, R., and Wang, S.: Evolution of Mechanical Properties and Internal Moisture Behavior of Emulsified Asphalt Cold Recycled Mixtures Based on LF-NMR and EIS Non-Destructive Testing Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3639, https://doi.org/10.5194/egusphere-egu26-3639, 2026.
Inverse electromagnetic source problems are inherently ill-posed, as small perturbations in the measured or prescribed fields can lead to large variations in the reconstructed current distributions, necessitating appropriate regularization to suppress non-physical and superdirective solutions. In this work, an inverse Method of Moments (MoM) formulation [1] based on Rao–Wilton–Glisson (RWG) basis functions [2] are developed for near-field antenna pattern synthesis. The forward operator is constructed by mapping RWG surface current coefficients to the near electric field through a dipole-based radiation approximation, yielding a linear but severely ill-conditioned inverse problem.
The resulting inverse formulation is solved using Tikhonov regularization [3], which stabilizes the solution by balancing field fidelity against current smoothness. The regularization parameter is selected using the classical L-curve criterion [4], which is shown to provide a stable and physically meaningful trade-off between the residual norm and the solution norm for the proposed inverse MoM framework. Numerical results demonstrate accurate synthesis of a near-field sector beam spanning approximately ±60° in angle, with the synthesized IEyI distribution closely matching the prescribed field profile. The reconstructed RWG surface currents remain spatially smooth and bounded in magnitude, indicating effective suppression of non-physical and superdirective solutions.
The proposed approach offers a robust and computationally efficient framework for inverse near-field antenna synthesis using surface integral formulations, and provides a validated foundation for future extensions to electrically large structures, inverse antenna–metamaterial design, and more complex inverse radiation control problems.
Fig.1: L curve for obtaining an appropriate 𝜆
Fig.2: Synthesized surface current for sector pattern for 𝜃=60° at 𝜌=0.01𝜆
Fig.3: Comparison plot of desired and synthesized sector pattern for 𝜃=60° at 𝜌=0.01𝜆
References
[1] S. H. Raad, J. S. Meiguni, and R. Mittra, “Inverse MoM Approach to Near-Field Prediction and RFI Estimation in Electronic Devices With Multiple Radiating Elements,” IEEE Access, vol. 11, pp. 21313–21325, 2023.
[2] S. Rao, D. Wilton, and A. Glisson, “Electromagnetic Scattering by Surfaces of Arbitrary Shape,” IEEE Transactions on Antennas and Propagation, vol. 30, no. 3, pp. 409–418, 1982.
[3] D.-H. Han, X.-C. Wei, D. Wang, W.-T. Liang, T.-H. Song, and R. X. K. Gao, “A Phase less Source Reconstruction Method Based on Hybrid Dynamic Differential Evolution With Least Square and Regularization,” IEEE Transactions on Electromagnetic Compatibility, vol. 66, no. 2, pp. 566–573, 2024.
[4] P. C. Hansen, “The L-curve and its use in the numerical treatment of inverse problems,” SIAM Review, vol. 34, no. 4, pp. 561–580, 1992.
How to cite: Bhattacharya, A. and Dey, A.: Regularized Inverse Near-Field Synthesis of Antenna Currents Using RWG Basis Functions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22638, https://doi.org/10.5194/egusphere-egu26-22638, 2026.
Seasonal variations in environmental conditions strongly influence evapotranspiration (ET) and its components, evaporation (E) and transpiration (T), thereby directly affecting agricultural water use. Understanding how these environmental factors regulate ET dynamics is essential for improving water management in semi-arid agricultural regions of India. The study is carried out in Sangareddy district of Telangana, India during the Kharif and Rabi seasons (2024). ET was estimated for both Kharif and Rabi seasons using satellite-based energy balance modelling and divided into its E and T components. To comprehend their impact on surface–atmosphere interactions and crop water consumption dynamics, the seasonal environmental impact on ET is analysed in connection to shifting climatic conditions. Temporal variations of major variables including air temperature (Ta), vapour pressure deficit (VPD), solar radiation (Rs), and precipitation (P) are investigated in this study. Cumulative precipitation distribution during establishment, flowering, growth, and maturity stages of the crop is estimated and compared with average crop water requirement. Furthermore, highlighting the need for irrigation, particularly during the establishment and flowering stages, avoiding crop water stress was observed. During the crop cycle in both seasons, mean leaf area index (LAI) and soil moisture content were also evaluated for the studied region. The analysis shows a significant seasonal contrasts in ET magnitude and its partitioning, with transpiration dominating during peak crop growth under favourable moisture conditions, while evaporation contributed more during early growth stages and dry spells. The results demonstrate that, higher air temperature, vapour pressure deficit, and solar radiation during the Rabi season enhanced atmospheric demand, leading to increased irrigation requirements, particularly during establishment and flowering stages. Thus, these findings emphasises the utility of satellite-based ET partitioning for identifying water-stress-prone growth stages and optimizing irrigation strategies in semi-arid agricultural regions.
Keywords: Evapotranspiration; ET partitioning; environmental variability; satellite imagery'; semi-arid agriculture
How to cite: Moharana, S. and Nithin, E.: Environmental Controls on Evapotranspiration Partitioning in a Semi-Arid Agricultural Region of Telangana, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16546, https://doi.org/10.5194/egusphere-egu26-16546, 2026.
Community engagement is a fundamental pillar of sustainable urban forestry, essential for expanding canopy cover and promoting a societal "stewardship for trees". While traditional outreach aligns urban development with local priorities, it often faces challenges in continuity and representation, frequently resulting in "engagement fatigue" [1]. There is currently a major missed opportunity to use digital tools to connect expert tree-care decisions with public input. Although smart-city technologies offer new ways to communicate, complex tech can accidentally reinforce disparities in egagement if it is not managed carefully [2].
To address this, the London Tree Officers Association (LTOA) established a Working Party on “Promoting Community Engagement for Trees Through Technology-Informed Practices.” This initiative aims to develop strategic pathways toward new guidelines that empower local stakeholders in tree management, improving socio-economic resilience and environmental stewardship. Central to this mission is the "Technological Level of Preparedness" (TLOP) model, which categorises community groups by technology familiarity to ensure equitable and accessible engagement.
The initiative is driven by ongoing, data-informed collaboration among a diverse range of urban forestry stakeholders. Regular meetings enable the Working Party to integrate expert knowledge with local insights to better understand the environmental and social priorities, such as urban cooling and biodiversity, that guide tree management. Recent deliberations also emphasis that content quality is key to maintaining interest, shifting focus from simple planting to long-term stewardship.
To support the formulation of these guidelines, the Working Party is currently shaping potential case studies to test technologies tailored to different identified TLOP levels. Proposed initiatives include the use of gamification via mobile applications to engage younger demographics; for example, trivia games and interactive digital badges could be implemented to incentivise physical interaction with urban nature [4]. Additionally, the application of immersive technologies, such as Augmented Reality (AR) and Virtual Reality (VR), is being explored to help visualise urban regeneration, making abstract environmental data tangible [5]. The initiative also explores enhancing visualisations of canopy health through geospatial and remote sensing technologies.
By exploring how to integrate traditional methods, like guided tree walks, with these new digital tools, the initiative seeks to build a strong foundation. This framework aims to provide the groundwork that could eventually lead to formal guidance for practitioners.
Keywords: Community Engagement; Urban Forestry; Digital Inclusion; Immersive Technology; Geospatial Data.
Acknowledgements: This work is supported by the London Tree Officers Association (LTOA) Working Party on Community Engagement.
References
[1] Nitoslawski, S. & Konijnendijk, C. (2022). The Emergence of Smart Urban Forestry: Challenges and Opportunities in the Digital Age. Arboric. & Urban For., 48(2).
[2] Russo, A. Towards Nature-Positive Smart Cities: Bridging the Gap Between Technology and Ecology. (2025). Smart Cities, 8(1):26.
[3] Srinurak, N. et al. (2024). Smart Urban Forest Initiative: Nature-Based Solution and People-Centered Approach for Tree Management in Chiang Mai, Thailand. Sustainability, 16(24), 11078.
[4] Nand, K., Baghaei, N., Casey, J. et al. (2019). Engaging children with educational content via Gamification. Smart Learn. Environ. 6, 6.
[5] Zürcher, R. et al. (2023). Advancing Forest Monitoring and Assessment Through Immersive Virtual Reality. AGILE: GIScience, 4,1-12.
How to cite: Tosti, F., Porter, B., Mortimer, D., Ardakanian, A., Darko, D., Doe Atsakpo, E., Kumari, S., Nesiah, S., Ressin, M., Saadati, P., and Tessema, T.: Promoting Community Engagement for Trees Through Technology-Informed Practices: From Framework to Guidance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18556, https://doi.org/10.5194/egusphere-egu26-18556, 2026.
In the context of the ongoing climate crisis, the Urban Heat Island (UHI) phenomenon represents one of the most critical challenges for the sustainability of the built environment. Critical transport infrastructures, such as highways, railways, and airports, play a pivotal role in this process. Due to their extension and to the thermophysical properties of the construction materials employed (e.g., asphalt, concrete), they act as significant thermal collectors. On the other hand, in urban areas transport infrastructures negatively affect the local microclimate, thereby reducing the resilience of urban areas.
In this context, satellite Earth Observation (EO) has emerged as a promising tool for monitoring temperature variations [1]. However, temperature measurement in the context of transport infrastructure remains challenging due to the limitations imposed by the spatial resolution of satellite sensors. The primary issue concerns the limited spatial resolution of currently available thermal satellite sensors (with a native resolution of 100 m, resampled to 30 m), such as the TIRS instrument on Landsat 8/9 [2]. While these sensors provide accurate radiometric data, they lack the geometric detail required to analyze specific transportation assets. To overcome this limitation, this research proposes an innovative methodology based on a multi‑scale thermal downscaling procedure, implemented within the Google Earth Engine platform and applied on a real‑scale parking area scenario.
The adopted methodology relies on the synergistic integration of multi‑scale satellite data, following the approach implemented by [3]. This study exploits the well‑established relationship between Land Surface Temperature (LST) and land‑cover metrics, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built‑up Index (NDBI), and the Normalized Difference Water Index (NDWI). A multiple linear regression model was first defined using spectral indices and LST derived from Landsat 8 data; this model was then applied using the corresponding spectral indices extracted from Sentinel‑2 imagery [4] to predict LST at a spatial resolution of 10 m. Subsequently, a second downscaling step was performed by applying a multi‑regression approach based on RGB bands from Sentinel‑2 and UAV imagery, enabling the estimation of surface temperature at sub‑meter resolution. Through this two‑stage procedure, the resolution of LST maps was significantly enhanced, achieving a resolution commensurate with the scale of transport infrastructure. This approach was applied to a parking area in Rome, demonstrating the potential of a sequential thermal downscaling procedure that progressively refines satellite‑derived temperatures using higher‑resolution Sentinel‑2 data and UAV imagery. The results confirm that thermal analysis based on satellite EO data and downscaling techniques is a promising, effective, and cost‑efficient method for assessing infrastructure resilience.
References
[1] Almeida CR, et al,. Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review. Environments 2021;8(10)
[2] Landsat Official Website. Accessed 01-2025. https://landsat.gsfc.nasa.gov/satellites/landsat-9/landsat-9-instruments/landsat-9-spectral-specifications/
[3] Onačillová, K. et al.. Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment. Remote Sens. 2022, 14, 4076.
[4] European Space Agency - Sentinel-2 User Handbook, (2015)
How to cite: Benedetto, A., Gagliardi, V., Manalo, J. R. D., and Cannone, N.: Thermal Analysis for Resilient Transport Infrastructure: A Downscaling Approach Using Satellite EO Data and UAV, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16398, https://doi.org/10.5194/egusphere-egu26-16398, 2026.
This paper addresses automated, uncertainty-aware estimation of shallow surface soil water content (SWC) from ground-penetrating radar (GPR) at the A-scan signal level, overcoming the reliance of conventional workflows on manually picked interfaces and empirical dielectric–moisture curves. Refined gradient soil boxes of sandy and clayey soils (0–30 % gravimetric SWC in 1 % steps) are constructed, 2 GHz GPR and TDR permittivity data are acquired, an effective time window is defined by consistency between travel-time inferred permittivity and TDR, and three physically interpretable attributes—time delay, envelope amplitude area (AEA) and centroid frequency (CF)—are extracted as candidate predictors. Attribute analysis reveals that AEA and CF behave as global indicators that are highly sensitive to SWC in sandy soil, whereas the local delay feature responds more strongly and monotonically in clayey soil because of its higher specific surface area, stronger bound-water effects and slower saturation. Single-indicator regressions already achieve high coefficients of determination (R² up to 0.98 for delay in sand and not less than 0.80 for the remaining indicators), but also expose soil-dependent bias and instability. To exploit the complementary information content of the three attributes, a three-indicator SWC model is built whose weights are obtained by multiplicatively fusing random forest importance with grey relational degree, thereby balancing direct predictive power with dynamic trend consistency. Model comparison shows that, for sandy soil, the three-indicator formulation reduces mean squared error (MSE) by more than 80 % relative to AEA- or CF-only models and remains comparable to delay-only regression, while for clayey soil it lowers MSE by approximately 27 %, 30 % and 51 % with respect to delay-, CF- and AEA-based models, respectively. Bayesian linear and nonlinear regression, combined with Monte Carlo sampling, is further employed to infer posterior distributions of model parameters and observation noise. The resulting credible intervals demonstrate that both model and data uncertainties remain within controllable ranges across the calibrated three-indicator space, with delay exhibiting particularly high predictive reliability. Building on the near-consistent predictions of the delay-only and three-indicator models, an error-recursive optimisation framework is proposed for fully automated SWC inversion. For each A-scan, an initial SWC is assumed, mapped to a travel time via the delay model, and used to recompute AEA and CF within the corresponding time gate; the discrepancy between the two SWC estimates is iteratively minimised until a strict convergence criterion is satisfied. The framework is implemented in dedicated software and validated on independent gradient-box samples and a 1.6 m field transect, where GPR-derived SWC profiles agree well with TDR yet avoid the low-moisture underestimation and high-moisture overestimation characteristic of TDR plus Topp/Roth mixing models. In terms of practical performance, the automated scheme markedly reduces manual interaction, maintains smooth SWC gradients even under 3 % step changes, and remains robust to mixing-induced heterogeneity in clayey samples. Overall, the study demonstrates a technically rigorous pathway toward highly automated, high-resolution GPR monitoring of shallow SWC with explicit quantification of predictive uncertainty.
How to cite: Fang, Y., Ma, T., Tong, Z., and Wang, S.: Study on automated detection methods of shallow surface soil water content based on GPR signal level, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1537, https://doi.org/10.5194/egusphere-egu26-1537, 2026.
Internal cracking in asphalt pavements develops beneath the surface and can rapidly propagate upward, threatening structural integrity and traffic safety. Crack size, especially top width, bottom width, and depth, is a key parameter for selecting maintenance strategies (e.g., grouting positioning and repaving decisions). Ground-penetrating radar (GPR) enables non-destructive subsurface inspection, yet practical crack size interpretation remains challenging due to (i) limited robustness when transferring signal–size relationships from simulations to heterogeneous field conditions, and (ii) the difficulty of directly characterizing crack size from raw GPR B-scan image features.
This study proposes an internal crack size detection network (ICSD-Net) trained on on-site GPR B-scans with interpreted crack size labels. The method targets the trapezoidal geometry of internal cracks (narrow top, wider bottom) and the fact that size-relevant information is concentrated near the hyperbolic apex of crack reflections, where confounding layer reflections often exist and conventional anchor-based/anchor-free detectors struggle with positive-sample matching.
ICSD-Net integrates three key designs. First, a deformable Cross Stage Partial (CSP) backbone improves geometric adaptability for irregular hyperbolic reflections. Second, a Directional Fusion Attention Module (DFAM) constructs direction-aware channel attention using 1D pooling along height/width and generates spatial interaction weights via directional feature broadcasting and multiplicative fusion, enhancing modeling of long-range dependencies across both sides of a hyperbola while suppressing background clutter. Third, an expert-inspired Bipartite Matching (BM) head adopts a DETR-like global set prediction strategy: the network outputs a fixed number of trapezoidal size candidates and uses Hungarian matching to select the optimal assignment between predictions and ground truth, emulating expert global reasoning on an entire B-scan.
A field dataset was built using a 3D GPR array system (24 channels, 800 MHz) from a highway rehabilitation project; signals were minimally processed (direct-wave removal and normalization). Crack size labels were derived by combining forward-model-informed relationships between reflection amplitude and crack top/bottom widths (high correlation reported) with travel-time-based depth estimation, then annotated as four-corner trapezoids on B-scans. The dataset contains 1968 labeled B-scans (small/medium/large targets) split into train/validation/test at 7:2:1.
Experiments show ICSD-Net outperforms multiple state-of-the-art baselines (including YOLO pose variants and DETR adaptations), achieving the highest mAP and mIoU with approximately 8-12% mAP improvement over the strongest baseline, while maintaining real-time feasibility. Ablation studies indicate that DFAM and the BM head contribute most to accuracy gains, improving attention focus toward the hyperbolic apex and reducing misdetections caused by layer reflections. Stability tests demonstrate consistent performance across antenna frequencies and pavement structures, supporting practical deployment. Field validation using coring measurements indicates predicted crack sizes generally meet engineering requirements, with remaining difficulty in accurately estimating bottom width for water-saturated and small cracks due to strong dielectric-contrast-induced multiple reflections and deeper-layer noise.
How to cite: Zhang, Y., Tong, Z., and Zhang, W.: Direction-Aware and Expert-Inspired Learning for Internal Crack Size Detection Using On-Site GPR Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4383, https://doi.org/10.5194/egusphere-egu26-4383, 2026.
Ground Penetrating Radar (GPR) is a widely used geophysical tool for subsurface investigation, including applications in civil engineering, environmental studies, and archaeological explorations. However, GPR signals are often contaminated by various types of noise. These noise factors can significantly degrade the quality of the GPR signal. Existing denoising techniques often struggle to remove complex, non-Gaussian noise or site-specific interference effectively. To address this issue, this study proposes a novel denoising model, the Swin-Conv Block with Attention Denoising Autoencoder (SCB-ADAE), which integrates convolutional and self-attention mechanisms to enhance GPR signal denoising performance. The SCB-ADAE model consists of two key components: the Swin-Conv Block (SCB) and the Attention Denoising Autoencoder (ADAE). The SCB captures high-level features of the raw GPR signal, preserving important details while extracting local and global features. The ADAE module, enhanced with self-attention, focuses on the most relevant components of the signal, suppressing noise and preserving the core features that are essential for accurate interpretation. The process begins by passing the raw GPR signal through the SCB for feature extraction. Next, the ADAE module denoises the extracted features by utilizing self-attention mechanisms. Finally, the denoised signal is passed through a second SCB module for refinement and dimension matching with the original input signal. The model was tested on radar signals contaminated by Gaussian noise at varying levels (5 dB, 7.5 dB, and 10 dB), inhomogeneous-material noise, and real-world GPR signals, with performance evaluated using key metrics such as Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The SCB-ADAE model consistently outperformed existing state-of-the-art models like U-Net and Denoising Autoencoders. For example, at a noise level of 5 dB, SCB-ADAE achieved an SNR of 31.33 dB, PSNR of 38.59 dB, and SSIM of 0.9817, significantly surpassing SCUNet, which achieved lower scores. As the noise level increased, SCB-ADAE maintained superior performance, demonstrating its ability to handle higher levels of noise effectively. In tests involving radar signals with inhomogeneous-material noise, SCB-ADAE demonstrated a 146.74% improvement in SNR and a 16.65% improvement in PSNR compared to SCUNet, highlighting its capacity to address complex, site-specific noise types. In conclusion, the SCB-ADAE model is an effective solution for denoising GPR signals in noisy environments. Future work should focus on expanding training datasets to include more diverse noise types and exploring transfer learning techniques to improve model generalization across different geological environments.
How to cite: liu, J., tong, Z., and zhang, Y.: SCB-ADAE: An Attention-based Deep Autoencoder for Ground Penetrating Radar Signal Denoising, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5246, https://doi.org/10.5194/egusphere-egu26-5246, 2026.
The condition of airfield pavements plays a central role in ensuring the safety and efficiency of airport operations. Compared with highway pavements, those on runways and taxiways are exposed to far heavier and more repetitive dynamic loading from aircraft. Over time, these loads can trigger complex deterioration processes that are not easily identified through surface inspection. Among these, mud pumping is particularly damaging, as it accelerates the structural deterioration of rigid concrete slabs and reduces their service life.
Mud pumping develops when water accumulates at the interface between a concrete slab and its sub-base or subgrade. Under repeated high-magnitude loads, a slurry of water and fine soil particles is expelled through joints and cracks. This movement of material results in subsurface voids, uneven support, increased slab deflection, and, eventually, cracking or differential settlement (sinking) of concrete bays. Previous studies have highlighted that knowing the location and extent of these voids is critical for effective slab stabilisation [1]. At Heathrow Airport, this mechanism has led to several cases of significant cracking and premature pavement distress, prompting a detailed investigation into its causes and distribution.
This study presents a collaborative research framework between academia and Heathrow Airport’s asset management team to assess the capability of Ground Penetrating Radar (GPR) as a primary non-destructive method for detecting early-stage pavement decay. Although Heathrow has identified specific areas of concern, such as the Charlie taxiway, the failure mechanisms often remain hidden until surface damage becomes advanced. By utilising GPR, this study aims to characterise the dielectric contrasts associated with moisture accumulation and subsurface voids that typically precede active mud pumping. The effectiveness of GPR for mapping these internal condition variations has been well-documented, particularly in the characterisation of pavement layer interfaces and moisture content [2].
The methodology focuses on high-resolution subsurface imaging to map the internal condition of concrete bays showing unexpected deterioration patterns. The flexibility of GPR enables the use of different frequencies to balance penetration depth with the resolution required to identify features such as thin delamination layers and incipient voids [3].
The long-term goal is to support a shift from reactive maintenance to a proactive, data-driven management strategy. By identifying the geophysical indicators of mud pumping and structural voids, the study aims to provide a diagnostic approach that can help forecast future failure areas, enhance maintenance planning, and extend the operational lifespan of critical airfield infrastructure.
Keywords: Ground Penetrating Radar (GPR); Airfield Pavement Management; Mud Pumping; Non-Destructive Testing (NDT)
References
[1] Maser, K. R. (2013). Use of GPR for Subsurface Pavement Investigations of 23 Airports in South Carolina, Proceedings Ninth International Conference on BCRRA, Vol 1.
[2] Al-Qadi, I. L., & Lahouar, S. (2005). Measuring Layer Thicknesses with GPR – Theory to Practice, Vol. 19, 10, 763-772.
[3] Benedetto, A., Tosti, F., Bianchini, L., & D’Amico, F. (2018). An Overview of Ground-penetrating Radar Signal Processing Techniques for Road Inspections, Vol. 32, 201-209.
How to cite: Parnow, S., Bolton, M., Fairley, R., Karastamati, A., Smith, R., Fernandez, J., and Tosti, F.: Investigating Subsurface Failure Mechanisms and Mud Pumping in Rigid Airfield Pavements Using Ground Penetrating Radar: A Collaborative Study at Heathrow Airport, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15445, https://doi.org/10.5194/egusphere-egu26-15445, 2026.
Ramsar sites are Wetlands of International Importance under the 1971 Convention. They function as premier nature-based solutions (NbS) by safeguarding ecosystems and delivering multifaceted services essential for sustainable development. The key services include flood regulation, water treatment, carbon sequestration, shoreline prevention, biodiversity support, providing space for recreational activities, generating local employment opportunities, and directly aligning with the UN Sustainable Development Goals (SDGs) [1].
The study aims to assess and analyse the trends of wetland dynamics, which are combinedly influenced by increased urban pressure, airport expansion, associated infrastructure, and climate variability. These overlapping stressors create a complex socio-ecological system that requires integrated monitoring approaches. To address this, the research applies Earth Observation (EO) data for the South West London Waterbodies Ramsar site [2]. This is a part of the Thames River basin and supports a significant waterfowl population and functions as a wetland ecosystem adjacent to a major aviation infrastructure.
The study explores Sentinel collections, Landsat series, land use land cover (LULC) and ancillary data to identify patterns and effectively capture and monitor wetland dynamics [3] [4], water quality [4], and changes in extent and overall condition [5].
The study emphasises the importance of EO for monitoring wetlands within complex urban infrastructure landscapes. This study will demonstrate how EO-derived insights can support stakeholders, policymakers, and decision-makers in designing and developing evidence-based climate adaptation and mitigation strategies, enabling targeted NbS interventions to strengthen system-wide resilience.
Keywords: NbS, Ramsar site, Wetlands, Climate Resilience, Urban Infrastructure, Earth Observation
Acknowledgments: This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London. Sincere thanks to the following for their support: The Lord Faringdon Charitable Trust, The Schroder Foundation, The Cazenove Charitable Trust, The Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, John Swire Charitable Trust, The Samuel Storey Family Charitable Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.
References
[1] Ramsar, "Scaling up wetland conservation, wise use and restoration to achieve
the Sustainable Development Goals," pp. 1–13, 2018. Available: https://www.ramsar.org/sites/default/files/documents/library/wetlands_sdgs_e.pdf.
[2] Ramsar. Ramsar Sites Information Service. Available: https://rsis.ramsar.org/ris/1038?__goaway_challenge=meta-refresh&__goaway_id=67266e1927adeb4042d00a7e1a15f9c3..
[3] Z. Wang et al, "Monitoring the Wetland of the Yellow River Delta by Combining GF-3 Polarimetric Synthetic Aperture Radar and Sentinel-2A Multispectral Data," Front. Ecol. Evol., vol. 10, 2022. Available: https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.806978/full. DOI: 10.3389/fevo.2022.806978.
[4] M. Singh and R. Sinha, "Hydrogeomorphic indicators of wetland health inferred from multi-temporal remote sensing data for a new Ramsar site (Kaabar Tal), India," Ecological Indicators, vol. 127, 2021. Available: https://www.sciencedirect.com/science/article/pii/S1470160X21004040. DOI: 10.1016/j.ecolind.2021.107739.
[5] W. Chaoyong et al, "SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning," Sci Rep, vol. 14, (1), pp. 14, 2024. Available: https://www.nature.com/articles/s41598-024-76730-1. DOI: 10.1038/s41598-024-76730-1.
How to cite: Kumari, S., Tessema, T., Ardakanian, A., Daou, D., and Tosti, F.: Earth Observations into Urban Resilience: Exploring the Nexus between Nature-based Solutions, Infrastructure and Climate Interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16577, https://doi.org/10.5194/egusphere-egu26-16577, 2026.
Conventional visual inspection of civil infrastructure and cultural heritage is limited by subjectivity, restricted accessibility, and safety risks. Developing an integrated methodology to accurately reconstruct and survey existing structures and infrastructures, with millimetric geometric accuracy and reliable information on material conservation conditions, is therefore essential to overcoming these constraints. With the progressive deterioration of historic structures and major cultural-heritage assets, including bridges, monuments, historic buildings, and aging roadways, the need for highly accurate measurement and documentation has become increasingly critical. To this end, the use of multi-source Non-Destructive Testing (NDT) techniques for the acquisition of fast and accurate geometric and radiometric information is a necessity. This research proposes an integrated geomatic workflow designed to digitize complex built environments through multi-sensor data integration, enabling advanced analysis within immersive virtual environments. The methodological approach relies on a robust topographic reference frame established via Global Navigation Satellite Systems (GNSS) and high-precision Total Stations, ensuring the global georeferencing required for engineering reliability. To capture the full complexity of the assets, the study employs a synergistic acquisition strategy. Terrestrial Laser Scanning (TLS) is a process of generating a high-resolution point cloud representing the geometry of the ground and other features that can be reached from ground level, while simultaneously employing Unmanned Aerial Vehicles (UAVs) to address any occluded areas caused by the ground perspective and enabling the inspection of buildings' upper levels and structural components. The UAV equipment consists of an optical-camera payload that enables millimetric-resolution acquisition for high-definition photogrammetric modelling.
All these multi-source surveying tools were employed to reconstruct a digital model of a real architectural complex, the Bernini Hall, now incorporated into the Palazzo Ripetta ensemble in Rome, Italy. Within this context, one of the most significant historic and artistic spaces within the building preserves a refined architectural and cultural heritage of substantial value. The multi-source datasets were subsequently post-processed for georeferencing and for the registration of the different acquisitions, resolving geometric discrepancies and producing a single, multi-layered 3D point cloud. This digital model forms the basis for structural analysis, also enabling the assessment deformation reconstruction, with millimetric accuracy. The novelty of this framework lies in its shift from traditional static digital-model analysis to immersive visualization. The digital model, derived from the integration of UAV imagery through photogrammetric reconstruction and LiDAR point-cloud data by TLS, is imported into a Virtual Reality (VR) environment using Unity®, a dedicated software optimized for high-fidelity rendering, enabling immersive exploration and navigation within the model, with millimetric accuracy consistent with the NDT-based survey. The use of Head-Mounted Displays (HMDs) enables users to experience a fully immersive digital representation, navigating the space as if physically present. The reconstructed digital model improves the accuracy of inspections in critical or hard-to-access areas, opens new ways for structural-health-monitoring efficiency and broadens opportunities for the valorization and remote accessibility of the built environment.
How to cite: Manalo, J. R. D., Gagliardi, V., Vennarucci, A., Bianchini Ciampoli, L., Meriggi, P., Fares, S., de Felice, G., and Benedetto, A.: A Multi-Source Non-Destructive Testing Survey for Digital Modelling and Reconstruction: The Case Study of Palazzo Ripetta in Rome, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12107, https://doi.org/10.5194/egusphere-egu26-12107, 2026.
Urban climate adaptation strategies increasingly rely on trees as multifunctional assets for mitigating heat stress and improving urban liveability [1]. As tree planting and management accelerate in response to climate pressures, interactions between urban trees and subsurface infrastructure become increasingly relevant for planners, asset managers, and local authorities operating at neighbourhood to asset-management scales. Ground Penetrating Radar (GPR) is a well-established non-destructive technique for imaging shallow subsurface conditions and detecting tree root systems in urban environments [2], yet its contribution to adaptation-oriented decision contexts remains limited.
This limitation is not necessarily related to detection capability, as in some instances this depends on how GPR outputs are transformed and communicated beyond individual case studies. In climate adaptation settings, decisions concerning tree retention, monitoring, and intervention typically rely on surface-based indicators and qualitative risk categories [3], limiting the use of subsurface information for cross-site comparison and prioritisation within decision-support processes.
To address this gap, GPR data from multiple urban sites within the same local area were analysed to capture subsurface conditions around urban trees and their interaction with pavements and engineered layers. Rather than focusing on site-specific detection outcomes, the methodology introduced an intermediate analytical step in which GPR profiles were structured into repeatable spatial units and used to derive relative, non-prescriptive descriptors of subsurface conditions. These descriptors were explored through alternative indicator formulations, allowing different representations of subsurface variability and uncertainty to be examined while remaining grounded in the same geophysical observations. Within this framework, the resulting indicators were then synthesised at tree level to support decision-relevant interpretation, enabling subsurface conditions to be characterised in comparative terms and translated into high-level management tendencies, such as prioritisation for monitoring, further investigation, or intervention.
The study demonstrates that reframing GPR outputs within a stakeholder-oriented decision-support framework, rather than site-specific detection outcomes, enhances their relevance for climate adaptation and resilience planning. The proposed approach provides a transferable pathway for integrating geophysical evidence into evidence-based urban policy and asset management processes, by explicitly aligning geophysical interpretation with the scale and needs of real-world decision-making.
Keywords: Ground Penetrating Radar (GPR); Urban Trees; Decision-support Framework; Urban Resilience; Climate Adaptation
References
[1] D.E. Bowler, L. Buyung-Ali, T.M. Knight and A.S. Pullin, "Urban greening to cool towns and cities: A systematic review of the empirical evidence," Landscape and Urban Planning, vol. 97, pp. 147–155, Sep 15. 2010.
[2] L. Lantini, F. Tosti, I. Giannakis, L. Zou, A. Benedetto and A.M. Alani, "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing, vol. 12, pp. 3417, Oct 1. 2020.
[3] S. Pauleit, T. Zölch, R. Hansen, T.B. Randrup and C. Konijnendijk van den Bosch, "Nature-Based Solutions and Climate Change – Four Shades of Green," in Nature-Based Solutions to Climate Change Adaptation in Urban Areas: Linkages between Science, Policy and Practice, N. Kabish, H. Korn, J. Stadler and A. Bonn, Springer, 2017, pp. 29–50.
How to cite: Lantini, L., Ardakanian, A., and Tosti, F.: From Detection to Interpretation: A Decision-Support Framework for GPR-Based Evidence in Urban Climate Adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22982, https://doi.org/10.5194/egusphere-egu26-22982, 2026.
