This session aims to bring together researchers, practitioners, and policymakers to address gaps in observation data. New observational strategies and sensing techniques will be discussed, which provide a more accurate and inclusive reflection of these underrepresented areas to bridge these gaps.
We welcome contributions on:
• Advanced Remote Sensing Technology: optical, SAR, LiDAR, thermal and hyperspectral methods, including use of UAVs and drones, for a synoptic observation of the territory;
• Remote Sensing Applications for Natural Hazards: monitoring strategies and organisational solutions for natural hazards (e.g. earthquakes, volcanic activities or landslides, droughts, etc);
• Integrated Sensing Networks: low-cost in-situ sensors (e.g., GNSS, meteorological,Tiltmeter, dynamic gravity) as complements to satellite observations;
• Data Science and AI: data fusion and machine learning approaches to enhance monitoring reliability, resolution and coverage;
• Immersive Technologies for Monitoring and Engagement: application of AR/VR/MR to visualise geospatial data, simulate hazard scenarios, and support participatory planning in underserved urban areas.
• Citizen Science: participatory sensing initiatives that improve observational data in underserved communities;
• Sociotechnical Applications: sustainable observational strategies for monitoring and protecting critical infrastructures (water, energy, transport), and promoting sustainable resource use and social inclusion;
• Geoscience for Social Good: applications for urban climate resilience, public health, pollution exposure, urban heat islands, green infrastructure, and disaster risk reduction, with a focus on big cities and remote areas.
Our goal is to facilitate integrating diverse sensing techniques into operational frameworks that promote resilience, inclusivity, and sustainable development for marginalised populations.
Up-to-date spatial information on deprived urban areas (DUAs) is essential for evidence-based urban policy and for monitoring Sustainable Development Goal (SDG) indicator 11.1.1 on slums and informal settlements. Yet, many cities in low- and middle-income countries (LMICs) lack reliable, spatial data on the location, extent, and dynamics of settlements. The IDEAtlas project addresses this gap by combining Earth Observation (EO), artificial intelligence (AI) (here referred to as GeoAI), and a user-centred design framework to deliver scalable, transparent, and policy-relevant DUA maps. This is achieved through a data-centric AI approach utilizing multi-modal EO inputs, primarily Sentinel-2 multispectral imagery, supplemented by ancillary geospatial layers such as building density, topography, and proximity to infrastructure. These datasets are fused within a lightweight Multi-Branch Convolutional Neural Network (MB-CNN) architecture (~33,000 parameters) designed for efficient, city-scale processing of Sentinel data. The model produces two main outputs at 10 m resolution, which are resampled to 100 m to protect vulnerable groups: (1) binary maps of deprived urban area extent and (2) a continuous deprivation severity index building on the IDEAMAPS Domain of Deprivation Framework. Multi-temporal processing provides annual DUA maps (2019–2023), capturing settlement expansion, densification, eviction, and upgrading dynamics. Thus, IDEAtlas adopts a user- and data-centric GeoAI approach. Through Living Labs in eight pilot cities (Nairobi, Lagos, Mumbai, Jakarta, Salvador, Medellín, Mexico City, and Buenos Aires), and an ongoing expansion to a larger number of Latin American cities, local governments, national statistical offices, NGOs, and community groups co-design data needs, validated outputs, and contributed to the creation of reference data. The interactive web-based IDEAtls Portal allows users to inspect model predictions, digitise settlement boundaries, correct misclassifications, and provide contextual feedback. These user-generated annotations are reintegrated into the model, improving performance and trust. In several cities, stakeholder-driven refinement increased F1-scores by up to 13%, demonstrating the value of participatory data curation in complex urban environments.
The IDEAtlas outputs provide several policy-relevant indicators, such as total DUA area, population living in deprived areas, and temporal change metrics directly linked to SDG 11.1.1. By integrating scalable GeoAI methods with user-in-the-loop validation and open-source infrastructure, IDEAtlas demonstrates how user- and data-centric GeoAI can bridge urban data gaps. The approach strengthens local capacity, enhances transparency, supports inclusive and evidence-based urban policy, and outlines a pathway towards a transferable framework for global SDG 11.1.1. monitoring.
How to cite: Kuffer, M., Tareke, B., Persello, C., Maretto, R. V., Wang, J., Abascal, A., and Vazquez Brust, H. A.: User- and Data-Centric GeoAI for Scalable Mapping of Deprived Urban Areas: The IDEAtlas Approach for SDG 11.1.1 Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16574, https://doi.org/10.5194/egusphere-egu26-16574, 2026.
Surface water in semi-arid and arid regions has been adversely affected by climate change, compounded by their inherent environmental conditions. One such area is East Africa, which consists of small reservoirs, pans, and non-perennial rivers that provide critical water resources for people, livestock, and ecosystems [1]. However, these water resources are poorly monitored and highly variable in space and time. Recent global water products overlook small and ephemeral water bodies due to the spatial resolution of the satellite data used and the target scale [2]. This monitoring gap is particularly consequential because pastoral adaptation in the region is tightly shaped by the timing and distribution of water; socio-ecological studies emphasise that tracking environmental variability is essential for understanding how mobility patterns respond to climate change [3]. Remote sensing techniques offer a way to address this gap by identifying changes in water systems, including the emergence and disappearance of temporary water bodies. The objective of this study is to develop a framework that facilitates understanding of the spatial and temporal patterns of these water bodies, providing a foundation for subsequent analyses of pastoral nomadic movements and resource-use dynamics. Here, we present a multi-year monitoring framework that integrates Sentinel-1 SAR and Sentinel-2 optical imagery to map surface-water dynamics. We develop Sentinel-1/2 stacks for a monthly compositing window, derive spectral water indices, perform spectral analysis, and include topographic variables. We use a basic classifier trained on multi-year reference data and produce a 10m water mask. Further, time-series metrics for water occurrence, duration, the number of wet spells, and transition frequency are produced. The preliminary results indicate a trend in changes to the temporal surface water and the extent of permanent water bodies. The results reveal strong contrasts between permanent lakes and reservoirs, seasonal floodplains, and highly ephemeral channels and pans. A good understanding of the pattern and location of such water bodies contributes to informed support for livelihoods in the region and to sustainable water resource management.
Keywords: Arid And Semi-arid; Water Resources; Remote Sensing; Pastoral Mobility; Sar; Optical Imagery; East Africa, Sentinel-1/2
Acknowledgements: The Authors would like thank the following trusts, charities, organisations and individuals for their generosity in supporting this project: 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] Sigopi M, Shoko C, Dube T. Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions. Geocarto Int 2024;39:2347935.
[2] Miura Y, Shamsudduha M, Suppasri A, Sano D. A Global Multi-Sensor Dataset of Surface Water Indices from Landsat-8 and Sentinel-2 Satellite Measurements. Sci Data 2025;12:1253.
[3] Cho, M.A., Mutanga, O. and Mabhaudhi, T. Adaptation to climate change in pastoral communities: a systematic review through a social-ecological lens. International Journal of Climate Change Strategies and Management, 2025 17(1), pp.246-267.
How to cite: Ardakanian, A., Mengistu, F., Lewi, E., Tosti, F., and Tessema, T.: Multi-year Satellite Observations Reveal Permanent Seasonal and Ephemeral Surface-water Regions in Arid and Semi-arid Areas Within the East African Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16661, https://doi.org/10.5194/egusphere-egu26-16661, 2026.
The new generation of high-resolution nightlight sensors (e.g., SDGSat-1, Jilin-1) with a spatial resolution of 10 m and below can capture the spatial patterns and light colours of Artificial Light at Night (ALAN). Our research leverages these new generations of high spatial and spectral resolution nightlight images and in situ data collection by citizen science and through nightlight mobile applications to accurately assess electricity access and reliability in Sub-Saharan African cities. More than 50% of Sub-Saharan Africa’s population (around 600 million people) has no access to electricity, and a large part (estimated at 80%) does not have access to stable electricity- data coming from the global dataset "Global Electrification Project" (World Bank). Existing global electricity access datasets use low-resolution satellite data (such as VIIRS) and, therefore, remain uncertain and misrepresent that many of these cities have universal access to electricity. In reality, many residents lack formal grid connections and face unreliable electricity supply. We conducted local fieldwork in informal settlements to capture access gaps and relate the results to high-resolution ALAN data. Surveyed areas experiencing unstable access to electric power, leading to frequent outages, such as Nigeria's average of over 32 monthly outages. Results show that field observations in combination with high-resolution night light images can provide a more accurate and nuanced understanding of electricity distribution and reliability to understand the gaps in SDG-7 across sub-Saharan Africa. Our study provides insights into how global monitoring of the multiple dimensions of urban poverty can include access to electricity as an essential indicator. Furthermore, emphasises incorporating nighttime light observations into global urban poverty monitoring to include electricity access as an essential indicator. It also outlines the need for advanced satellite-based sensors to support comprehensive urban poverty mapping, in line with the European Space Agency-funded “NightWatch” project.
How to cite: Abascal, A., Kuffer, M., and Kyba, C.: Capturing the Urban Divide in Nighttime Light Images with the New Generation of High-Resolution Night Light Images and Citizen Science Data. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20229, https://doi.org/10.5194/egusphere-egu26-20229, 2026.
Agro-pastoral communities in East Africa are increasingly affected by recurrent natural hazards, particularly drought, which leads to severe pasture degradation and substantial losses of livestock—their primary livelihood asset. These challenges highlight the urgent need for adaptive livelihood diversification strategies that reduce dependence on climate-sensitive resources. Beekeeping has emerged as a viable and climate-resilient alternative; however, its successful implementation requires reliable information on suitable habitats and forage availability. Advances in remote sensing and geospatial technologies provide powerful tools to support such strategies by integrating multi-source satellite data to monitor vegetation dynamics, landscape structure, and climate variability, thereby enabling informed planning and sustainable livelihood development in agro-pastoral environments. This study presents an integrated geospatial assessment of honey bee habitat suitability, fragmentation dynamics, and climate-smart apiary site selection in Yabelo (Ethiopia) and Taita-Taveta County (Kenya). Using Google Earth Engine, multi-source satellite data, including Planet Scope, Sentinel-1 SAR, Sentinel-2 multispectral imagery, and SRTM Digital Elevation Model were analyzed to map honey bee habitats through advanced machine learning techniques. Four classifiers (Gradient Tree Boosting, Random Forest, Classification and Regression Trees, and Support Vector Machine) were evaluated and combined using an Ensemble Learning Approach, achieving the highest classification accuracy, significantly outperforming individual models.
To understand landscape structure and resource accessibility, habitat fragmentation was assessed using key landscape metrics (Shannon diversity, contagion, and splitting index) across multiple spatial scales (500–3000 m buffers). Results reveal pronounced scale-dependent fragmentation, with Yabelo characterized by high landscape heterogeneity but increasing patch disconnection at larger scales, while Taita-Taveta exhibits more continuous but less diverse habitats. Human-induced land-use changes and edge effects were identified as major drivers of fragmentation, with wetlands and water bodies being particularly vulnerable.
To support adaptive beekeeping under climate change, fuzzy Multi-Criteria Decision-Making methods incorporating current and future climate projections (SSP1-2.6 and SSP5-8.5) were applied for apiary site suitability analysis. Findings indicate a projected decline in highly suitable apiary areas under future climate scenarios, highlighting climate-driven shifts in bee forage availability. Overall, this integrated framework demonstrates how ensemble machine learning, landscape ecology, and climate projections can support evidence-based, climate-resilient planning for sustainable beekeeping in East Africa.
Keywords: Agro-pastoral livelihoods, Remote sensing, Honey bee habitat, Climate change, Ensemble learning, Apiary suitability
How to cite: Walle, F. M.: An Integrated Geospatial Framework for Climate-Resilient Honey Bee Habitat Mapping, Fragmentation Assessment, and Apiary Site Selection., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16057, https://doi.org/10.5194/egusphere-egu26-16057, 2026.
In previous work, we applied a spatially-aware graph neural network model to West Nile virus data collected in Illinois for mosquito surveillance. The purpose of this was to aid mosquito abatement efforts within the state while accounting for environmental variability. Other studies have also taken this data to examine links between West Nile virus levels and spatially-varying demographic factors, but did not identify any evidence of such links. This raises the question of whether the inability to identify such links could be due to confounding via surveillance levels related to the demographic factors. That is to say, such links may be disguised by varying West Nile virus surveillance data quality across Illinois, and this variance in data quality may be related to the demographic factors themselves. In our work, we examine the spatial trends of surveillance frequency in this data within the Chicago area using a functional data analysis model within a Bayesian hierarchical model that is implemented in the Stan probabilistic programming language. We then relate these trends to zipcode-level demographic factors and present the likelihood for the existence of the statistical relationships in question. The use of a functional data analysis method allows for increased flexibility in our choice of model, such that it more closely reflects the reality supported by our sources from various fields in the literature. Furthermore, our use of a Bayesian hierarchical framework allows for greater interpretability of our findings, at the cost of greater required computational resources for model fitting.
How to cite: Tonks, A., Bravo, L., and Smith, R.: Confounding effects via spatially-varying demographic factors upon West Nile virus surveillance in Illinois identified using a Bayesian functional data analysis model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23010, https://doi.org/10.5194/egusphere-egu26-23010, 2026.
Heritage structures are widely recognised as irreplaceable cultural assets that connect communities with their history, and their preservation serves both current and future generations [1, 2]. As definitions of heritage have expanded to include tangible and intangible values and a broad range of stakeholders, conservation and refurbishment approaches aim to safeguard heritage significance by protecting material authenticity, cultural values, structural integrity, functional performance, and use.
In specific heritage spaces such as religious buildings and theatres, the acoustic environment plays a central role in cultural practices, symbolic meaning, and functional performance, making acoustics an integral component of aural heritage. Recent works in archeoacoutics highlight growing interest in a multisensory, sound-focused approach to heritage communication and interpretation. Since auditory perception is observer-centred and inherently spatial, effective communication of acoustic or aural heritage requires spatially representative, immersive media such as auralisation and extended reality (XR), which can reproduce listener-centric sound fields rather than purely visual reconstructions [3, 4].
This study thus investigates a stakeholder-centred framework for the visualisation and communication of acoustic data from heritage architecture, using immersive technologies to support the maintenance and preservation of archeoacoustic information. A three-dimensional digital model of the Roman Theatre at Palmyra, Syria, a UNESCO World Heritage Site, was refined, and room-acoustic simulations were conducted to generate auralisations representing the theatre’s acoustic performance under different listener and source configurations. These simulated acoustic outputs were integrated into an extended reality (XR) environment, enabling interactive exploration of the theatre’s acoustic characteristics through combined audio–visual–spatial representations of sound.
This immersive, auralised, interactive system is designed to support stakeholder-centred evaluation and knowledge exchange, thereby informing its refinement in relation to visualisation, interpretation, and decision-making needs.
Keywords: Auralisation; Extended Reality; Archeoacoustics; Human-in-the-loop
Acknowledgements: This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.
References
[1] T. Penjor, S. Banihashemi, A. Hajirasouli and H. Golzad, "Heritage building information modeling (HBIM) for heritage conservation: Framework of challenges, gaps, and existing limitations of HBIM," Digital Applications in Archaeology and Cultural Heritage, vol. 35, -07-29. 2024.
[2] J. Mu, T. Wang and Z. Zhang, "Research on the Acoustic Environment of Heritage Buildings: A Systematic Review," Buildings, vol. 12, -11-11. 2022.
[3] V. Hohmann, R. Paluch, M. Krueger, M. Meis and G. Grimm, "The Virtual Reality Lab: Realization and Application of Virtual Sound Environments," 2020.
[4] C. Innocente, L. Ulrich, S. Moos and E. Vezzetti, "A framework study on the use of immersive XR technologies in the cultural heritage domain," Journal of Cultural Heritage, vol. 62, pp. 268, -06-15. 2023.
How to cite: Doe Atsakpo, E., Donati, E., Squartini, N., Bianchini Ciampoli, L., Tessema, T., Uzor, S., and Tosti, F.: Immersive Auralisation (IA-XR): Visualising and Communicating Acoustic Data from Cultural Heritage Structures via Extended Reality Technology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16485, https://doi.org/10.5194/egusphere-egu26-16485, 2026.
Rural coastal areas are among the most poorly monitored environments, despite being highly exposed to marine hazards, climate variability, and increasing anthropogenic pressure. Limited accessibility, the absence of permanent infrastructures, and high operational costs often prevent the deployment of conventional in situ monitoring instruments, leading to significant observational gaps in the estimation of sea state parameters and surface current dynamics. Developing sustainable and low-impact observational strategies is therefore crucial to improve environmental monitoring and risk awareness in these marginal coastal regions [1].
Satellite-based remote sensing systems provide valuable large-scale observations of ocean dynamics. However, their low revisit time and reduced performance in nearshore and shallow-water environments limit their effectiveness along rural coastlines. As a result, increasing attention has been devoted to sensing technologies operating at local scales and closer to the sea surface, including radar and video-based monitoring systems. Among radar-based solutions, short range (SR) K-band systems are particularly suited for this purpose. Their compact size, low power emissions, high temporal resolution, and sensitivity to short surface waves make them ideal for monitoring coastal and semi-enclosed environments. Moreover, recent developments in portable K-band radar prototypes enable rapid and non-invasive deployment in remote coastal areas, without the need for permanent installations [2], [3].
With regard to video monitoring, small unmanned aerial systems equipped with lightweight optical cameras have attracted considerable interest as highly flexible and cost-effective tools for rapid data acquisition in hard-to-reach areas, such as rocky coastlines, wetlands, river mouths, and sparsely populated shores [4], [5].
Within this framework, this contribution reviews the results of lightweight portable SRK-band radar for sea monitoring and presents an innovative signal processing strategy for extracting quantitative information on sea state parameters and surface current fields from drone-based optical camera. Both the considered technologies are useful for nearshore zone, where other observational systems are often unreliable or unavailable.
[1] P. Neill and M. R. Hashemi, “In situ and remote methods for resource characterization,” in E-Business Solutions, Fundamentals of Ocean Renewable Energy, S. P. Neill and M. R. Hashemi. New York, NY, USA: Academic, 2018, pp. 157–191.
[2] Ludeno, G.; Catapano, I.; Soldovieri, F.; Gennarelli, G. Retrieval of sea surface currents and directional wave spectra by 24 GHz FMCW MIMO radar. IEEE Trans. Geosci. Remote Sens. 2023, 61, 5100713.
[3] Ludeno, G.; Antuono, M.; Soldovieri, F.; Gennarelli, G. A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar. Remote Sens. 2024, 16, 261
[4] Streser, R. Carrasco, and J. Horstmann, “Video-based estimation of surface currents using a low-cost quadcopter,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 11, pp. 2027–2031, Nov. 2017.
[5] Solodoch, Y. Toledo, V. Grigorieva and Y. Lehahn, "Retrieval of Surface Waves Spectrum From UAV Nadir Video," IEEE Trans. Geosci. Remote Sens., vol. 63, pp. 1-14, 2025, Art no. 4201914, doi: 10.1109/TGRS.2025.3536378.
How to cite: Catapano, I., Gennarelli, G., Esposito, G., Noviello, C., Soldovieri, F., and Ludeno, G.: Monitoring of Sea State and Surface Currents in Rural Coastal Areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11371, https://doi.org/10.5194/egusphere-egu26-11371, 2026.
The structural condition of historic masonry walls plays a crucial role in the conservation and management of cultural heritage assets. Moisture ingress, material degradation, and hidden internal defects often develop beneath the surface, remaining undetected by visual inspection alone. Traditional invasive inspection methods are limited by their local nature and the potential risk of damaging heritage fabric. Consequently, non-destructive testing (NDT) techniques capable of providing reliable subsurface information are essential.
Ground Penetrating Radar (GPR) has become a widely adopted non-destructive and cost-effective technique for investigating masonry structures [1-3], allowing the identification of internal heterogeneities, voids, and moisture-related anomalies. However, the interpretation of conventional GPR sections is frequently affected by signal complexity, non-uniqueness, and ambiguity, particularly when dealing with heterogeneous historic materials and varying wall thicknesses.
This study presents a multi-frequency GPR survey conducted on a historic brick masonry wall located in Walpole Park (Ealing, London, UK), a site of special historic interest. The primary objective was to assess the internal condition of the wall and investigate suspected moisture ingress. Data were acquired using ground-coupled GPR systems operating at 2 GHz and 600 MHz, including dual-polarised configurations (HH and VV), allowing a comparative evaluation of resolution and penetration depth for walls of approximately 25 cm and 55 cm thickness.
To enhance data interpretation, attribute analysis, originally developed in seismic exploration, was applied to the GPR datasets. Attributes such as Centroid Frequency (CF) and Instantaneous Frequency (IF) were extracted from both processed and unprocessed radar data. These attributes provide additional information on material properties and signal attenuation, which are strongly linked to moisture content, material heterogeneity, and structural condition. Three-dimensional visualisation of attribute volumes was employed to better delineate spatial variations within the masonry fabric.
Attribute-based representations significantly improved the clarity and interpretability of subsurface anomalies compared to conventional amplitude-based GPR images.
The findings confirm that GPR attribute analysis enhances the assessment of historic masonry structures and supports more reliable interpretation of moisture-related and structural features. The proposed workflow offers a robust, non-invasive tool for heritage conservation and condition monitoring, with potential for wider application across cultural heritage and built-environment diagnostics.
Keywords: Ground Penetrating Radar (GPR); Cultural Heritage; Masonry Inspection; Moisture Ingress; Signal Attribute Analysis
References
[1] Sambuelli, L., Bohm, G., Capizzi, P., Cardarelli, E., & Socco, L. V. (2011). The use of ground penetrating radar and complementary NDT techniques for the diagnostic of masonry structures. Near Surface Geophysics, 9(5), 433–447.
[2] Bianchini Ciampoli, L., Parnow, S., Tosti, F., and Benedetto, A.: Retrieving signs of buried historical road tracks by GPR data processing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16105, https://doi.org/10.5194/egusphere-egu25-16105, 2025.
[3] Catapano, I, Gennarelli, G., Ludeno, G., and Soldovieri, F., Applying Ground-Penetrating Radar and Microwave Tomography Data Processing in Cultural Heritage: State of the Art and Future Trends. IEEE Signal Processing Magazine, vol. 36, no. 4, pp. 53-61, July 2019, doi: 10.1109/MSP.2019.2895121.
How to cite: Parnow, S., Soldovieri, F., Doe Atsakpo, E., and Tosti, F.: Enhancing the Assessment of Historic Masonry Walls Using Multi-Frequency GPR Attribute Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15312, https://doi.org/10.5194/egusphere-egu26-15312, 2026.
In recent years, the development of monitoring and early warning systems for critical infrastructures has become increasingly relevant, as highlighted by the scientific literature and the significant number of projects focused on this specific topic, as well as their practical applications.
In this frame, the necessity arises of developing holistic approaches/strategies [1, 2], which are based not only on the integration of different sensing technologies, but more importantly on a multidisciplinary approach encompassing disciplines related to sensing, ICT, positioning technologies, and civil engineering methodologies. This contribution will provide a brief survey of the different classes of sensing techniques supporting civil engineering analysis. In addition, the other main aim of this abstract will be the setup of smart and effective observational chains for smart and sustainable monitoring, especially concerning marginal areas. Attention will also be given to the embedded miniaturized sensors, which have the main advantage of ensuring an always updated long-term monitoring and provide a more reliable early-warning system. Finally, it is evident that the concepts specifically considered here for critical infrastructures can also be extended to urban areas (built environment) and cultural heritage.
[1] Soldovieri, F.; Dumoulin, J.; Ponzo, F.C.; Crinière, A.; Bourquin, F.; Cuomo, V. Association of sensing techniques with a designed ICT architecture in the ISTIMES project: application example with the monitoring of the Musmeci bridge. EWSHM 2014, 7th European Workshop on Structural Health Monitoring, Nantes, France, 8 - 11 July 2014.
[2] Cuomo, V.; Soldovieri, F.; Ponzo, F.C.; Ditommaso, R. A holistic approach to long term SHM of transport infrastructures. Emerg. Manag. Soc. (TIEMS), 2018, 33, 67–84.).
How to cite: Soldovieri, F., Cuomo, V., Ponzo, F., Di Tommaso, R., and Lapenna, V.: A smart approach for long-term SHM of critical infrastructures , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20426, https://doi.org/10.5194/egusphere-egu26-20426, 2026.
High-frequency and long-term measurements of soil gas fluxes are essential for quantifying terrestrial carbon cycling and for monitoring fluid migration processes associated with hydrological, tectonic, and volcanic activity. However, the widespread application of automated soil gas flux observations remains limited by the high cost, power consumption, and operational complexity of commercial systems.
We present a self-developed, low-cost automated soil gas flux (ASF) system designed for real-time, long-term field monitoring. The system is based on a closed-chamber circulation concept integrating a low-cost NDIR CO₂ sensor, controlled gas mixing and flushing, humidity regulation using a drying module, and environmental correction for soil temperature and atmospheric pressure. A modular hardware architecture, combined with a microcomputer-based controller, enables flexible configuration, autonomous operation, and wireless data transmission. The system is powered by a solar-assisted lithium battery unit, allowing continuous deployment in remote environments.
Laboratory validation shows that CO₂ concentrations measured by the ASF system exhibit excellent linearity when compared with a high-precision cavity-enhanced gas analyzer (LGR M-GGA-918), with deviations generally within ±5% across a wide concentration range. Field deployments lasting more than three months demonstrate stable system performance, energy autonomy, and the ability to resolve high-temporal-resolution variability in soil CO₂ fluxes. A dedicated data-processing workflow is implemented to identify stable accumulation intervals and convert high-frequency concentration time series into instantaneous and diel-scale fluxes.
The ASF system provides a cost-efficient, scalable, and reproducible solution for continuous soil gas flux monitoring. Its open and modular design makes it suitable for applications ranging from ecosystem carbon exchange and ecohydrological studies to fault-zone degassing and volcano-related gas monitoring, and facilitates integration into multi-parameter geophysical and geochemical observation networks.
How to cite: Fu, C.-C., Chen, K.-H., Ku, C.-S., and Wu, K.-W.: A Self-Developed Low-Cost Automated Soil Gas Flux System for Real-Time Monitoring: Design and Field Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17691, https://doi.org/10.5194/egusphere-egu26-17691, 2026.
Surface reflectance consistency of multi satellite optical satellites is an essential factor for quantitative Earth observation and multi-satellite data integration. However, due to differences in spectral response functions (SRFs), band definitions, and preprocessing strategies, surface reflectance discrepancy between satellite sensors still exists.
In this study, the surface reflectance data of Korea Multi-Purpose Satellite (KOMPSAT)-3/3A were analyzed together with Sentinel-2 MSI, Landsat-8/9 OLI, MODIS, and New-space Earth Observation Satellite (NEONSAT) data. In order to minimize the influence of surface characteristics, radiometrically stable Pseudo-Invariant Calibration Sites (PICS), including desert areas and the North Pacific Ocean (NPO), were selected.
Before performing the surface reflectance consistency analysis, sensor-dependent reflectance differences were analyzed using spectral response functions (SRFs) based on the USGS Spectral Library. Spectral differences between sensors were evaluated by simulating band-equivalent reflectance through convolution of established hyperspectral surface reflectance spectra, including the USGS Spectral Library Version 7, with the spectral response function (SRF) of each sensor. Based on these simulation results, the Spectral Band Adjustment Factor (SBAF) was calculated by applying the median-based ratio method and the regression method constrained to pass through the origin. The calculated SBAF was evaluated using SRF-based simulated reflectance, and differences in reflectance between sensors before and after adjustment were quantitatively compared and analyzed using statistical indicators such as mean, standard deviation, and RMSE.
Surface reflectance differences showed sensor- and band-dependent patterns, with more evident deviations appearing in the near-infrared (NIR) region compared to other spectral bands. Based on the SRF-based SBAF evaluation, agreement among sensors generally increased, while the degree of improvement varied depending on the spectral band and adjustment strategy, resulting in residual discrepancies in some cases. Overall, these observations summarize the present characteristics of surface reflectance differences observed between KOMPSAT and other optical satellite sensors.
In future studies, the selected PICS will be used to apply radiative transfer model–based atmospheric correction using the 6S model, in order to further assess and improve surface reflectance consistency across multiple optical satellite sensors.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(RS-2025-00515357).
How to cite: Song, S., Han, D., Lee, S., Lee, J., Jeong, S., and Yeom, J.: Development of a Surface Reflectance Consistency Algorithm between KOMPSAT-3A and Sentinel-2A/Landsat Satellites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9238, https://doi.org/10.5194/egusphere-egu26-9238, 2026.
The resilience and sustainability of urban areas depend heavily on the ability to implement strategic programs for the protection and maintenance of civil infrastructure. Structural health monitoring (SHM) of transport infrastructure (e.g., tunnels, bridges, and railways) and lifeline pipelines (e.g., water and energy networks and communication systems) is among the main pillars of urban planning [1-3]. The ability to manage and protect urban infrastructure more effectively is becoming increasingly important, considering the growing urbanization process on a global scale and the exponential increase in extreme events related to climate change. In this scenario, urban areas will be more exposed to and vulnerable to these catastrophic events, resulting in increased socio-economic costs for the maintenance of civil infrastructures. Furthermore, even minor natural events could cause damage through cascading effects in urban networks.
Another key action within the urban planning framework is the introduction of the concept of 'compact cities’. In fact, there is a growing interest in creating spaces that accommodate multiple urban functions and services within proximity, thereby reducing the environmental footprint of urban areas and contributing to reduced energy consumption. Compact cities avoid the problems associated with urban sprawl and are an effective way of adapting to climate change. Once again, the organization of compact cities requires modern, innovative systems for managing civil infrastructures. Smart monitoring is even more important in suburban areas and remote areas, as it enables the concept of inclusivity for the populations living in these areas.
In this scenario, applied geophysics, also known as near-surface geophysics, can significantly support a wide range of urban planning activities. This work focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. In fact, the development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. Prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process, and interpret data, as well as to design smart operational guidelines for infrastructure management, which will be presented at the conference.
- Cuomo, V.; Soldovieri, F.; Bourquin, F.; El Faouzi, N.E.; Dumoulin, J. The necessities and the perspectives of the monitoring/surveillance systems for multi-risk scenarios of urban areas including COVID-19 pandemic. In Proceedings of the 2020 TIEMS Conference, Citizens and Cities Facing New Hazards and Threats, Oslo, Norway.
- Soldovieri, F.; Dumoulin, J.; Ponzo, F.C.; Crinière, A.; Bourquin, F.; Cuomo, V. Association of sensing techniques with a designed ICT architecture in the ISTIMES project: application example with the monitoring of the Musmeci bridge. EWSHM 2014, 7th European Workshop on Structural Health Monitoring, Nantes, France, 8 - 11 July 2014.
- Cuomo, V.; Soldovieri, F.; Ponzo, F.C.; Ditommaso, R. A holistic approach to long term SHM of transport infrastructures. Emerg. Manag. Soc. (TIEMS), 2018, 33, 67–84.).
How to cite: Soldovieri, F. and Lapenna, V.: The role of geophysics in monitoring urban areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17451, https://doi.org/10.5194/egusphere-egu26-17451, 2026.
The BLUE-HeArTS project establishes a UK and EU multidisciplinary partnership and supports the development of a major Horizon Europe Pillar 2 project proposal for the call HORIZON-CL2-2026-01-HERITAGE-01: “Artistic intelligence” (Focus 2). The project employs the transformative power of the arts to address complex societal challenges, enhance soft skills and promote innovation and competitiveness.
BLUE-HeArTS integrates artistic creativity, advanced sensing technologies, and cultural heritage research into a unified framework that addresses climate-driven risks to heritage assets within the “Blue Environment”, i.e., coastal, riverine, and subterranean water systems. By embedding artists as active partners, the project translates scientific data from non-invasive sensing technologies and climate modelling, into storytelling and extended reality (XR) experiences that inspire creativity, empathy, and public engagement.
A central component of the project’s strategy is the evaluation of potential pilot demonstrators, with sites such as the Reculver Tower in Kent, UK, identified as illustrative case studies. The project proposes to explore the application of satellite monitoring, utilising high-resolution thermal imagery, alongside climate modelling to assess heritage vulnerability. Such an approach is designed to inform prototype artistic performances, effectively bridging the gap between technical data and public perception.
BLUE-HeArTS key objectives include:
- Identifying real-world case studies in UK, Italy, and Malta as pilot demonstrators for narrative technologies to be further developed in the Horizon Europe project.
- Designing a simplified XR-based prototype to test the effectiveness of scientific content interpreted through artistic perspectives.
- Engaging partners, including policymakers and social sciences/humanities experts, to explore the historical, cultural, and environmental complexity of the selected sites.
- Developing and refining the Horizon Europe proposal.
Ultimately, BLUE-HeArTS demonstrates that human-centered, art-driven innovation is essential for promoting cultural resilience. By linking technology and culture, the project ensures heritage research is inclusive and resilient to climate change.
Acknowledgements
The Authors would like to acknowledge the project BLUE-HeArTS (January – September 2026), funded by the British Academy under the "Pump Priming Collaboration between UK and EU Partners 2026" programme (Award Reference: PPHE26\100311).
How to cite: Tosti, F., Catapano, I., Ghani, A. M., Daou, D., D'Amico, S., Lastorina, A., Linford, N., Motavallizadeh Naeini, M., and Tessema, T.: BLUE-HeArTS: BLUE Heritage through Art, Technology, and Science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19572, https://doi.org/10.5194/egusphere-egu26-19572, 2026.
In recent years, segments of the Red Sea Rift System (Erta Ale, Hayli Gubbie, areas around Atsbi) and the East African Rift System (Fentale) have experienced intensified seismo-volcanic activity, highlighting the urgent need for enhanced monitoring across Ethiopia’s tectonically active regions. Between July and August 2025, Erta Ale underwent a significant magmatic and diking episode propagating southward toward Afder, followed by renewed activity culminating in the November 2025 eruption at Hayli Gubbie, 11.6km southeast of the Erta Ale lava lake. In the region around Atsbi, seismic sequences in October 2025 appear primarily seismo-tectonic, though potential deeper magmatic involvement remains uncertain. On the other hand, the Fentale region of the East African Rift System exhibited sustained seismicity and deformation from September 2024 to March 2025, affecting nearby communities and infrastructure. Despite the scientific significance of these events, Ethiopia faces challenges in maintaining and expanding geophysical and geodetic monitoring due to limited local resources and occasional constraints on field accessibility, which can affect instrument installation, maintenance, and rapid response, delaying situational awareness and complicating decision-making. This study emphasizes the essential role of collaborative partnerships and new international scientific projects, including shared remote-sensing initiatives, expanded seismic and geodetic networks, technical training, and open-data frameworks, in bridging monitoring gaps, improving early-warning and emergency-response capabilities, and building long-term resilience in Ethiopia’s data-poor and resource-constrained rift environments.
How to cite: Lewi, E., Temtime, T., Biggs, J., Ayele, A., Wright, T., Pagli, C., Keir, D., and Loughlin,, S.: Seismo-Volcanic Crises Across the Red Sea and East African Rifts: The Critical Role of New Projects and International Collaborations in Data-Poor Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1296, https://doi.org/10.5194/egusphere-egu26-1296, 2026.
Unmanned Aerial Vehicles (UAVs) have become vital tools for environmental monitoring and disaster response, particularly in remote regions where ground-based infrastructure is sparse (Erdelj et al., 2017). Yet the practical value of these platforms depends heavily on precise flight control when navigating challenging terrain or conducting time-sensitive surveys after natural hazards. Tuning Model Predictive Control (MPC) weight matrices for such varied operational demands remains tedious and expertise-intensive, which slows deployment during crises.
We present an adaptive control framework merging reinforcement learning with formal stability guarantees. A learning agent tunes controller gains online while Lyapunov-based bounds confine every candidate gain to a provably stable region (Christofides et al., 2011). A projection operator acts as a hard safety layer, clipping any out-of-bounds gain before it reaches the MPC solver. The resulting architecture preserves guaranteed stability regardless of policy network behaviour—essential when aircraft operate beyond visual line of sight in poorly monitored areas.
Validation spans four UAV platforms covering a 200-fold mass range (27 g to 5.5 kg). On an aggressive 3D figure-8 trajectory (±4.0 m on both horizontal axes), tracking improves by 22–27 %. Position root mean square error falls from 0.45–0.55 m to 0.33–0.43 m, with variance reductions of 28–33 %. Across 60 evaluation trials, no stability violations occurred. Sequential transfer learning cuts per-platform training by 75 %, valuable when field crews must swap vehicles mid-campaign—switching, for instance, from a compact quadrotor on initial reconnaissance to a heavier hexacopter carrying hyperspectral sensors.
These results show that rigorous stability guarantees and learning-based adaptation can coexist. For observation campaigns in areas lacking ground-based networks, self-tuning controllers that never risk unstable flight could meaningfully extend what small drone fleets achieve—whether assessing earthquake damage or inspecting infrastructure in informal settlements.
References
Christofides, P.D., Liu, J., Muñoz de la Peña, D. (2011). Lyapunov-Based Model Predictive Control. In: Networked and Distributed Predictive Control. Advances in Industrial Control. Springer, London.
Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24–32.
How to cite: Khan, A. M. and Tessema, T.: Stable Adaptive Flight Control for Multi-Platform UAV Monitoring: Combining Reinforcement Learning with Lyapunov-Guaranteed Gain Tuning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11383, https://doi.org/10.5194/egusphere-egu26-11383, 2026.
This study explores the capabilities of non-destructive testing (NDT) and Earth Observation (EO) not only as measurement technologies but as an evidence translation layer between technical data and sustainability governance. While remote sensing and NDT generate increasingly rich data on urban and coastal environments, a persistent gap remains between these data and the formats, categories, and narratives required for policy, regulation, and organisational reporting [1]. The main aim of this study is to address that gap by developing a sustainability matrix framework that structures, interprets, and reformulates NDT and EO outputs to enable meaningful use in decision-making and sustainability [2]. The matrix also provides accountability processes aligned with the United Nations Sustainable Development Goals (SDGs) [3].
The framework is illustrated through three exemplary case studies strategically spanning the built, natural and heritage environments. These are: analysing land surface temperature patterns across parks and built-up areas (SDG 13); monitoring urban green infrastructure using LiDAR and satellite imagery (SDG 15); and detecting coastal landform change with implications for heritage assets (SDG 11). The applications demonstrate how non-invasive, data-intensive methods provide spatially and temporally resolved evidence on environmental and infrastructural change (SDG 9). The core contribution, however, lies in how these results are translated into decision-relevant indicators and interpretive narratives that align with the UN 2030 Agenda for Sustainable Development [3, 4].
This research shows how technical findings become intelligible and usable for organisations, policymakers, and stakeholders concerned with risk, performance, and long-term resilience. The proposed translation layer provides a replicable approach to embedding environmental factors across planning, asset management, and regulatory contexts. It will provide an evidence base in situations where existing assessment practices are fragmented, inconsistent, or insufficient to meet emerging transparency and disclosure expectations [5, 6].
Keywords: Sustainability Matrix Framework; SDG Mapping; Earth Observation & NDT Integration; Resilience Decision-Making; Multi-Domain Monitoring
References
[1] Tosti, F. (2025) ‘Year III: The NDT—Journal of Non-Destructive Testing 2025 End-of-Year Editorial’, NDT, 4(1), p. 3. Available at: https://doi.org/10.3390/ndt4010003.
[2] Elliott, B. and Elliott, J. (2022) ‘Integrated reporting: sustainability, environmental and social’, in Financial Accounting and Reporting. 20th ed. Harlow, UK: Pearson Education Limited.
[3] Tsalis, T.A. et al. (2020) ‘New challenges for corporate sustainability reporting: United Nations’ 2030 Agenda for sustainable development and the sustainable development goals’, Corporate Social Responsibility and Environmental Management, 27(4), pp. 1617–1629. Available at: https://doi.org/10.1002/csr.1910.
[4] United Nations (2015) Transforming our world: the 2030 Agenda for Sustainable Development.
[5] Lai, A. and Stacchezzini, R. (2021) ‘Organisational and professional challenges amid the evolution of sustainability reporting: a theoretical framework and an agenda for future research’, Meditari Accountancy Research, 29(3), pp. 405–429. Available at: https://doi.org/10.1108/MEDAR-02-2021-1199.
[6] Financial Conduct Authority (FCA) (2022) Sustainability Disclosure Requirements (SDR) and investment labels. London. Available at: www.fca.org.uk/cp22-20-response-form.
How to cite: Mohammad, J., Tessema, T., Tosti, F., and Husamaldin, L.: A Sustainability Matrix Framework for Translating Earth Observation and NDT Data into SDG-aligned Resilience and Decision-Making, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15507, https://doi.org/10.5194/egusphere-egu26-15507, 2026.
