Conference Agenda

Accurate estimation of Soil Organic Carbon (SOC) is essential for sustainable land management, agricultural productivity, and climate change mitigation. This study presents a novel framework for SOC estimation using machine learning models and diverse predictors, including spectral bands, vegetation and soil indices, topographical features, soil texture components, and HSV-derived soil color proxies. SOC data from 180 samples collected between 2007 and 2020 across 21 fields in Manitoba, Canada, were used for model training and validation. Landsat 5, 7, and 8 data were utilized to extract spectral and soil indices, while SoilGrids and SRTM DEM provided texture and topographical features. Random Forest (RF), Extreme Gradient Boosting (XGB), and a BC-VW-based ensemble model were evaluated across five feature scenarios. The ensemble model achieved the highest accuracy, with an R² of 0.57, RMSE of 0.25, and RMSPE of 7.87%, outperforming individual models. SHAP-based feature selection identified Clay%, SWIR1, and Value (HSV) as the most critical predictors. Independent validation using data from 2021 and 2023 confirmed the model's robustness, with RMSPE values of 10.93% and 12.83%, respectively. This study demonstrates the importance of integrating soil-specific indices, texture, and color features with ensemble modeling to improve SOC predictions. The framework offers a scalable and reliable approach for large-scale SOC monitoring, contributing to sustainable agriculture and carbon sequestration efforts. The findings underscore the need for robust uncertainty analysis and independent validation, setting a benchmark for future SOC modeling studies. 

Soil salinization is a major form of land degradation in drylands, where closed hydrological systems, shallow water tables, and strong evaporative demand favor the recurrent buildup of salts at the surface. Accurate and spatially explicit salinity assessment is crucial for guiding agricultural management and land rehabilitation, yet conventional soil sampling remains spatially restrictive and most remote-sensing approaches insufficiently capture the hydrological and pedological processes that drive seasonal salt redistribution. This study evaluates whether post-rainfall spectral information can improve soil salinity mapping in a large endorheic depression in central Morocco (Sehb El Masjoune). A dataset of 121 ECe-measured topsoil samples was combined with multi-sensor optical imagery from Sentinel-2, Landsat-9, and PlanetScope. In addition to standard salinity, soil, vegetation, and moisture indices, two new post-rainfall predictors were developed: a Depression Proxy (DP), delineating moisture-retentive micro-depressions where salts accumulate, and a Soil Cluster Proxy (SCP), capturing soil textural and compositional contrasts from spectral responses. These predictors were integrated into Random Forest and Gradient Boosting Regressor models and evaluated using repeated cross-validation on Box–Cox-transformed ECe. The combination of DP and SCP with Sentinel-2 predictors yielded the highest performance (R² = 0.92; RMSE = 20.53 dS·m⁻¹), outperforming models relying only on spectral indices and topographic covariates. Seasonal salinity maps revealed strong intra-annual dynamics associated with rainfall events and subsequent evaporative concentration. The proposed DP–SCP framework offers transferable, physically interpretable predictors for dryland salinity assessment and provides a scalable step toward process-informed remote-sensing approaches supporting climate-resilient land-use planning.

Soil total nitrogen (TN) is a critical nutrient for sustainable agricultural management, yet large-scale mapping remains constrained by high laboratory analysis costs. Spaceborne hyperspectral remote sensing offers a promising alternative, but its effectiveness is limited by spectral gaps caused by atmospheric water-vapor absorption in nitrogen-sensitive NIR and SWIR regions. This study evaluates the contribution of reconstructing missing spectral domains to improve soil TN estimation from PRISMA hyperspectral imagery. A spectral gap-filling framework combining a conditional generative adversarial network (cGAN) with a self-supervised masked autoencoder pretraining strategy was developed to reconstruct reflectance spectra across water-vapor absorption intervals (950–990 nm, 1320–1500 nm, and 1780–2050 nm), achieving R² = 0.95 on PRISMA test data and R² = 0.91 against ASD FieldSpec III measurements. Applied to 1,037 samples across three Moroccan agricultural regions, incorporating reconstructed bands consistently improved TN prediction: R² increased from 0.83 to 0.89 in Al Haouz, 0.73 to 0.79 in Doukkala, with R² = 0.73 in Khouribga. Feature-selection analyses identified reconstructed water-vapor bands among the most informative predictors (1050–1450 nm, 1800–2100 nm, and 2300–2400 nm). These findings demonstrate that spectral gap filling enhances spaceborne hyperspectral data usability for operational soil TN monitoring and precision agriculture.

This study investigates the performance of a novel L-band passive microwave radiometer mounted on a Remotely Piloted Aerial System (RPAS) for high-resolution soil moisture retrieval. Soil moisture is a critical variable for predicting crop stress, scheduling field operations, and optimizing irrigation, yet traditional measurement approaches have limitations. Satellite radiometers provide broad spatial coverage but coarse resolution, while in situ sensors offer high accuracy with limited spatial representativeness. RPAS-based sensing offers an intermediate solution, enabling fine-scale mapping with flexible deployment. The sensor evaluated in this research, developed by Skaha Remote Sensing Ltd., measures brightness temperature (Tb) at 1.4 GHz, a frequency where soil emissivity varies strongly with moisture content.

Field campaigns were conducted from May to October 2025 at the Elora Research Station in Ontario, with weekly flights over plots containing different crops and tillage conditions. Concurrent ground measurements of soil moisture, leaf area index (LAI), and vegetation water content (VWC) supported evaluation of vegetation impacts. Statistical analyses, including Pearson correlation and linear regression, revealed the relationships between microwave emissions, soil moisture, and vegetation properties.

Results show a strong inverse relationship between microwave emissions and soil moisture, with vertically polarized signals exhibiting the highest sensitivity. Vegetation effects were crop-dependent due to the unique canopy structures. These findings demonstrate that RPAS-mounted radiometers can provide reliable, high-resolution soil moisture measurements and highlight the importance of crop geometry in interpreting microwave observations.

Drought represents one of the most devastating natural hazards, causing billions in economic losses and threatening global food security. Conventional single-variable drought indices often fail to capture drought's multifaceted nature, while existing composite indices are frequently constrained by linear assumptions or operate as 'black boxes,' obscuring physical drivers. This study introduces the State-Space Gradient Drought Index (SSGDI), developed via a novel Gaussian Mixture Model–Minimum Spanning Tree (GMM–MST) framework that re-conceptualizes drought as a trajectory within a physical system. By modeling a 3D state-space composed of the Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), and Standardized Runoff Index (SRI) with a Gaussian Mixture Model (GMM), the framework learns distinct hydro-climatic archetypes; a Minimum Spanning Tree (MST) then imposes physically plausible connections among these archetypes to define the principal wet-to-dry gradient. The final SSGDI is derived from a data point's probabilistic position along this gradient and is complemented by a classification system that diagnoses the drought's physical type. Applied to the Central China Triangle, the framework successfully uncovered the hydro-climatic system's intrinsic, non-linear structure. Validation showed the SSGDI provides a significantly more robust measure, with SSGDI-6 achieving a spatially-averaged Pearson correlation of r = 0.80 against the PDSI benchmark—a marked improvement over any single component. The SSGDI framework bridges robust statistical aggregation with clear physical interpretation, offering a powerful tool that provides not just a severity score but a diagnostic narrative for proactive drought management.

Synthetic Aperture Radar (SAR) has become a popular form of remotely sensed data for agricultural management due to its ability to acquire cloud-free images at extremely high temporal resolutions. A particularly useful product that can be derived from SAR imagery is coherence, which visualizes structural target changes over time based on phase decorrelation. In a crop management context, coherence is largely unexplored. This is in part due to the fine resolution image requirements that field-scale vegetation monitoring demands. Within agricultural fields, high image coherence should correlate to areas with minimal to no crop growth, whereas low image coherence should correlate to areas where crops are consistently growing. Based upon this hypothesis, our research investigates the applications an ICEYE fine spatial resolution X-band SAR imagery time series has for detecting low yielding regions within corn fields using coherent change detection.

Flooding stands as one of the world's prominent natural hazards, which exerts severe threats to sustainable socioeconomic development. Physical asset information in flood disasters refers to the location, quantity, and damage severity of exposed elements within the affected area. Rapid and accurate extraction of such information is crucial for flood disaster emergency management. To achieve this goal, a remote sensing-based framework for extracting physical asset information in flood disasters is proposed in this paper. This framework summarizes extraction methods for flood damage to typical asset types including cropland, buildings, and roads, and comparatively analyzes the advantages and limitations of multi-source remote sensing data, geographic data, and social media data in physical asset information extraction. This study further investigates the differences between statistical analysis, shallow learning methods, deep learning, and transfer learning approaches, with respect to three key dimensions, namely extraction accuracy, scenario applicability, and computational efficiency. Future research should focus on: (1) Development of operational technologies for flood emergency response and disaster mitigation; (2) multi-source data fusion and dynamic simulation based on digital twin technology; (3) intelligent mining of multi-modal information and development of generalized extraction models driven by foundation models, with the aim of providing technical support for rapid flood emergency response.

Rapid flood damage assessment is essential for emergency response and post-disaster recovery. Following catastrophic flooding in the Haihe River Basin on July 28, 2023, the Dongdian flood detention basin was activated on August 1, with inundation persisting until early October. This study integrates satellite remote sensing, UAV imagery, and field surveys to develop a rapid multi-source approach for comprehensive flood loss assessment. The methodology comprises: (1) extraction of inundation characteristics (spatial extent, depth, duration); (2) classification of exposed assets (agricultural land, forests, residential and industrial areas); (3) comprehensive damage and economic loss evaluation. Results show that 301.49 km² (79.55% of the basin) was inundated from August 1 to October 5, 2023, with an average depth of 2.64 m. The central-western zone sustained the most severe damage, with prolonged residential inundation. Complete corn crop failure occurred, and agricultural-forestry production suffered near-total losses. Direct economic losses exceeded 17.5 billion yuan. Compared to traditional field methods, this approach demonstrates superior efficiency and accuracy, providing scientific support for flood risk management in detention basins.

Coastal environments provide various economic, ecological and societal benefits. Coastal erosion which is the gradual loss of sediment over time, poses a significant threat to South Africa’s coastline. The monitoring and detection of coastal erosion is essential for the effective management of coastal environments. One way to quantify coastal erosion is the delineation of coastal boundaries. Remote sensing techniques such as Synthetic Aperture Radar offers a unique opportunity to extract shoreline positions over large areas of the coast. Furthermore, thresholding and edge detection methods have been successfully used to extract land-water boundaries. In this study, C-band SAR data was used to derive backscatter coefficients for three different areas of interest in the Eastern Cape province in South Africa over a ten year period. The coastal erosion and accretion trends were calculated from the results indicated that the Linear Regression Rate (LRR) for the three different study area showed various coastal erosion seasonality trends. The shoreline LLR ranged between -0.01 and -3.28 m/year for the Cape Recife area and -0.17 and -4.78 m/year for the Nelson Mandela Bay beach front. The overall pattern was erosion during the winter months and accretion during the summer months. In contrast, for the Kings Beach area, there was a consistent accretion trend where the LRR values ranged between 0.94 and 1.68 m/year. The findings confirm that SAR remote sensing is suitable for detecting and monitoring coastal changes in three different coastal environments.

Oil spills in confined maritime corridors often evolve faster than any single satellite mission can observe. This often complicates the interpretation of individual images and create gaps in understanding how a spill progresses between satellite overpasses. This study examines whether combining Sentinel-1 and Sentinel-2 observations can provide a more coherent picture of its development of a spill event, using the case of an oil spill occurred near the Strait of Gibraltar in late August 2022 after a collision between the OS35 and the Adam LNG.

The preliminary analysis evaluated each sensor separately. Sentinel-1 highlighted changes in surface roughness, while Sentinel-2 revealed reflectance anomalies linked to modified optical properties of the water. Since neither dataset on its own offered a complete account of the surface conditions, a fusion procedure was applied to the closest pair of post-event images. The fused map displayed sharper boundaries and more spatial detail than the radar scene alone, offering a clearer outline of the affected area. To address the temporal mismatch between acquisitions, intermediate surfaces were also reconstructed for both sensors, producing estimated representations of the marine conditions at dates not directly observed.

Taken together, the fused and reconstructed products formed a more continuous sequence of the spill’s evolution, capturing both its fragmentation and its short-term reorganisation. Although the approach does not replace dedicated operational monitoring, it demonstrates that combining complementary satellite data can reduce ambiguity in single-sensor interpretation and strengthen situational awareness in regions where surface conditions change quickly and unpredictably.

Windstorms in Peninsular Malaysia have increased in both frequency and severity, posing growing risks to communities, infrastructure, and the national economy. Despite these escalating threats, the region currently lacks a comprehensive, location-specific index capable of evaluating and categorizing windstorm hazards for effective planning and mitigation. This study develops a Windstorm Hazard Index (WHI) tailored to Peninsular Malaysia to assess spatial patterns of windstorm risk and support evidence-based decision-making. Four objectives were addressed: (1) identifying key environmental and geographical factors influencing windstorm occurrences; (2) quantifying these parameters using windstorm records from 2008–2018, numerical simulations generated via WRF-ARW, and urban morphology modelling using Envi-MET; (3) formulating the WHI through the integration of Analytic Hierarchy Process (AHP) and Principal Component Analysis (PCA); and (4) validating the index using documented windstorm events from 2020–2024.The WHI categorizes the peninsula into six hazard levels ranging from very low (0.1–0.5) to extreme (0.901–1.0). Southern and central states, including Negeri Sembilan and Pahang, generally exhibited very low hazard levels, while Kelantan and Terengganu showed moderate risk. High-risk zones were concentrated in northern and coastal regions such as Penang, Kedah, and Perlis, with extreme-risk areas detected in parts of Kedah and Perlis. Results indicate that wind speed, temperature, humidity, precipitation, land use, and urban density strongly influence windstorm intensity, particularly in coastal and densely built environments. Validation confirmed the WHI’s reliability, as extreme-risk classifications aligned with recorded damage patterns. Overall, the WHI serves as a robust framework for regional hazard assessment and disaster-resilient infrastructure development across Peninsular Malaysia.

Flooding is one of the most frequent and costliest hydro-meteorological hazards, impacting every nation and causing significant societal and economic disruption. Despite the abundance of Earth Observation (EO) datasets and hydrodynamic models available to map, monitor, and forecast flood events, decision-makers and first responders often struggle to translate these resources into actionable insights. To bridge this gap, we’ve developed the Flood Risk Index for Resilience (FRI-R), a data-driven machine learning model designed to support resource planning, emergency response, and downstream analytics. FRI-R is powered by the Model of Models (MoM), an operational, open-source ensemble framework that integrates outputs from hydrologic models and EO data from optical imagery. Leveraging historical MoM outputs, FRI-R analyzes the spatial and temporal patterns of past flood events and classifies sub-watersheds from high to low risk based on flood frequency and duration, offering a dynamic lens into vulnerability hotspots. MoM has proven effective in disseminating early flood warnings. Building on this success, FRI-R is designed to enable targeted interventions for at-risk populations and critical infrastructures, thereby empowering communities and decision-makers to proactively mitigate and improve long-term resilience.

This paper introduces the 3D-4CH project and its open framework, i.e. a sustainable ecosystem of tools designed to overcome the fragmentation and limited maintainability of previous EU-funded 3D heritage initiatives. Aligned with the European Collaborative Cloud for Cultural Heritage (ECCCH), the framework integrates an end-to-end pipeline for 3D data generation and processing, semantic enrichment and long-term dissemination, including metadata and paradata inclusion. The 3D-4CH initiative bridges the gap between ICT research and operational heritage practices, ensuring the scalability and reproducibility of 3D digital assets for cross-institutional data sharing and preservation. All software components, including GitHub repositories and online processing frameworks, are openly available, in accordance with open science principles and FAIR data practices. Further information is available at https://www.3d4ch-competencecentre.eu/en/tools/.

The paper presents the integrated 3D metric survey of the Genete Leul Palace in Addis Ababa, demonstrating how metric reliability and operational speditivity can coexist through an adaptive hybrid TLS–MMS workflow that supported the restoration project and heritage documentation in a low-infrastructure context.

Laser scanning, photogrammetry, and other technical tools are staples for cultural heritage documentation and reverse engineering projects. However, manufacturers and even researchers often conflate the data capture process with reverse engineering itself, even though the data alone cannot provide the insight needed for a full reverse engineering or understanding of the historic site. This paper illustrates how laser scanning and photogrammetric applications were used in reverse engineering the construction and details of Conrad’s Jewel, a 1908 Silver/Gold mill in the Yukon, Canada. Analogous to systems and software engineering fields, the reverse engineering process is framed by considering related designs, existing documentation, personal experience, and general external knowledge.

The 3D reconstruction of cultural heritage artefacts plays a crucial role in documentation, conservation and dissemination. While recent advances in photogrammetry, laser scanning and neural rendering techniques have significantly improved the geometric accuracy and visual realism of digitised assets, the reconstruction of transparent and reflective materials - typical in museal collections - remains a major challenge. Materials such as glass, glazes and varnishes exhibit complex optical behaviours, leading to incomplete or inaccurate 3D models. Recent developments in Gaussian Splatting (GS) offer a potential alternative by enabling efficient, high-fidelity scene representation without explicit surface modelling. However, their application to non-Lambertian and transparent heritage objects remains largely unexplored. This paper presents a study on GS methods for the 3D digitisation of transparent cultural heritage artefacts. Through a series of experimental reconstructions, the work investigates the potential and limitations of GS, highlight the opportunities of hybrid pipelines for addressing long-standing challenges in the digitisation of non-collaborative materials.

In recent years, the European Commission (EC) identified the 3D digitization of cultural heritage sites and artifacts as one of its priorities and promoted numerous initiatives and recommendations to accelerate documentation campaigns. However, current digitization targets remain far from being achieved, and heritage institutions have been increasingly encouraged to explore faster and cost-effective 3D documentation solutions. Moreover, traditional image- and range-based 3D surveying techniques frequently struggle when reconstructing objects featuring non-collaborative surfaces (such as reflective or transparent objects), are time-consuming, and require specialized knowledge. Generative AI methods, able to generate 3D models also from a single input image, have recently emerged as a potentially faster alternative, yet their performance on heritage assets remains mostly unexplored. This paper evaluates three state-of-the-art and recent single-image GenAI frameworks - SAM3D, Tripo3D and Trellis2 - on several museum artifacts featuring diffuse, reflective, transparent, and mixed-material surfaces of varying scale and geometric complexity, for which accurate ground truth is available. The aim is to analyze whether these frameworks can act as complementary or alternative solutions for fast heritage documentation.

To address the issues of geometric distortion and loss of details in 3D modeling for complex cultural heritage scenes, this paper proposes an improved 3D Gaussian Splatting (3DGS) reconstruction method that integrates LiDAR and illumination-awareness. First, high-precision 3D coordinates from LiDAR point clouds are utilized to guide the initialization of Gaussian Primitives, establishing a precise geometric foundation and effectively overcoming deformation on weakly textured surfaces. Second, an illumination-aware network is constructed to dynamically adjust appearance parameters by combining global illumination from images with LiDAR reflectance intensity. This decouples complex lighting from material properties, accurately reproducing the unique textures of artifacts. Finally, a multi-dimensional joint loss function incorporating photometric, scale, and appearance smoothness constraints is introduced to collaboratively optimize scene geometry, appearance, and camera poses. Experimental results on indoor and outdoor cultural heritage preservation scenarios demonstrate that the proposed method significantly outperforms various comparative algorithms in terms of both visual fidelity and geometric accuracy. The quantitative and qualitative evaluations confirm that our approach effectively eliminates geometric distortions and recovers fine texture details, providing an efficient and reliable technical solution for the digital preservation of cultural heritage.

Cultural Heritage assets as the Bremen Cog at the German Maritime Museum are often subject to long-term preservation processes and being monitored over time. The Bremen Cog, a clinker-build vessel from 1380, was found in the River Weser in 1962 and thereafter salvaged and reconstructed until 1981. Prior to conservation efforts (1981 to 1999), a photogrammetric 3D measurement campaign was conducted using a stereometric camera SMK 120. Due to deformation a permanent support system was installed in 2003 including the application of local corrections using pressure plates to correct the hull to its measured one from 1980. Since 2020 a long-term geometric monitoring of the cog has been carried out in order to detect deformation. With the analyses of the monitoring data in connection with the measurement conditions, it is of high interest whether the cog in its current shape corresponds to the one estimated in 1980. Historic SMK 120 stereo image pairs on glass plates are analysed in order to estimate their usability and potential for 3D object reconstruction and subsequently comparing the results to the current monitoring data. The proposed workflow includes an optimized digitization process of the glass plate and reconstruction of the interior and exterior orientations. Feature detection and matching methods as well as robust orientation tasks are analysed in order to allow for a 3D hull reconstruction. The reconstruction at least in parts of the cog and with lower precision is desirable and promising in terms of evaluating changes of the hull over time.

Photogrammetry and laser scanning are widespread tools for documenting movable and immovable cultural heritage assets. Documenting the entire surface of an object presents a set of specific challenges, with various solutions currently available. Complete object documentation relies on established capture techniques that utilize the registration method for different model orientations. This paper presents the “Mask Model Method,” a semi-automatic approach for seamlessly documenting entire objects while seeking high-quality results. This workflow works well for most objects that would be considered viable for general photogrammetric capture. The advantages are also in capturing small and large objects (with and without a turntable) with hinge-type moving parts. This method of documenting full architectural artefacts is useful in heritage conservation, repairs, and restoration; specifically, digital patternmaking, virtual reconstruction, digital annotation of historic materials & geometry, and applied experimental archaeology.