Conference Agenda

Artificial Intelligence (AI) integration has become an essential of modern AEC workflows, yet it has failed to gain a position in waste management. This gap is particularly prominent given the urgent environmental and legal imperatives for the sector to mitigate its demolition outputs. Existing approaches to waste classification and diversion cost estimation rely on manual interpretation of project documentation, a process that is both resource-intensive and structurally incompatible with the machine-readable data environments established by Building Information Modelling (BIM). This paper presents a framework that bridges Industry Foundation Class (IFC) compliant BIM data and Large Language Model (LLM) capabilities to automate Construction and Demolition Waste (C&DW) classification and probabilistic cost optimisation. The framework utilizes IfcOpenShell to extract element geometry and material data, channeling this information into a Retrieval-Augmented Generation (RAG) pipeline. To ensure rigorous compliance during classification, a FAISS-indexed knowledge base grounds a locally deployed Llama3 model against the specific mandates of Province of Ontario, Canada regulation 102/94. Diversion cost scenarios are computed through a Bayesian cost module coupled to a multi-objective genetic algorithm (MOGA) optimiser. Th proposed approach is evaluated against a labelled dataset of 104 IFC type-and-material combinations, the RAG classifier. Performance thresholds were established a piori based on multi-class classification benchmarks and Bayesian cost model uncertainty tolerances. The framework achieved a macro-average F1 of 0.84 and overall accuracy of 88%, satisfying the minimum criteria for automated C&DW characterization under Ontario Regulation 102/94.

This case study details the integration of official large-scale open 2D and 3D geospatial data of the city of Berlin, Germany, into the Virtual Battlespace 4 (VBS4) simulator for security applications. Realistic scenery with elements specific to the target area is obtained from a digital terrain model, true-ortho mosaic, and high-resolution land use/land cover layer rasterized from OpenStreetMap vector primitives. For the central Mitte borough with its government institutions and foreign embassies, almost 20000 buildings are prepared from textured CityGML data in an automatic multi-stage process. This process involves pre-wrapping the texture images, which are referenced by the semantic 3D models using non-canonical coordinates, and the rapid creation of compact atlases to reduce the bitmap count by three orders of magnitude. To ensure that the building meshes blend seamlessly into the terrain, vertical adjustment methods are discussed, and ground extrusion is implemented to approach the model's base surfaces from below. Data import into VBS4 happens through its Geo interface for the terrain, ortho, and land cover, while the buildings are compiled into an add-on with a custom workflow that involves reprojection, collision component setup, and damage behavior configuration. During interactive convoy training in the virtual environment, a high recognition value compared to the real landscape could be attested visually. Simulation exhibited acceptable frame rates, but required considerable computing resources.

Water scarcity and increasing demand have made sustainable water management a global priority, reflected in UN SDG 6, which emphasizes water-use efficiency and reducing water scarcity. Smart Water Management (SWM) has emerged as an advanced, data-driven approach that leverages ICT and IoT systems to monitor, analyze, and optimize water use. Digital Twin (DT) technology enhances SWM by creating dynamic virtual replicas of physical systems to support predictive analytics and operational intelligence. While DTs are widely used in large-scale Water Distribution Networks, these implementations typically do not require detailed 3D modelling.

Campus-scale water systems present unique challenges due to the integration of external and interior water networks, variable building functions, and the need for detailed spatial representation. This study proposes a comprehensive DT framework for Smart Water Management at Toronto Metropolitan University. It integrates BIM, GIS, sensor data, and graph-based modelling to capture 3D interior utilities and enable real-time monitoring, hydraulic simulation, and network analysis. The framework adopts Tao et al.’s five-layer DT architecture and introduces the IFCGraph Model, which combines IFC multipatch geometry with a Neo4j knowledge graph for enhanced interoperability and topological analysis. Overall, the framework supports operational intelligence, proactive management, and scalable campus-level water system optimization.

Urban Digital Twins (UDTs) are increasingly recognized as enablers of evidence-based planning and citizen engagement. While the involvement of civil society in planning the built environment is well established, its role and motivation in advancing the clean energy transition remain largely unexplored. This paper presents the development and application of an Open Geospatial Consortium (OGC) standards-based UDT platform for the co-creation of Positive Energy Districts (PEDs), as demonstrated through the Nordbahnhof district case study in Stuttgart. The platform integrates interoperable 3D city and energy data using CityGML 2.0 with its Energy ADE 3.0 extension, both compliant with OGC standards to ensure semantic consistency and cross-domain interoperability. SimStadt energy simulation results are stored in the Energy ADE schema within PostgreSQL/3DCityDB database. These data are published through an OGC Web Feature Service (WFS), while 3D city geometries are served as 3D Tiles. In the CesiumJS web-viewer, both services are linked via GML identifiers, enabling coordinated interaction between geometry and energy data for real-time visualization of the district-scale energy balance. The platform was tested with citizens, who learned about load profiles, photovoltaic (PV) potential, and energy efficiency while acting as “district energy planners.” Their responses/willingness to adopt PV and/or modify energy-use behavior were translated into slider inputs to visualize real-time energy-balance outcomes through the platform. Results demonstrate the potential of interoperable, OGC-compliant UDTs to connect data providers, planners, and citizens in a shared decision-support environment. The architecture’s open, modular design enables wider replication, promoting scalability and long-term municipal adoption for participatory energy-transition planning.

Building Information Modeling (BIM) is increasingly mandated worldwide as part of the digital transformation of the construction industry. While widely used in design and construction, its potential for managing construction and demolition waste (C&DW) remains underexplored, despite demolition accounting for 70–90% of building-related waste and 30–40% of global solid waste. Revit models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This study develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO₂ emissions and energy intensities. A case study of York University’s Bergeron Centre illustrates the framework’s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO₂ reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.

This contribution presents a prototype Earth Observation-based simulation framework to assess how large-scale urban interventions affect Land Surface Temperature (LST). Focusing on the Metropolitan City of Milan (Northern Italy), the framework integrates thermal (Landsat 8/9) and multispectral (Sentinel-2) satellite imagery with Local Climate Zone (LCZ) maps, urban morphology and material fraction layers. Random Forest regression models are trained to predict seasonal LST patterns. A simulation module, based on raster algebra, enables scenario testing by modifying predictor layers to reflect planned urban transformations, generating corresponding LST responses. The framework is conceived for integration into Urban Digital Twin platforms to support “what-if” scenario analyses for climate-resilient urban planning and adaptation.

Urban trees play a crucial role in mitigating urban heat islands through shading and transpiration, processes directly linked to Leaf Area Index (LAI). However, estimating LAI for individual urban trees remains challenging due to their geometric and temporal heterogeneity.

This study evaluates the PATH (Path length distribution) method, a terrestrial laser scanning (TLS) based approach, to estimate LAI for three urban tree species in Strasbourg, France. The PATH method models foliage area volume density from point clouds, accounting for non-random foliage arrangements and woody structure contributions, unlike traditional optical methods.

TLS campaigns were conducted in three streets at three phenological. The sensitivity of PATH to geometric reconstruction parameters was assessed to optimize LAI estimation. Results show that envelope geometry significantly influences PAI estimates, with concave shapes (of at least 3000 facets) yielding more accurate values, while leaf angle distribution has minimal impact.

The obtained LAI estimates varied by species, reflecting species-specific crown densities. PATH-derived PAI was compared to LAI-2000 optical sensor measurements and direct LAI obtained by leaf collection. PATH estimates aligned more closely with true LAI than LAI-2000, especially during early leaf expansion, though discrepancies arose due to branch pruning and polycyclic flushing. The study highlights the importance of adapting scanning protocols and PATH parameters to species-specific morphology.

In conclusion, this work highlights the potential of TLS-based methods for providing robust PAI estimates for urban trees. Future research will link these species-specific estimates to urban microclimate benefits.

South Africa is the leading soybean producer in Africa, contributing approximately 35% of the continent’s total production. Soybean is important for national food security and agricultural sustainability–– serving as a key nitrogen-fixing crop that support soil fertility and economic growth. Whilst monitoring biochemical parameters such as leaf chlorophyll content (LCC) is essential for assessing the soya bean health, cultivar-level variability can complicate the use of remote sensing–based approaches. This study evaluates the performance of four machine-learning algorithms, XGBoost, Random Forest, Partial Least Squares Regression, and Artificial Neural Network, using unmanned Aerial Vehicle based data across 15 soybean cultivars during the early reproductive phase. Results show that model performance is strongly cultivar dependent. Tree-based models achieved the highest accuracy, with XGBoost and Random Forest reaching RMSE values as low as 2.9 µmol m⁻² for PHIP62T16R and R² values up to 0.96 for RA655R, while ANN and PLSR performed substantially worse for cultivars with more complex spectral responses, such as PAN1555R. Residual results from generalised models revealed systematic over- and under-prediction in several cultivars, indicating that models developed using pooled data are unable to fully account for cultivar-specific spectral differences. Variable-importance analyses identified red-edge, NIR, and greenness-enhancing indices as the most influential predictors of LCC, highlighting their strong sensitivity to canopy structure and chlorophyll variation. Overall, the study shows that cultivar-specific, ensemble-based modelling delivers stronger predictions of chlorophyll in soybean. Incorporating cultivar information and using stratified model calibration improves the reliability of UAV-based chlorophyll monitoring in heterogeneous soybean canopies.

The monitoring of crop traits on a landscape scale is of key interest in the context of precision farming and food production. Many studies use moderate-resolution satellite data like Sentinel-2, Landsat for crop monitoring. However, enhanced spatial resolution is improving monitoring quality significantly. In this context, commercial but expensive very high resolution (VHR) satellite data from Ikonos, Quickbird, Formosat-2, and WorldView-2 have been successfully applied for crop monitoring over the last two decades.

The focus is on the research question “Can Pléiades Neo data quantify plot-scale variation in dry biomass and N uptake?” and on developing an analysis workflow which could support precision farming on a landscape scale using VHR satellite data.

In this contribution, we propose the application of pansharpened Pléiades Neo satellite data for the monitoring of crop traits like dry biomass and N uptake - in our study for winter wheat. The very high spatial resolution of 0.3 m even allows to investigate field experiments with plot sizes of several m2 and therefore, would be suitable for crop phenotyping.

Smallholder farmers play a critical role in the growing of underutilized crops, such as sweet potato. Obtaining accurate maps of sweet potato biophysical variables is essential for farmers to assess and monitor crop health at different growth stages. Integrating radiative transfer model (RTM) data with vegetation indices (VIs) based on unmanned aerial vehicle (UAV) data, may have the potential for accurately estimating leaf chlorophyll concentration (LCC) across multiple crop varieties. Firstly, in this paper we developed and tested varying hybrid retrieval models by combining PROSAIL RTMs with broadband, narrowband and leaf-pigment VIs applied to 2-cm resolution UAV imagery, to retrieve LCC over 20 sweet potato varieties at 120 days i.e. matured growth stage. Secondly, the best hybrid retrieval model was transferred to a different site which contain similar sweet potato varieties at matured growth stage for the estimation of sweet potato LCC. Results show that the most accurate retrievals of LCC were achieved by integrating a larger database containing 11000 PROSAIL simulated reflectance samples with broadband indices, particularly the enhanced vegetation index (EVI) with coefficient of determination (R2) of 0.85, root mean squared error (RMSE) of 5.93 µg/cm2, and relative RMSE (RRMSE) of 9.87%. Furthermore, when transferred to a different site containing similar sweet potato varieties at matured growth stage, this model achieved 60% agreement with field LCC measurements and responded fairly well by capturing LCC variability. These findings have significant implications in sweet potato breeding programmes for developing new cultivars.

Remote sensing and laboratory assays can improve field-scale crop assessment and management. This exploratory pilot study analyses relationships between leaf tissue nutrients and UAV-derived normalised difference vegetation index (NDVI) using seventeen paired samples collected across a mixed crop trial. Tissue measures for nitrogen, phosphorus and potassium were standardised and entered into principal component analysis to reduce pairwise correlation and extract orthogonal nutrient axes. The first principal component explained 54.79% of variance, the second explained 34.10%, together accounting for 88.9%. Principal component scores for the first two axes were used in linear and polynomial regression models to predict NDVI. Model skill was assessed on training data and with leave-one-out cross-validation, and bootstrap resampling produced empirical confidence intervals for component loadings. Linear models built on principal components provided the most stable cross-validated performance, while polynomial expansions improved training fit but generalised poorly. These findings indicate that a low-dimensional nutrient representation can predict NDVI with reasonable stability and that combining spectral and biochemical data supports spatially explicit nutrient assessment. The study recommends expanded and stratified sampling, reflectance calibration and targeted spectral bands for follow-up studies, and external validation before wider applications.

This study integrates multispectral (UAV) and hyperspectral (ground-based) remote sensing data to estimate coffee (Coffea arabica) yield using machine learning algorithms. Forty field plots were analyzed with multispectral Mavic 3M imagery and hyperspectral Blue Wave spectroradiometer data. Spectral indices such as NDVI, NDRE, GNDVI, CIRE, and PRI were correlated with yield, revealing distinct responses across spectral domains. Neural networks achieved the best predictive performance (R = 0.93; RMSE = 7.9%), followed by SVM models (R = 0.90). The Red Edge and Green bands were most sensitive to productivity variations in multispectral data, while hyperspectral narrowband indices provided superior correlations with canopy physiological traits. The integration of both datasets highlights the complementary strengths of spatially extensive multispectral imagery and the spectral precision of hyperspectral sensing. This multiscale approach enables more accurate and operational yield estimation for perennial crops and supports the development of precision agriculture protocols for coffee production systems.

In Indoor Mobile Mapping Systems (iMMS) the trajectory estimation is implemented by the SLAM (Simultaneous Localization and Mapping) algorithm. By assuming a fixed environment surrounding the instrument, the algorithm relies on stable geometries to establish the trajectory. Drift effects represent the main source for errors and affect the trajectory estimation. These effects can be magnified in feature-deficient or degenerate environments, where the variation of geometrical elements can be minimal, as in the case of tunnels. In this context, difficult environments such as tunnels are suitable for the implementation of alternative algorithms for the trajectory estimation. Considering this kind of scenario, the contribution has the twofold objective of evaluating the results of two trajectory estimation methods, in terms of trajectory drift, with reference to an indoor SLAM-based MMS, and to establish a repeatable methodology to do so. A novel algorithm for the trajectory estimation, not just relying on geometrical SLAM algorithm, but also taking advantage of reflectance images coming from LiDAR sensors mounted on the system, is considered.

The case study is a 200 m long branch of a motor-way tunnel, with a diameter of 15 m. The test is further subdivided by computing all trajectories with different constraining strategies, first without any constraints, then considering global optimisation, loop closure and static control scans, to replicate typical realistic scenarios in tunnel mapping. The results of this work highlight how the novel reflectance-aided SLAM algorithm is beneficial in terms of drift reduction in the estimated trajectories.

This presentation addresses the challenge of range errors in point clouds of road signs captured by Mobile Mapping Systems (MMS) equipped with phase-shift laser scanners.

Under certain conditions, retroreflective materials cause range errors in point clouds. Previous studies have proposed mitigation techniques for range errors caused by sensor saturation in TOF systems, but similar studies on phase-shift systems are scarce. In addition, existing road sign detection methods assume accurate point representation, making them ineffective when sign points are displaced.

To overcome this limitation, we developed a detection method that first extracts road signs through point cloud visualization and then identifies range errors based on the standard deviation of relative distances from reference emission points.

The proposed approach was validated using 5 km of driving data collected on general roads. Results show that 32 road signs were extracted, and 26 were correctly detected as exhibiting range errors, achieving 100% agreement with manual visual assessment.

This study demonstrates the effectiveness of the proposed detection method and its potential for improving the reliability of identifying range errors of road signs on general roads.

RTK-SLAM systems integrate simultaneous localization and mapping (SLAM) with real-time kinematic (RTK) GNSS positioning, promising both relative consistency and globally referenced coordinates for efficient georeferenced surveying. A critical and underappreciated issue is that the standard evaluation metric, Absolute Trajectory Error (ATE), first fits an optimal rigid-body transformation between the estimated trajectory and reference before computing errors. This so-called SE(3) alignment absorbs global drift and systematic errors, making trajectories appear more accurate than they are in practice. We present a geodetically referenced dataset and evaluation methodology that expose this gap. A key design principle is that the RTK receiver is used solely as a system input, while ground truth is established independently via a geodetic total station. This separation is absent from all existing datasets, where GNSS typically serves as (part of) the ground truth. The dataset is collected with a handheld RTK-SLAM device, comprising two scenes. We evaluate LiDAR-inertial, visual-inertial, and LiDAR-visual-inertial RTK-SLAM systems alongside standalone RTK, reporting direct global accuracy and SE(3)-aligned relative accuracy to make the gap explicit. Results show that SE(3) alignment can underestimate absolute positioning error by up to 76\%. RTK-SLAM achieves centimeter-level absolute accuracy in open-sky conditions and maintains decimeter-level global accuracy indoors, where standalone RTK degrades to tens of meters. The dataset, calibration files, and evaluation scripts are made publicly available. The dataset, calibration files, and evaluation scripts are publicly available at https://rtk-slam-dataset.github.io/

Scene reconstruction and novel view synthesis from calibrated multi-view images still attracts a lot of attention in computer vision and graphics. However, the assumption that images are noise-free rarely holds in real-world scenarios where adverse weather conditions are inevitable. Being a part of our environment, we are particularly interested in rain as dynamic semi-transparent occlusion which imposes challenges to a complete and accurate geometry of the underlying features. More precisely, we qualitatively and quantitatively analyze the photometric image quality under rainy conditions generated by radiance field methods, namely: Neural Radiance Fields (NeRFs), 3D Gaussian Splatting (3DGS) and 2D Gaussian Splatting (2DGS) due to the different geometric representation. To assess the impact of rain to the scene reconstruction we consider raindrops and streaks captured with illumination variation as well as occlusion masks with different coverage. The evaluation is based on comparing 2D image metrics of the rendered novel views without and with masks. The experiments and results show that 3DGS achieves highest rendering fidelity in all scenarios without and with masks with SSIM of 0.724 and LPIPS of 0.291, followed by 2DGS with slightly lower scores, while NeRF exhibits lowest correspondence with the input images with SSIM of 0.584 and LPIPS of 0.384. We demonstrate the effectiveness of using masks to handle rain as transient element and radiance field methods’ ability to reliably approximate the geometry behind rain occlusions.

In recent years, many coastlines worldwide have retreated under the influence of storm surges and other extreme events, exacerbated by intensifying wave conditions in certain regions and seasons. Consequently, wave-dissipating units (e.g., tetrapods) have been widely deployed for coastal protection. In this paper, we propose a novel three-dimensional geometric method for extracting robust feature points from 3D point clouds to track tetrapod displacements and assess seawall safety. The model represents a tetrapod as four cylinders sharing a common center. By fitting this geometric model to the point cloud, we obtain parameters that allow us to derive multiple feature points—such as the intersections of conical surfaces—which can also be verified through alternative measurement techniques. These feature points serve as stable references for position comparison and displacement estimation. As this research is at an early stage, we have not yet collected field data from full-scale tetrapods. Instead, we conducted indoor experiments using a 3D depth camera (Microsoft Azure) in place of LiDAR, utilizing several high-fidelity resin tetrapod scale models (approximately 10 cm in height) as test subjects. The results demonstrate the feasibility of our method: when compared against total-station measurements, our approach yields highly accurate displacement estimates (averaging approximately 3 mm). This provides a solid foundation for the future deployment of 3D laser scanning in seawall monitoring.

As the technology of hydroacoustic sensors advances, there is a growing trend in the use of generated sonar images and point clouds in the analysis of the seabed and objects of anthropogenic origin in water bodies. In the context of cognitive and practical dimensions, obtaining data on sunken ships is of particular importance. Based on the data obtained from hydroacoustic sensors, it is possible to extract their geometric features. As a result, it is possible to develop digital repositories of wrecks, based on sonar and bathymetric data, among others, which in the future may enable the construction of integrated knowledge bases on underwater heritage. The purpose of the work was to extract the geometric features of the wreck of the Zawiszaczek located in the Puck Bay of the Baltic Sea. As part of the work, bathymetric measurements were planned, side-scan sonar and multibeam echosounder data were collected. Based on the acquired data, the geometric features of the wreck were extracted. The differences in the wreck's dimensions, as determined by sonar images obtained from different routes, did not exceed 0.25 m.