Cities are complicated entities with multiple stakeholders operating data infrastructures complying to different regulations and standards in heterogeneous environments; this can be challenging when developing a smart city data platform to support cross-sector urban data management. Recent advances in Internet-of-Things technology can combine real-time data streams, such as weather sensors, traffic lights, cameras, and parking sensors, in a smart city data platform that supports city decision-making and enables new collaborations and knowledge production. This paper uses a case study methodology to analyze the Smart City Operating System (SCOS), part of a Smart City project awarded by the US Department of Transportation in 2016 in Columbus Ohio. SCOS was developed as a robust smart city data management platform. However, despite a well-designed organization, methodology, and processes, the platform did not sufficiently capture city users, and was no longer used soon after demonstration funding ended in 2021. We employ a literature review, project completion reports, key informant interviews, and a project evaluation to understand the value and limitations of SCOS and consider how it could have better captured city users. Our comparative analysis of the UK Observatories shows that their more restrained “living laboratory” vision, university support, and stable funding environment helped them endure, although they serve primarily as a research platform rather than a city management platform. To make recommendations for future city data platform projects, we discuss organizational and technical aspects of conducting smart city projects, including continuous stakeholder engagement, required data ownership and real-time data management support. The results aim to support city stakeholders in developing future data platforms and provide urban management support.

The importance of accurate and reliable DTMs are paramount for hydrodynamic modelling. Currently, bathymetry is either not considered or a simpler mathematical representation of the river is created from observed cross sections for hydrodynamic models. Topographic and Bathymetric LiDAR creates centimetre resolution bathymetry and topography. However, it has not been applied into large scale hydrodynamic modelling. Following a major flood event in September 2024, a large fieldwork campaign on the Pielach River, Lower Austria, was conducted to capture the environmental changes using Topographic-Bathymetric LiDAR. Metre resolution DTMs of the bathymetric and topographic environment were created to produce flood inundation maps from crewed and uncrewed aerial mapping systems. Using the observed flow data of the September 2024 storm, resulting flood models prove the inclusion of bathymetry can produce reliable flood models with depths of greater than 6 m modelled. Due to the lower flying altitude and tactical flight paths of ULSs, it is possible to identify regions occluded by vegetation that would otherwise be overlooked by crewed Airborne Laser Scanning methods to produce more reliable flood models.

The determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work.

As the demand for satellite imagery increases, efficient data processing at the sensor level is essential to overcome bandwidth and latency limitations. This study investigates the use of AI-driven atmospheric cloud segmentation directly onboard satellites to optimise sensor data utilisation, reduce transmission loads, and enable autonomy in Earth observation missions. Various deep learning models were tested on embedded hardware to assess their feasibility under power and computational constraints. While some models achieved high accuracy, their resource demands made them impractical for real-time deployment. A lightweight approach, particularly using optimized versions of CloudNet, demonstrated an effective balance between efficiency and performance. The research highlights the impact of model selection and optimization techniques in enabling real-time cloud detection on small / cube satellites, offering insights into practical onboard AI implementation and providing meaningful guidance on selecting models best suited for different satellite applications based on hardware limitations.

Space is one of the UK’s fastest-growing industry sectors of the last decade. Recognising this, in 2021 the UK Government’s first National Space Strategy established a new vision to make the nation one of the most innovative and attractive worldwide space economies. As part of the strategic funding programme, in 2023, the UK Space Agency (UKSA) funded a scoping study to assess the potential of satellite data to address issues that the three North East England Local Resilience Forums face at all stages of implementing the UK’s Integrated Emergency Management Framework (IEMF). Through dedicated workshops convened by two North East England universities, regional stakeholders from the emergency domain and related industries identified three case studies ripe for applying satellite data in support of multi-agency IEMF activities. Master’s students in the UK’s Centre for Doctoral Training in Geospatial Systems then undertook a month-long integrated group project to assess the potential for satellite imagery to be applied in the identified application areas. The research reported in this paper demonstrates how satellite imagery may be adopted to help address challenges posed during power outages, for mitigating illegal waste site fires, and during periods of snow and extreme cold. While the maturity levels of satellite applications vary in the three case studies due to data availability and image resolution, all three cases demonstrate that space data, particularly when augmented with additional geospatial information, help to enhance IEMF analysis. It is anticipated that the findings from the study will help stakeholders involved in IEMF management appreciate the added value of integrating satellite data into their current processes and analyses. By empowering key stakeholders to use satellite applications more effectively, it is predicted that better decisions will be achieved, thereby improving emergency risk management. A prototype dashboard, an output of the research to demonstrate the potential of space data for emergency management, is available online.

The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests.

The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.

Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.

Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing.

There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with feature-level fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms.

Several methods have been proposed for correcting the elevation bias in digital elevation models (DEMs) for example, linear regression. Nowadays, supervised machine learning enables the modelling of complex relationships between variables, and has been deployed by researchers in a variety of fields. In the existing literature, several studies have adopted either machine learning or statistical approaches in the task of DEM correction. However, to our knowledge, none of these studies have compared the performance of both approaches, especially with regard to open-access global DEMs. Our previous work has already shown the potential of machine learning approaches, specifically gradient boosted decision trees (GBDTs) for DEM correction. In this study, we share some results from the comparison of three recent implementations of gradient boosted decision trees (XGBoost, LightGBM and CatBoost), versus multiple linear regression (MLR) for enhancing the vertical accuracy of 30 m Copernicus and AW3D global DEMs in Cape Town, South Africa.

Urban flooding caused by heavy rainfall is a common natural hazard in cities globally. Impervious surfaces are often increased during urban development, but there is limited research on the impact of large-scale and long-term land use/land cover (LULC) changes on urban flooding, while considering the influence of using different remote sensing data sources. In this study, a framework to evaluate the correlation between LULC changes and flooding extents is proposed, mainly comprising: 1) classifying remote sensing time series, using different sources but adopting the same classifier, to obtain the LULC of the Greater Bay Area, China, over a one decade period; 2) designing flooding scenarios with different rainfall intensities, and using the soil conservation service curve number (SCS-CN) model and local equal volume method to extract the inundation extent of urban flooding; and 3) analyzing the influence of different data sources on flood simulation results, and calculating the correlations between LULC and inundation area over the decade to analyze the impact of LULC changes on urban flooding. The resulting correlation coefficients of water and built-up land are 0.93 and 0.42, and those of bare land, grassland, orchard, and forest are −0.40, −0.61, −0.57, and −0.75, respectively. The inundation derived by Sentinel and Landsat data showed around 99% consistency, while Landsat tends to derive more inundation areas, with the differences mainly scattered in flat areas.

The Leica GS18i visual positioning system, introduced in 2021, uses 3D imaging to measure points beyond the reach of conventional GNSS receivers, such as building facades. By leveraging RTK and PPP GNSS capabilities, it enables continuous image acquisition during surveys. This study assessed its 3D geometric accuracy in both open and complex environments with poor GNSS signals. Two field tests at Vindolanda Roman Fort and Newcastle University's Quadrangle Gateway served to evaluate processing results within the proprietary Leica Infinity and Agisoft Metashape. Results demonstrated 3D RMSEs of ca 3.5 cm in RTK mode without GCPs and 2.5 cm when all images are triangulated with a single GCP. The system achieved consistent cm-level accuracy and precision under challenging conditions with sufficient initial GNSS RTK image orientation. Comparative analyses with Canon DSLR datasets highlighted the GS18i system's efficiency, though software differences emerged.

The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage.

<p>Residents of Kabul, Afghanistan have historically relied on groundwater delivered from unconfined aquifers. However, recent drought events and population growth have resulted in withdrawing groundwater beyond sustainable rates and water level decrease. Groundwater overexploitation might have induced various magnitudes of ground subsidence, however, the issue had been overlooked.  In this study, we assessed the spatial and temporal evolution of ground deformation in Kabul and analysed the main governing processes.</p> <p>Deformation rates were extracted using InSAR SBAS time-series analysis of C-Band Sentinel-1 products from both ascending and descending orbits acquired from 2014 to 2019.  The analysis revealed Line-of-Sight (LOS) mean velocity accounting for -4.08 and -4.3 cm/year for ascending and descending tracks, respectively. High correlation coefficient of 0.75 and the low RMSE of 0.60 cm/year suggest that the two measurements are consistent. To overcome the inherent limitation of InSAR- measurements being made in LOS direction only, multi-geometry data fusion was employed by combining two independent LOS measurements to estimate vertical and horizontal components of displacement.  We delineated four subsidence bowls with highly variable spatial extent and deformation magnitudes in four aquifers present within the city boundaries, the largest of which is detected in Upper Kabul basin area with the vertical component reaching -5.3 cm/year and horizontal motion accounting for 1 cm/year.  </p> <p>The InSAR-derived subsidence time-series results were interpreted in relation to the spatio-temporal variations of hydrological and geotechnical conditions of the study area to identify the triggering factors of subsidence. The trend observed in the deformation time-series and groundwater level change shows a good agreement, suggesting that groundwater drawdown is largely a function of groundwater level change.  Wavelet analysis analysis allowed to distinguish long-term subsidence from recurring clay shrink-swell hazard in the region.  Furthermore, the susceptibility of the aquifers to consolidation was found to be largely caused by the prevalence of fine-grained sediments in the subsurface, such as clay and silt, thus differential consolidation in the area is a function of variation in thickness and types of the lithology of the base sediment.  </p> <p>In the context of climate change and increasing urban sprawl in Kabul, monitoring ground deformation using the satellite InSAR is an invaluable tool that provides new opportunities to inform not only subsidence hazard mitigation plans, but also approaches for sustainable management of groundwater resources and for improved planning of operations such as Managed Artificial Recharge (MAR) of aquifers.</p>

Canopy water mass is an important plant characteristic that can indicate the water status of vegetation. However, the parameter remains under-investigated because measuring it requires defoliating the canopy. This study introduced a non-destructive approach to estimate canopy water mass using terrestrial laser scanning data. Tree 3D models were generated from dual-wavelength TLS data for six forest canopies, then the models were utilized in estimating the canopy LAI, total leaf area, and vertical profiles of canopy leaf area. The estimates were then coupled with canopy equivalent water thickness estimates and vertical profiles of canopy water mass were generated. The results revealed some over- and underestimation in the estimated LAI, but the obtained accuracy was considered sufficient as leaf-on point clouds were used to generate the 3D models. The vertical profiles of canopy water mass showed that the leaf area distribution within the canopy, and the canopy architecture were the main parameters affecting the water mass distribution within the canopy, with mid canopy layers having higher water mass than the other canopy layers. This study showed the potential of TLS to estimate canopy water mass, but controlled experiments that include defoliating canopies are still needed for a direct and accurate validation of the TLS estimates of canopy water mass.

Comprehensive documentation is the foundation of effective conservation, repair and maintenance (CRM) practices for architectural heritage. In order to diagnose historic buildings and inform decision making, a combination of multi-disciplinary surveys is fundamental to understanding a building’s heritage and performance. Infrared thermography (IRT), a non-contact, non-invasive and non-destructive imaging technique, allows both qualitative and quantitative assessments of temperature to be undertaken. However, the inherent low spatial resolution of thermal imaging has led recent work to fuse thermographic and geometric data for the accurate 3D documentation of architectural heritage. This paper maps the scope of this emerging field to understand the application of IRT and 3D-data fusion (IRT-3DDF) for architectural heritage. A scoping review is undertaken to systematically map the current literature and determine research gaps and future trends. Results indicate that the increasing availability of thermal cameras and advances in photogrammetric software are enabling thermal models to be generated successfully for the diagnosis and holistic management of architectural heritage. In addition, it is evident that IRT-3DDF provides several opportunities for additional data integration, historic building information modelling (H-BIM) and temporal analysis of historic buildings. Future developments are needed to transform IRT-3DDF findings into actionable insights and to apply IRT-3DDF to pressing climate-related challenges, such as energy efficiency, retrofitting and thermal comfort assessments.

This paper presents Image2DEM, a framework for the semi-automated structural analysis of arches. Numerical models of arch specimens were semi-automatically developed from structure-from-motion (SfM) photogrammetry, by employing image-processing techniques, to bypass laborious computer-aided design (Cad)-based processes. Then, the numerical models of the proposed framework were assessed by comparison with a conventional Cad-based framework in terms of (a) geometrical agreement (i.e. joint and block properties) and (b) structural capacity agreement (i.e. stiffness, load multipliers and normal forces between joints at each hinge formation). The results firstly demonstrated SfM photogrammetry as an effective avenue of structural surveying for numerical modelling. Afterwards, the numerical models developed from the proposed and conventional frameworks were demonstrated to have good agreement in terms of geometry. Furthermore, good agreement was also found in the predicted structural capacity, with differences in structural capacity of less than 7%. Although only the potential to capture both the geometry and structural capacity of small-scale arch structures was demonstrated here, this study lays the foundation for automated geometry acquisition for arches for their structural analysis.

Extreme weather conditions can adversely impact transport networks and driver behaviour, leading to variations in traffic volumes and travel times and increased accident rates. Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making. However, real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research, we demonstrate how we obtain historical time-series and real-time data from sensors operated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne, UK. In the study, we assess the impact of rainfall on traffic volume and travel time, and the cascading impacts during a storm event in Newcastle during early October 2021. We also estimate the economic cost of the storm, with regards to transport disruption, as the cost of travel, using the “value of time” based on Department for Transport guidelines (2021).Using spatial-temporal analysis, we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm. We identified increases in travel times of up to 600% and decreases in traffic volume of up to 100% when compared to historical data. Further, we assessed cascading impacts at important traffic locations and their broader implications for city areas. We estimated that the storm's economic impact on one sensor location increased by up to 370% of the reference value.By analysing historical and real-time data, we detected and explained patterns in the data that would have remained uncovered if they had been examined individually. The combination of different data sources, such as traffic and weather, helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.