Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B3-2026, pp. 589-594· 0 citations· 2 references
Abstract
Abstract. Reliable urban risk assessment requires accurate and up-to-date information on building characteristics, particularly construction year and height, which are often incomplete or unavailable in existing databases. This study presents a cloud-based methodology for the automatic estimation of these parameters using multispectral and very high-resolution Earth Observation (EO) data. The proposed approach integrates temporal analysis of multispectral satellite imagery (Sentinel-2 and Landsat) with photogrammetric processing of very high-resolution stereo imagery (Pléiades). Building construction year is estimated by detecting temporal changes in spectral indices using spline-based modeling and discrete-difference analysis, achieving an accuracy of better than ±3 years. Building height is derived from digital surface models generated from satellite stereo imagery, with a mean accuracy of less than 2 m relative to LiDAR reference data (~1.40 m). The methodology was implemented in a cloud computing environment (Google Earth Engine and Google Colab) and tested in the City of Zagreb, Croatia. Validation results show robust performance, with an F1-score of 0.819 for construction year estimation and strong agreement between EO-derived and LiDAR-based height values. The results demonstrate the potential of EO-based methods for scalable, reliable extraction of building information, thereby supporting improved urban risk assessment and decision-making.
Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery is cost-prohibitive or unavailable. While recent works have demonstrated building height estimation using freely available Sentinel imagery, the resolution ceiling of resulting products is still coarse for material stock analysis. This study incorporates products derived from data freely accessible under scientific research licenses, TerraSAR-X StripMap and PlanetScope, alongside Sentinel-1 to predict building heights in a large city in Brazil. To account for the spatial autocorrelation in the training set, features from all sources are integrated in a geographically weighted random forest model, returning an RMSE of 5.34 m and R2 of 0.756 against a LiDAR reference dataset. Local feature importance showed predictor dominance to vary consistently across intra-urban contexts, with footprint geometry dominating for low-rise buildings, shadow-derived height for taller and more isolated structures, and spectral reflectance for the tallest buildings in the set. Sentinel-1 backscatter and InSAR occupy complementary spatial niches, with no single sensor uniformly preferable across the set. Results provide optioneering guidance and insight over satellite-derived products predictive relevance in distinct contexts, which global machine learning or neural network models cannot offer.
Guilherme Iablonovski, P. Frison, T. D. da Silva· 0 citations
Abstract. Accurate mapping of urban tree canopy is essential for quantifying ecosystem services and assessing the impact of green infrastructure on wellbeing and public health. This study evaluates and compares three Geospatial Artificial Intelligence (GeoAI) frameworks for the automated detection and segmentation of tree cover. The frameworks are YOLO, Detectree, and TreeEyed Utilizing high-resolution aerial imagery (0.2 m and 0.5 m ground sampling distance), the research tests different deep-learning paradigms, including object detection and semantic segmentation. The results indicate that while object-based models like YOLO align closely with statistical baselines (30.83% vs 30.11%), pixel-based models such as Detectree may underestimate fragmented urban vegetation. The study highlights the effectiveness of the TreeEyed QGIS plugin for urban applications and emphasizes the necessity of local LiDAR-derived data for model validation. Further studies would benefit from ad-hoc training with correct co-registration and consistent coordinate reference systems across layers.
F. Pirotti, L. Avesani, Matteo Pianetti et al.· The International Archives o...· 0 citations
Abstract. This study presents a semi-automated Level of Detail (LoD) 2 building modelling and analysis framework, implemented using fully open-source geographic information system (GIS) software, for the high-accuracy identification of rooftop photovoltaic (PV) potential in smart city digital twins. The LoD 1 building model, traditionally based on two-dimensional building footprint data used in large-scale building modelling, systematically overestimates solar energy potential as it neglects roof pitch, orientation and shading from the immediate surroundings. In this study, analyses were conducted in a high-density urban area of Birmingham, UK, using 1-metre resolution aerial LiDAR point clouds provided by the UK Department for Environment, Food and Rural Affairs (DEFRA). Throughout the process, reliance on proprietary software was completely eliminated; point cloud pre-processing, building boundary extraction using DBSCAN clustering and Concave Hull algorithms, and orthogonalisation filters were carried out entirely within the QGIS environment. Dynamic solar radiation simulations were performed using the SAGA ‘Potential Incoming Solar Radiation’ algorithm. The findings of the study provide a scalable framework for sustainable urban planning by enhancing LoD of modelled buildings and laying the groundwork for the semantic enrichment of urban digital twins. Furthermore, it generates outputs that provide concrete support to end-users, thereby helping achieve global net-zero targets.
Muhammed Yahya Bıyık, M. Mete· The International Archives o...· 0 citations
Digital elevation models (DEMs) are essential for infrastructure design and flood modeling, yet publicly available DEMs typically exhibit vertical accuracies of one to three meters, insufficient for these applications. This study develops and systematically evaluates a constraint-based DEM enhancement approach that integrates sparse, high-accuracy light detection and ranging (LiDAR) reference data collected at bridge locations to improve regional DEM quality. The methodology employs nearest-neighbor spatial interpolation to generate elevation correction surfaces from constraint points (points of known accurate elevations), followed by Gaussian smoothing to preserve topographic continuity. The approach was evaluated using LiDAR data from 15 bridge locations in the Austin metropolitan area in Central Texas. Four spatial interpolation methods (nearest neighbor, inverse distance weighting, natural neighbor, and linear interpolation) were compared, with nearest neighbor achieving optimal performance with a 28.79% improvement in mean Root Mean Square Error (RMSE) within the study area. Gaussian filtering with an optimized smoothing parameter (
σ
=
0.3
m
) further enhanced accuracy, achieving a mean RMSE to 0.105 m. Spatial configuration analysis across six-, nine-, and 15-bridge configurations revealed critical dependencies: distributed constraint arrangements consistently achieved optimal accuracy with six to eight constraint locations, while clustered configurations (constraint points clustered inside the area of interest) with peripheral constraints exhibited performance degradation despite increasing constraint count. This performance resulted from nearest-neighbor interpolation’s reliance on geometric proximity without terrain similarity consideration. The findings provide practical guidance for transportation agencies and flood management programs seeking to leverage existing infrastructure-derived LiDAR surveys for regional DEM enhancement, demonstrating that strategic constraint placement throughout the area of interest is essential for maximizing performance.
Xin-Ke Huang, R. S. Wilkho, N. Gharaibeh· Journal of Surveying Enginee...· 0 citations
In developing countries, urbanization is rapidly growing. To manage such urbanization, identifying and tracking building construction to keep it up to date in all dimensions is the essential and challenging job of the government. The objective of this research work is to find the individual building height and identify the illegal buildings in the selected area located in the Godavari municipality of Kailali district, Nepal. For this research work, the latest airborne LiDAR and cadastral datasets were acquired from the Survey Department. It reduces the manual field survey effort, gives accurate results, and minimizes interference from the external environment. One of the most important dimensions of LiDAR data is the z-axis value, which gives the accurate height of the object. A normalized digital surface model is calculated with the help of DTM and DSM. By utilizing the normalized Digital Surface Model (nDSM), precise building heights were extracted, which facilitated the estimation of the total number of floors per structure. This derived floor count data was then cross-referenced with municipal records to identify discrepancies. By adopting this process, the municipality can systematically verify building heights and automate floor-area and floor height calculations to ensure alignment with local building codes, and perform more accurate property tax assessments.
Shib Raj Bhatt, Bhoj Raj Ghimire· Journal of Land Management a...· 0 citations
Abstract. With TDX-EDEM (EDEM), AW3D30, SRTM and ASTER GDEM-3 (GDEM) global or nearly global free elevation models are available, which are in several countries more accurate than the Digital Elevation Models (DEMs) of the national survey administrations. All these DEMs have a point spacing of 1 arcsecond, which corresponds to ~31 m at the equator. EDEM, based on the TanDEM-X satellite combination, is the latest one, available since 2024. All named DEMs were analysed against reference aerial LiDAR DEMs in four test areas – Mountain, Rolling, Flat and City to gain an overview of their characteristics and accuracy. All these DEMs exhibit systematic errors that depend on the type of data acquisition and the terrain slope. Using ICESat-2 elevation profiles, these systematic errors can be reduced, thus improving the DEMsTo exclude areas with high vegetation, built-up areas, and water surfaces, an exclusion layer is required. The major improvement by ICESat-2 can be reached through a Z-shift of the DEMs determined by comparing the DEMs with ICESat-2 data. Only in few cases a correction by tilt or higher order systematic errors was justified. The best results were achieved with EDEM; only in steep mountainous and in built-up areas, AW3D30, corrected by ICESat-2, has advantages. SRTM and GDEM DEMs are not as good and should no longer be used.
K. Jacobsen· The International Archives o...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
