Evaluating U.S. Geological Survey Three-dimensional Elevation Program Lidar Data as a Source for Building Height

This paper provides a practical procedure for fusing LiDAR and photogrammetric point clouds for the extraction of tree metrics. Aerial photogrammetric point clouds are first generated using the structure-from-motion and dense-matching methods. Registration of the LiDAR and photogrammetric point clouds is then performed using an onboard global positioning system and inertial measurement unit. However, due to systematic deviations, it is impossible to directly merge the two types of point cloud. Therefore, an urban street network obtained from the OpenStreetMap digital mapping system is utilized for point cloud segmentation. After segmentation, each chunk is finely registered and merged based on the iterative closest point algorithm, allowing the two types of data to be accurately co-registered and a fused point cloud obtained. Finally, we conducted experiments to extract stand and individual tree metrics from fused point clouds created for two study plots. The height distributions of the fused point clouds were highly consistent with LiDAR data, with the 5%, 10%, 25%, 50%, 75%, 90%, and 95% height percentiles showing acceptable similarities. The height distribution of individual trees was also consistent with that of field measurements. Furthermore, the fused point clouds contain a high point density and RGB color information, which allow shape delineation and estimation of tree health status. This comprehensive analysis demonstrates that this procedure provides a practical way to inventory tree stands and individuals in urban areas.

Recent urban earthquakes and rapid urbanization have intensified the demand for fine-scale building exposure information in disaster risk assessment. However, existing approaches for high-resolution building exposure extraction often suffer from limited data completeness, insufficient semantic detail, and weak update capability, particularly at detailed spatial scales. Moreover, traditional methods relying on homogeneous data sources and static classifications struggle to represent the heterogeneity of urban building exposure.To address these limitations, we propose a multi-source data-driven framework combined with machine learning to extract high-resolution building exposure information, focusing on building function and building height. Building function types are inferred by integrating OpenStreetMap building footprints with time-series mobile signaling data, exploiting differences in population activity patterns across day-night and workday-non-workday periods. Machine learning techniques are then applied to identify clusters of buildings with similar population dynamic characteristics, enabling the inference of building function types. Building height is extracted from bi-temporal Sentinel-2 imagery by capturing variations in image brightness induced by seasonal differences in building shadow length, and a random forest model is employed to learn the nonlinear relationship between image features and building height, thereby reducing reliance on very high-resolution imagery and manual interpretation.Case studies in representative Chinese cities indicate that the integration of multi-source data and machine learning enables more effective use of data for different building exposure attributes, resulting in improvements in spatial detail, attribute completeness, and data timeliness. Population-dynamic-based building function identification provides an activity-oriented characterization of building use, while building height estimation based on freely available Sentinel-2 imagery offers a cost-efficient and scalable approach. Overall, these findings suggest that multi-source data integration and machine learning can support large-scale, high-resolution urban building exposure mapping.

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

High-resolution 3D mapping of subterranean environments remains challenging due to their complex geometry, low-light conditions, and restricted accessibility. Among these environments, caves represent particularly demanding settings where detailed spatial documentation is essential for monitoring processes, supporting exploration and conservation efforts. Laser scanning has become a key technique for capturing accurate and detailed 3D representations of caves that form the basis for this heritage documentation and multidisciplinary research. Despite these advances, the creation of cave maps still commonly relies on traverse-line measurements and field sketches, later digitized using specialized cave-surveying software. In recent years, LiDAR data have been used for deriving the cave extent. While this method effectively captures the general geometry of cave passages, the delineation of cave-floor units, sediments, speleothems, rock blocks, and other features remains largely manual and relies heavily on the surveyor’s interpretation. As a result, feature boundaries vary between authors, and detailed cave-surface representation lacks reproducibility that is problematic for long-term documentation. In this study, we explore the use of deep-learning semantic segmentation for classifying selected cave-floor surface types based on geometric features derived from LiDAR data. Building on previous work focused on semi-automatic cave-map generation from LiDAR point clouds, we extend the workflow from deriving cave extent and floor morphology toward the automated interpretation of surface materials and forms. The method was tested on several common cave-floor surface types, including clastic sediments, flowstone, and bedrock, as well as artificial surfaces and objects typical in showcaves. The resulting classifications show that deep-learning models can distinguish surfaces with subtle geometric differences and produce consistent, reproducible delineations of units that are traditionally mapped by hand. Compared with manual digitization, the approach reduces subjectivity and provides a scalable way to generate polygonal layers used in speleocartographic workflows.

We introduce University of Texas - GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 city or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset’s utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

The accuracy of lidar remote sensing in characterizing three-dimensional forest structural attributes has encouraged foresters to integrate lidar approaches in routine inventories. However, lidar point density is an important consideration when assessing forest biophysical parameters, given the direct relationship between higher spatial resolution and lidar acquisition and processing costs. The aim of this study was to investigate the effect of point density on mean and dominant tree height estimates at plot level. The study was conducted in an intensively managed Eucalyptus grandis plantation. High point density (eight points/m2) discrete-return, small-footprint lidar data were used to generate point density simulations averaging 0.25, one, two, three, four, five, and six points/m2. Field surveyed plot-level mean and dominant heights were regressed against metrics derived from lidar data at each simulated point density. Stepwise regression was used to identify which lidar metrics produced the best models. Mean height was estimated at accuracy of R2 ranging between 0.93 and 0.94 while dominant height was estimated with an R2 of 0.95. Root mean square error (RMSE) was also similar at all densities for mean height (∼1.0 m) and dominant height (∼1.2 m); the relative RMSE compared to field-measured mean was constant at approximately 5%. Analysis of bias showed that the estimation of both variables did not vary with density. The results indicated that all lidar point densities resulted in reliable models. It was concluded that plot-level height can be estimated with reliable accuracy using relatively low density lidar point spacing. Additional research is required to investigate the effect of low point density on estimation of other forest biophysical attributes.

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

Unmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Rapid advances in miniature sensor technology are leading to greater potentials for ecological research. We demonstrate one of the first applications of UAV lidar and hyperspectral imagery and a fusion method for individual plant species identification and 3D characterization at submeter scales in south‐eastern Arizona, USA. The UAV lidar scanner characterized the individual vegetation canopy structure and bare ground elevation, whereas the hyperspectral sensor provided species‐specific spectral signatures for the dominant and target species at our study area in leaf‐on condition. We hypothesized that the fusion of the two different data sources would perform better than either data type alone in the arid and semi‐arid ecosystems with sparse vegetation. The fusion approach provides 84–89% overall accuracy (kappa values of 0.80–0.86) in target species classification at the canopy scale, leveraging a wide range of target spectral responses in the hyperspectral data and a high point density (50 points/m2) in the lidar data. In comparison, the hyperspectral image classification alone produced 72–76% overall accuracies (kappa values of 0.70 and 0.71). The UAV lidar‐derived digital elevation model (DEM) is also strongly correlated with manned airborne lidar‐derived DEM (R2 = 0.98 and 0.96), but was obtained at a lower cost. The lidar and hyperspectral data as well as the fusion method demonstrated here can be widely applied across a gradient of vegetation and topography to monitor and detect ecological changes at a local scale.

Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data

Three-dimensional (3D) building structures are vital to understanding urban dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale building height retrieval when compared to LiDAR data and multi-view imagery. Existing methods learn building footprints and height maps per pixel via either a multi-task network or two separate networks, however, failing to consider the information of neighboring pixels that belong to the identical building. Therefore, we propose learning a novel representation for 3D buildings, namely 3D centripetal shifts, a unified representation of individual building instances. Our method is termed as 3DCentripetalNet and learns the 3D centripetal shift representation that incorporates planar and vertical structures of buildings. Afterward, a decoupling module is devised to learn building corner points. Finally, a 3D modeling module is designed to retrieve building height from the learned 3D centripetal shift map and corner points. We investigate the proposed 3DCentripetalNet on two datasets with different spatial resolutions, i.e., the ISPRS Vaihingen dataset (9 cm/pixel) and the Urban 3D dataset (50 cm/pixel). Experimental results suggest that 3DCentripetalNet is able to preserve sharp building boundaries, largely alleviate false detections, and significantly outperform other competitors. Thus, we believe that 3DCentripetalNet is a robust solution for the task of building height retrieval from monocular imagery.

Owing to the remarkable improvements that have been made in radar sensors, it becomes possible to obtain the information for a single structure from high-resolution SAR images. In our previous research, a method for detecting the heights of low-rise buildings automatically was proposed using 2D GIS data and a single high-resolution TerraSAR-X intensity image. However, this method is difficult to apply for high-rise buildings due to surface/material conditions of their exterior walls. In this study, a new method was developed for estimating the heights of high-rise buildings based on the results from an Interferometric SAR (InSAR) analysis. The characteristics of phases in the InSAR result were investigated and used for extracting the potential layover areas of target buildings. Then, the heights were estimated according to their layover lengths. The developed method was tested on two TerraSAR-X images for San Francisco, USA, in the HighSpot mode. Comparing the result with Lidar data, the detected heights were found to be reasonable.

Accurate and automatic extraction of building boundary from LiDAR data has been an active research topic in photogrammetry, computer vision and remote sensing. Boundary detection from LiDAR data consists of four steps: LiDAR data processing, Initial boundary detection, Boundary tracing, and Boundary regularization. Following these steps, the initial boundary is determined from LiDAR data of building. Techniques such as boundary tracing and regularization are also applied to generate the building boundary. Here a novel approach is introduced for automatically obtaining the high-rise building boundaries from LiDAR data accurately, with individual plane detection. Then, the computed boundaries are projected on the corresponding images to check its correctness and have compared the result with the boundary obtained from the images. The evaluation results show that this approach effectively detects the boundary.

Existing research suggests that LIDAR data is best suited for providing highway designers with general terrain information early in the location process in order to identify final corridors where more intensive photogrammetric work can be performed. The LIDAR points cloud is adopted to produce the digital terrain models (DTM) and digital orthophoto map (DOM) to aid the professional designers' work. In this paper, we propose a novel direct approach for highway design and visualization based on LIDAR point cloud. This approach omits the procedure of DTM and DOM, and automatically generates the 3D profile and cross sections by using LIDAR points cloud only. The visualization of highway corridors is also quickly achieved by triangulation and texture mapping of the LIDAR points cloud.

In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters.

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability.