Search Results (399)

Lava tubes on Earth provide unique hydrogeological niches for life to proliferate. Orbital observations of the Martian surface indicate the presence of lava tubes, which could hold the potential for extant life or the preservation of past life within a subsurface environment protected from harsh conditions or weathering at the surface. Secondary minerals in lava tubes form as a combination of abiotic and biotic processes. Microbes colonize the surfaces rich in these secondary minerals, and their actions induce further alteration of the mineral deposits and host basalts. We conducted a biogeochemical investigation of basaltic lava tubes in the Medicine Lake region of northern California by characterizing the compositional variations in secondary minerals, organic compounds, microbial communities, and the host rocks to better understand how their biogeochemical signatures could indicate habitability. We used methods applicable to landed Mars missions, including Raman spectroscopy, X-ray diffraction (XRD), Laser-Induced Breakdown Spectroscopy (LIBS), and gas chromatography–mass spectrometry (GC-MS), along with scanning electron microscopy (SEM) and metagenomic DNA/RNA sequencing. The main secondary minerals, amorphous silicates, and calcite, formed abiotically from the cave waters. Two types of gypsum, large euhedral grains with halites, and cryptocrystalline masses near microbial material, were observed in our samples, indicating different formation pathways. The cryptocrystalline gypsum, along with clay minerals, was associated with microbial materials and biomolecular signatures among weathered primary basalt minerals, suggesting that their formation was related to biologic processes. Some of the genes and pathways observed indicated a mix of metabolisms, including those involved in sulfur and nitrogen cycling. The spatial relationships of microbial material, Cu-enriched hematite in the host basalts, and genetic signatures indicative of metal cycling also pointed to localized Fe oxidation and mobilization of Cu by the microbial communities. Collectively these results affirm the availability of bio-essential elements supporting diverse microbial populations on lava tube basalts. Further work exploring these relationships in lava tubes is needed to unravel the intertwined nature of abiotic and biotic interactions and how that affects habitability in these environments on Earth and the potential for life on Mars. Full article

Pedestrian accessibility is a critical dimension of sustainable and inclusive transportation systems, yet many cities lack reliable data on infrastructure features that support visually impaired users. Among these, podotactile paving plays a vital role in guiding movement and ensuring safety at intersections and transit nodes. However, tactile paving networks remain largely absent from digital transport inventories and automated mapping pipelines, limiting the ability of cities to systematically assess accessibility conditions. This paper presents a scalable approach for identifying and mapping podotactile areas from mobile and handheld laser scanning data, broadening the scope of data-driven urban modelling to include infrastructure elements critical for visually impaired pedestrians. The framework is evaluated across multiple sensing modalities and geographic contexts, demonstrating robust generalization to diverse transport environments. Across four dataset configurations from Madrid and Mantova, the proposed DeepLabV3+ model achieved podotactile F1-scores ranging from 0.83 to 0.91, with corresponding IoUs between 0.71 and 0.83. The combined Madrid–Mantova dataset reached an F1-score of 0.86 and an IoU of 0.75, highlighting strong cross-city generalization. By addressing a long-standing gap in transportation accessibility research, this study demonstrates that podotactile paving can be systematically extracted and integrated into transport datasets. The proposed approach supports scalable accessibility auditing, enhances digital transport models, and provides planners with actionable data to advance inclusive and equitable mobility. Full article

In advanced manufacturing, accurate and reliable 3D geometry measurement is vital for the quality control of large-sized components with multiple small key local features. To obtain both the geometric form and spatial position of these local features, a hybrid robot-assisted laser scanning strategy is introduced, combining a laser tracker, a fringe-projection 3D scanner, and a mobile robotic unit that integrates an industrial robot with an Automated Guided Vehicle. As for improving the overall measurement accuracy, we propose an accuracy-enhanced calibration method that incorporates both error control and compensation strategies. Firstly, an accurate extrinsic parameter calibration method is proposed, which integrates robust target sphere center estimation with distance-constrained-based optimization of local common point coordinates. Subsequently, to construct a high-accuracy, large-scale spatial measurement field, an improved global calibration method is proposed, incorporating coordinate optimization and a hierarchical strategy for error control. Finally, a robot-assisted laser scanning hybrid measurement system is developed, followed by calibration and validation experiments to verify its performance. Experiments verify its high precision over 14 m (maximum error: 0.117 mm; mean: 0.112 mm) and its strong applicability in large-scale scanning of key geometric features, providing reliable data for quality manufacturing of large-scale components. Full article

Quarries represent critical sites for raw material extraction, for which regular monitoring and mine surveying documentation, along with its updating, is essential to ensuring safety, environmental protection, and effective management of the mining process. This article aims to evaluate the modern approach to quarry surveying and the creation of a base mining map using advanced laser scanning methods, such as terrestrial laser scanning (TLS) and simultaneous localization and mapping (SLAM)-based mobile laser scanning (MLS). Particular attention is given to the analysis of noise generated using TLS and SLAM-based MLS methods. An analysis of mutual differences between point clouds is presented to compare the spatial accuracy of the point clouds obtained using MLS technology against those from the reference TLS method on both horizontally and vertically oriented test areas. To assess the quality and usability of data obtained using the TLS and MLS methods, a selected section of the mining wall was analyzed based on the distance between points (Cloud-to-Cloud analysis), cross-section analysis, and volume calculations based on 3D mesh models generated from stage edges and point clouds. The findings offer valuable insights into the effective use of each method in quarry surveying, contributing to the development of innovative approaches to spatial data collection as a base for creating Digital Twins of quarries. The article also evaluates the efficiency of both measurement approaches in terms of accuracy, measurement speed, and practical applicability in mining practices. The results show that the point cloud obtained by the TLS Leica RTC360 device, compared to that by the MLS method using the FARO Orbis device (FARO Technologies, Inc., Lakemary, FL, USA), achieves better values in terms of average noise level, standard deviation, interval of highest point density, and RMSD (Root Mean Square Deviation) in test areas. Our conclusions highlight the high potential of laser scanning for the modernization of mining documentation and the improvement of surveying processes in the smart mining industry, particularly for updating Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and other 3D models of quarries for the creation of their Digital Twins. Full article

Accurate and reliable extraction of building footprints from LiDAR point clouds is a fundamental task in remote sensing and urban scene reconstruction. Building footprints serve as essential geospatial products that support GIS database updating, land-use monitoring, disaster management, and digital twin development. Traditional image-based methods enable large-scale mapping but suffer from 2D perspective limitations and radiometric distortions, while airborne or vehicle-borne LiDAR systems often face single-viewpoint constraints that lead to incomplete or fragmented footprints. Recently, backpack mobile laser scanning (MLS) has emerged as a flexible platform for capturing dense urban geometry at the pedestrian level. However, the high noise, point sparsity, and structural complexity of MLS data make reliable footprints delineation particularly challenging. To address these issues, this study proposes a Deep Line-Segment Detection–Driven Building Footprints Extraction Framework that integrates multi-layer accumulated occupancy mapping, deep geometric feature learning, and structure-aware regularization. The accumulated occupancy maps aggregate stable wall features from multiple height slices to enhance contour continuity and suppress random noise. A deep line-segment detector is then employed to extract robust geometric cues from noisy projections, achieving accurate edge localization and reduced false responses. Finally, a structural chain-based completion and redundancy filtering strategy repairs fragmented contours and removes spurious lines, ensuring coherent and topologically consistent footprints reconstruction. Extensive experiments conducted on two campus scenes containing 102 buildings demonstrate that the proposed method achieves superior performance with an average Precision of 95.7%, Recall of 92.2%, F1-score of 93.9%, and IoU of 88.6%, outperforming existing baseline approaches by 4.5–7.8% in F1-score. These results highlight the strong potential of backpack LiDAR point clouds, when combined with deep line-segment detection and structural reasoning, to complement traditional remote sensing imagery and provide a reliable pathway for large-scale urban scene reconstruction and geospatial interpretation. Full article

The Building Information Model (BIM) has been increasingly adopted for building maintenance and management. For existing buildings lacking prior digital models, a BIM is often generated from 3D scanned point clouds. In recent years, the automatic construction of simple BIMs comprising major structural elements, such as floors, walls, ceilings, and columns, has become feasible. However, the automated generation of detailed BIMs that incorporate building equipment, such as electrical installations and safety systems, remains a significant challenge, despite their essential role in facility maintenance. This process not only enriches the information content of the BIM but also provides a foundation for evaluating building safety and hazard levels, as well as for supporting evacuation planning and disaster-preparedness simulations. Such equipment is typically attached to ceilings or walls and is difficult to detect due to its small surface area and thin geometric profile. This paper proposes a method for detecting building equipment based on laser reflection intensity, with the objective of facilitating the automatic construction of detailed BIMs from point clouds acquired by mobile laser scanners (MLSs). The proposed approach first corrects the reflection intensity by eliminating the effects of distance and incidence angle using polynomial approximation, thereby normalizing the intensity values for surfaces composed of identical materials. Given that the corrected intensity approximately follows a normal distribution, outliers are extracted as candidate points for building equipment via thresholding. Subsequently, the point cloud is converted into a 2D image representation, and equipment regions are extracted using morphological operations and connected component labeling. Experiments conducted on point clouds of building ceilings and walls demonstrate that the proposed method achieves a high detection accuracy for various types of building equipment. Full article

The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning due to canopy occlusion, making integration challenging. Due to the variations in observation platforms, ULS and MLS point clouds exhibit significant structural discrepancies and limited overlapping areas, necessitating effective methods for feature extraction and correspondence establishment between these features to achieve high-precision registration and integration. Therefore, we propose a registration algorithm that introduces a Feature Deviation Metric to enable feature extraction and correspondence construction for forest point clouds in complex regional environments. The algorithm first extracts surface point clouds using the hidden point algorithm. Then, it applies the proposed dual-threshold method to cluster individual tree features in ULS, using cylindrical detection to construct a Feature Deviation Metric from the feature points and surface point clouds. Finally, an optimization algorithm is employed to match the optimal Feature Deviation Metric for registration. Experiments were conducted in 8 stratified mixed tropical rainforest plots with complex mixed-species canopies in Malaysia and 6 structurally simple, high-canopy-density pure forest plots in anorthern China. Our algorithm achieved an average RMSE of 0.17 m in eight tropical rainforest plots with an average canopy density of 0.93, and an RMSE of 0.05 m in six northern forest plots in China with an average canopy density of 0.75, demonstrating high registration capability. Additionally, we also conducted comparative and adaptability analyses, and the results indicate that the proposed model exhibits high accuracy, efficiency, and stability in high-canopy-density forest areas. Moreover, it shows promise for high-precision ULS-MLS registration in a wider range of forest types in the future. Full article

Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments. Full article

Accurate measurement of tree height and diameter at breast height (DBH) is essential for forest inventory, biomass estimation, and habitat assessment but remains challenging in structurally complex, multi-layered forests. This study evaluates the accuracy and operational feasibility of Unmanned Aerial Vehicle (UAV) LiDAR and Mobile Laser Scanning (MLS) for estimating tree height and DBH in such stands with a diverse structure in the Romanian Carpathians. Field measurements from six plots encompassing mixed-species (Fagus sylvatica L., Abies alba Mill., Picea abies (L.) H.Karst.) and single-species (Picea abies) stands were compared against UAV- and MLS-derived metrics. MLS delivered near-inventory-grade DBH accuracy across all species (R² up to 0.98) and reliable height estimates for intermediate and suppressed trees, while UAV LiDAR consistently underestimated tree height, especially in dense, multi-layered stands (R² < 0.2 in mixed plots). Voxel-based occlusion analysis revealed that over 93% of area under canopy and interior crown volume was captured only by MLS, confirming its dominance below the canopy, whereas UAV LiDAR primarily delineated the outer canopy surface. Species traits influenced DBH accuracy locally, but structural complexity and canopy layering were the main drivers of height underestimation. We recommend hybrid UAV–MLS workflows combining UAV efficiency for canopy-scale mapping with MLS precision for stem and sub-canopy structure. Future research should explore multi-season acquisitions, improved SLAM robustness, and automated data fusion to enable scalable, multi-layer forest monitoring for carbon accounting, biodiversity assessment, and sustainable forest management decision making. Full article

Forest inventories are essential for monitoring and managing forest ecosystems, relying on accurate measurements of tree attributes such as tree detection, Diameter at Breast Height (DBH), and Tree Height (TH). Nowadays, advances in LiDAR technology have enabled increasingly effective and reliable solutions for 3D mapping and tree feature extraction. However, the performance of this method is strongly influenced by point cloud density, which can be limited for technological and/or economic reasons. This study therefore aims to investigate and quantify the effect of density on the accuracy of measured parameters. Starting from high-density datasets, these are progressively downsampled, and the extracted features are compared. Results indicate that DBH estimation requires densities of 600–700 points/m³ for errors below 1 cm (5% RMSE), while accurate tree height estimation (RMSE < 1 m—5% error) can be achieved with densities exceeding 300 points/m³. These findings provide guidance for balancing measurement accuracy and operational efficiency in automated forest surveys using laser scanner technology. Full article

The automatic detection and accurate geolocation of key railway objects plays a crucial role in the mapping, monitoring and management of railway infrastructure. This study presents a novel approach for the identification and geolocation of key railway elements through point cloud analysis. The methodology relies on high-density LiDAR point clouds acquired along railway lines using a mobile laser-scanning system operating in the infrared (IR). This research contributes to the advancement of railway mapping and monitoring technologies by providing an innovative solution that can be integrated into railway infrastructure management software. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Given its considerable cultural, historical, and economic value, built heritage requires the application of modern techniques for effective documentation and conservation. While multiple sensors are available for 3D modeling, laser scanning remains the most commonly employed due to its efficiency, precision, and ability to comprehensively capture the building’s geometry, surface textures, and structural details. This results in highly detailed 3D representations that are very important for accurate documentation, analysis, and conservation planning. This study investigates the complementary potential of different 3D modeling approaches for the digital representation of the Dosoftei House in Iasi, a monument of historical significance. For this purpose, an integrated point cloud was created based on a mobile hand-held laser scanner (HMLS), i.e., the FJD Trion P1 and a terrestrial laser scanner (TLS), i.e., the Maptek I-Site 8820 long-range laser scanner, the latter specifically used to capture the roof structures. Based on this dataset, a parametric model was created in Revit, supported by panoramic images, allowing for a structured representation useful in technical documentation and heritage management. In parallel, a mesh model was generated in CloudCompare using Poisson surface reconstruction. The comparison of the two methods highlights the high geometric accuracy of the mesh model and the Building Information Modeling (BIM) model’s capability to efficiently manage information linked to architectural elements. While the mesh provides detailed geometry, the BIM model excels in information organization and supports informed decision-making in conservation efforts. This research proposes leveraging the advantages of both methods within an integrated workflow, applicable on a larger scale in architectural heritage conservation projects. Full article

Digital twins play a crucial role in linking data with practical solutions. They convert raw measurements into actionable insights, enabling spatial planning that addresses environmental challenges and meets the needs of local communities. This paper presents the development of a digital geospatial twin for a residential district in Burgas, the largest port city on Bulgaria’s southern Black Sea coast. The aim is to provide up-to-date geospatial data quickly and efficiently, and to merge available data into a single, accurate model. This model is used to test three scenarios for revitalizing coastal functions and improving a waterfront urban park in collaboration with stakeholders. The methodology combines aerial photogrammetry, ground-based mobile laser scanning (MLS), and airborne laser scanning (ALS), allowing for robust 3D modeling and terrain reconstruction across different land cover conditions. The current topography, areas at risk from geological hazards, and the vegetation structure with detailed attribute data for each tree are analyzed. These data are used to evaluate the strengths and limitations of the site concerning the desired functionality of the waterfront, considering urban priorities, community needs, and the necessity of addressing contemporary climate challenges. The carbon storage potential under various development scenarios is assessed. Through effective visualization and communication with residents and professional stakeholders, collaborative development processes have been facilitated through a series of workshops focused on coastal transformation. The results aim to support the design of climate-neutral urban solutions that mitigate natural risks without compromising the area’s essential functions, such as residential living and recreation. Full article

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R², RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure. Full article