Search Results (724)

This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R² = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R² = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R² = 0.90–0.94, RMSE = 0.10–0.18 m³, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments. Full article

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete representation framework. However, these approaches typically rely on manually designed regularization terms, which fail to accurately capture the complex geostructural patterns in remote sensing imagery. In response to this issue, we develop a continuous blind cloud removal model. Specifically, the cloud-free component is represented using a continuous tensor function that integrates implicit neural representations with low-rank tensor decomposition. This representation enables the model to capture both global correlations and local smoothness. Furthermore, a band-wise sparsity constraint is employed to represent the cloud component. To preserve the information in regions not covered by clouds during reconstruction, a box constraint is incorporated. In this constraint, cloud detection is performed using an adaptive thresholding strategy, and a morphological erosion function is employed to ensure accurate detection of cloud boundaries. To efficiently handle the developed model, we formulate an alternating minimization algorithm that decouples the optimization into three interpretable subproblems: cloud-free reconstruction, cloud component estimation, and cloud detection. Our extensive evaluations on both synthetic and real-world data reveal that the proposed method performs competitively against state-of-the-art cloud removal methods. Full article

The precise monitoring of solar flares holds significant scientific value for space mission safety, communication security, and space environment forecasting. The H$\alpha$ line has long been utilized as a tool to extract information about the structure and dynamics of the solar chromosphere and is crucial for observing solar activities such as prominences and flares. However, ground-based H$\alpha$ observations are susceptible to cloud interference, which significantly reduces data reliability and complicates the effective separation of genuine flare signals from cloud modulation effects. To address this challenge, our study proposes a dual-band brightness ratio method tailored to the SMAT configuration, leveraging synchronous observation data from the Huairou SMAT at two wavelengths (photospheric 5324 Å and chromospheric 6562.8 Å). Observational data validation demonstrates that this method can effectively characterize true chromospheric brightness variations. In real observational data, the reconstructed brightness curve successfully recovered the brightness peak of an M1.5 class flare, with the peak position aligning well with the X-ray flux peak. This method enhances the accuracy of flare monitoring under cloudy conditions for SMAT, providing a promising pathway for high-reliability ground-based solar activity observations with this telescope. Full article

Scanned data often contain substantial outliers due to environmental interference, which drastically decreases the performance of traditional registration algorithms. To address this issue, this article proposes an improved principal component analysis (PCA) and Jacobian-enhanced whale optimization collaborative method for point cloud registration. First, an improved PCA point cloud initial registration algorithm is proposed by introducing the normal vector local information to set the screening conditions. This algorithm can streamline the original set of 48 candidate rotation matrices down to 4, achieving rapid point cloud registration at the data level between the scanned and model point clouds. Second, a Jacobian whale optimization algorithm for fine registration (JWOA-FR) is proposed by incorporating local gradient information. The algorithm employs gradient descent on optimal whale individuals to dynamically guide global search updates, thereby enhancing both registration accuracy and efficiency. Finally, a threshold is set to remove the outliers contained in the workpieces based on the information of the matched point pairs. The iterative closest point (ICP) algorithm is further used to improve registration accuracy for data without outliers. The experimental results showed that registration errors of large workpieces 1, 2, and 3 were 2.0755 mm, 2.3955 mm, and 2.5823 mm, respectively, after outlier removal, which indicates that the proposed method is applicable to data with outliers, and the registration accuracy meets the requirements. Full article

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe. Full article

This paper proposes a structure-aware compression technique for efficient compression of high-resolution synthetic aperture radar (SAR)-based point clouds by quantitatively analyzing the directional characteristics of local structures. The proposed method computes the angular difference between the principal eigenvector of each point and those of its neighboring points, selectively removing points with low contribution to directional preservation and retaining only structurally significant feature points. The method demonstrates superior information preservation performance through various compression evaluation metrics such as entropy, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Additionally, the SHREC’19 human mesh dataset is employed to further assess the generality and robustness of the proposed approach. The results show that the proposed method can maximize data efficiency while preserving the core information of the point cloud through a novel directionality-based structural preservation strategy. Full article

This study synthesizes the rapidly expanding evidence on how digital technologies reshape international trade, with a particular focus on small and medium-sized enterprises (SMEs). Guided by two research questions—(RQ1) How do digital tools influence the volume and composition of cross-border trade? and (RQ2) How do these effects vary by countries’ development level and firm size?—we conducted a PRISMA-compliant systematic literature review covering 2010–2024. Searches across eight major databases yielded 1857 records; after duplicate removal, title/abstract screening, full-text assessment, and Mixed Methods Appraisal Tool (MMAT 2018) quality checks, 86 peer-reviewed English-language studies were retained. Findings reveal three dominant technology clusters: (1) e-commerce platforms and cloud services, (2) IoT-enabled supply chain solutions, and (3) emerging AI analytics. E-commerce and cloud adoption consistently raise export intensity—doubling it for digitally mature SMEs—while AI applications are the fastest-growing research strand, particularly in East Asia and Northern Europe. However, benefits are uneven: firms in low-infrastructure settings face higher fixed digital costs, and cybersecurity and regulatory fragmentation remain pervasive obstacles. By integrating trade economics with development and SME internationalization studies, this review offers the first holistic framework that links national digital infrastructure and policy support to firm-level export performance. It shows that the trade-enhancing effects of digitalization are contingent on robust broadband penetration, affordable cloud access, and harmonized data-governance regimes. Policymakers should, therefore, prioritize inclusive digital-readiness programs, while business leaders should invest in complementary capabilities—data analytics, cyber-risk management, and cross-border e-logistics—to fully capture digital trade gains. This balanced perspective advances theory and practice on building resilient, equitable digital trade ecosystems. Full article

The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union (mIoU) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error (RMSE) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using the DJI Matrice 300 RTK equipped with a Zenmuse L2 sensor, enabling high-density point cloud generation (98 points/m²). The point clouds were processed to remove non-ground points and generate a 0.25 m resolution DEM using TIN interpolation. A total of seven dolines were detected and delineated, and their morphometric characteristics—including area, perimeter, major and minor axes, and elevation—were analyzed. These results were compared with a 1:5000-scale DEM derived from the 2013 National Basic Map. Visual and numerical comparisons highlighted significant improvements in spatial resolution and feature delineation using UAV LiDAR. Although the 1:5000-scale DEM enables general doline detection, UAV LiDAR facilitates more precise boundary extraction and morphometric analysis. The study demonstrates the effectiveness of UAV LiDAR for detailed topographic mapping in complex karst terrains and offers a foundation for future automated classification and temporal change analysis. Full article

Because neural networks pervade many industrial domains and are increasingly complex and accurate, the trained models themselves have become valuable intellectual properties. Developing highly accurate models demands increasingly higher investments of time, capital, and expertise. Many of these models are commonly deployed in cloud services and on resource-constrained edge devices. Consequently, safeguarding them is critically important. Neural network watermarking offers a practical solution to address this need by embedding a unique signature, either as a hidden bit-string or as a distinctive response to specially crafted “trigger” inputs. This allows owners to subsequently prove model ownership even if an adversary attempts to remove the watermark through attacks. In this manuscript, we adapt three state-of-the-art watermarking methods to “tiny” neural networks deployed on edge platforms by exploiting symmetry-related properties that ensure robustness and efficiency. In the context of machine learning, “tiny” is broadly used as a term referring to artificial intelligence techniques deployed in low-energy systems in the mW range and below, e.g., sensors and microcontrollers. We evaluate the robustness of the selected techniques by simulating attacks aimed at erasing the watermark while preserving the model’s original performances. The results before and after attacks demonstrate the effectiveness of these watermarking schemes in protecting neural network intellectual property without degrading the original accuracy. Full article

The current methods for removing thick clouds from remote-sensing images face significant limitations, including the integration of thick cloud images with synthetic aperture radar (SAR) ground information, the provision of meaningful guidance for SAR ground data, and the accurate reconstruction of textures in cloud-covered regions. To overcome these challenges, we introduce SAR-DeCR, a novel method for thick cloud removal in satellite remote-sensing images. SAR-DeCR utilizes a diffusion model combined with the transformer architecture to synthesize accurate texture details guided by SAR ground information. The method is structured into three distinct phases: coarse cloud removal (CCR), SAR-Fusion (SAR-F) and cloud-free diffusion (CF-D), aimed at enhancing the effectiveness of the thick cloud removal. In CCR, we significantly employ the transformer’s capability for long-range information interaction, which significantly strengthens the cloud removal process. In order to overcome the problem of missing ground information after cloud removal and ensure that the ground information produced is consistent with SAR data, we introduced SAR-F, a module designed to incorporate the rich ground information in synthetic aperture radar (SAR) into the output of CCR. Additionally, to achieve superior texture reconstruction, we introduce prior supervision based on the output of the coarse cloud removal, using a pre-trained visual-text diffusion model named cloud-free diffusion (CF-D). This diffusion model is encouraged to follow the visual prompts, thus producing a visually appealing, high-quality result. The effectiveness and superiority of SAR-DeCR are demonstrated through qualitative and quantitative experiments, comparing it with other state-of-the-art (SOTA) thick cloud removal methods on the large-scale SEN12MS-CR dataset. Full article

Optical remote sensing images suffer from information loss due to cloud interference, while Synthetic Aperture Radar (SAR), capable of all-weather and day–night imaging capabilities, provides crucial auxiliary data for cloud removal and reconstruction. However, existing cloud removal methods face the following key challenges: insufficient utilization of spatiotemporal information in multi-temporal data, and fusion challenges arising from fundamentally different imaging mechanisms between optical and SAR images. To address these challenges, a spatiotemporal feature interaction-based cloud removal method is proposed to effectively fuse SAR and optical images. Built upon a conditional generative adversarial network framework, the method incorporates three key modules: a multi-temporal spatiotemporal feature joint extraction module, a spatiotemporal information interaction module, and a spatiotemporal discriminator module. These components jointly establish a many-to-many spatiotemporal interactive learning network, which separately extracts and fuses spatiotemporal features from multi-temporal SAR–optical image pairs to generate temporally consistent, cloud-free image sequences. Experiments on both simulated and real datasets demonstrate the superior performance of the proposed method. Full article

In target detection tasks equipped with depth sensors, it is crucial to adopt the point cloud pretreatment process, which is directly related to the quality of the obtained three-dimensional model of the target. However, there are few methods that can be combined with common preprocessing methods to quickly process ToF camera output. In real-life experiments, the common method is to adopt multiple types of preprocessing methods and adjust parameters separately. We proposed PcBD, a method that integrates outlier removal, boundary detection, and smooth sliders. PcBD does not limit the number of input points, and can remove outliers and predict smooth projection boundaries at one time while ensuring that the total number of points remains unchanged. We also introduced Bound57, a benchmark dataset that contains point clouds with synthetic noise, outliers, and projected boundary labels. Experimental results show that PcBD performs significantly better than state-of-the-art methods in various de-noising and boundary detection tasks. Full article