- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations
摘要： Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN and AGB (CV-R2 = 0.95, performed well rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m?2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m?2) to 5% (8 pts·m?2).
关键词： Ginkgo,UAV,LiDAR,Forest structural attributes,Point cloud density,Planted forest
更新于2025-09-23 15:21:21
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Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach
摘要： For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.
关键词： Lidar,Powerlines,Voxel-based subsampling,Laser scanning,Point cloud
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of Three Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning Data
摘要： Terrestrial laser scanning (TLS) is widely used in forest inventory surveys. Diameter at breast height (DBH) is one of the most important parameters in the forest inventory survey. There are many methods to estimate DBH. In this study, cylinder fitting algorithm, circle fitting algorithm and Hough transform algorithm are used to estimate DBH of two larches of different ages to find a better DBH extraction algorithm. Compared with the circle fitting algorithm and Hough transform algorithm, the cylinder fitting algorithm achieves the highest accuracy. In addition, it is worth noting that different structure of the trees may affect the accuracy of these methods greatly.
关键词： Tree point cloud,Terrestrial laser scanning (TLS),Diameter at breast height (DBH)
更新于2025-09-23 15:21:01
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[IEEE 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) - Kathmandu, Nepal (2019.11.4-2019.11.6)] 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) - Human Tracking of Single Laser Range Finder Using Features Extracted by Deep Learning
摘要： Human recognition using single laser range finder (LRF) is utilized for the task of following a target person such as a cargo transport robot. In these recognition methods, the approach is applied in which human-crafted features is inputted to the one-class classification model to identify whether it is a human or not. In this paper, we propose a method that introduce features extracted by deep learning. In this method, we create an encoder that can extract features from input data using PointNet-based autoencoder. In its experiment, the features extracted by encoder is compared with the human-crafted features, and these extraction process length of time is measured.
关键词： One-Class Classification,Point Cloud,Deep Learning,Laser Range Finder
更新于2025-09-23 15:21:01
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Noise-robust transparent visualization of large-scale point clouds acquired by laser scanning
摘要： We propose a high-quality transparent visualization method suitable for large-scale laser-scanned point clouds. We call the method “stochastic point-based rendering (SPBR),” which is based on a novel stochastic algorithm. SPBR enables us to clearly observe the deep interior of laser-scanned 3D objects with the correct feeling of depth. The high quality of SPBR originates from the effect of “stochastic noise transparentization,” which is an effect to make the measurement noise transparent and invisible in the created images. We mathematically prove that this effect also makes the created transparent images coincide with the results of the conventional methods based on the alpha blending, which is time-consuming and impractical for large-scale laser-scanned point clouds. We also demonstrate the effectiveness of SPBR by applying it to modern buildings, cultural heritage objects, forests, and a factory. For all of the cases, the method works quite well, realizing clear and correct 3D see-through imaging of the laser-scanned objects.
关键词： High quality 3D see-through imaging,Laser-scanned point cloud,Large-scale data,Stochastic noise transparentization,Visualization
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds
摘要： The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal LiDAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m2) to improve the single tree parameter estimation of low density data (up to 5 pts/m2) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.
关键词： Point Cloud,Tree Crown Parameters,Remote Sensing,Multitemporal LiDAR Data,Data Fusion
更新于2025-09-23 15:21:01
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Digital Modeling and Display of Ancient Architecture Based on Multi-Station Laser Scanning
摘要： In order to better display and protect ancient Chinese architecture, a three-dimensional model reconstruction method based on multi-station laser scanning is proposed. This method mainly includes several steps, such as point cloud data collection, preconditioning, multi-site cloud data fusion, point cloud data compression, 3D model reconstruction and texture mapping, environment rendering, video processing, and 3D roaming. According to the requirement of video rendering and virtual roaming, we focus on cloud processing, 3D modeling and 3D model display in this paper. Experimental results show that an famous building named Enshi Dong Drum Tower is well digitally reconstructed, so as to result in ethnic characteristics and cultural heritage protection in practical application.
关键词： 3D laser scanning,3D reconstruction,ancient architecture,point cloud data,digital modeling
更新于2025-09-23 15:19:57
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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Terrestrial laser scanner applied to fluvial geomorphology
摘要： Measuring river geometry and its evolution through time has always been a cornerstone of fluvial geomorphology. While experimental and numerical modeling of fluvial dynamics has been central in understanding long-term dynamics and testing ideas, they remain simplified versions of complex natural systems and cannot necessarily include all relevant processes. Field measurements are thus central to our understanding of elementary processes such as sediment entrainment and deposition, bank erosion, bedrock incision as well as the macroscopic dynamics of river reaches such as channel bed accretion/erosion, bedforms mobility, and river meandering. It is therefore not surprising that fluvial geomorphologists have quickly embraced the use of terrestrial laser scanner (TLS) to study rivers (e.g., Heritage and Hetherington, 2007; Hodge et al., 2009a). TLS allows 3D digitization of fluvial environment in a dense (sub-cm), accurate (mm precision), and nearly exhaustive way (Fig. 1). The very large range of spatial scales covered is particularly impressive, from individual pebbles to km long river reaches (e.g., Brasington et al., 2012). Sub-cm accuracy also offers the possibility of detecting very subtle changes (Lague et al., 2013), a key attribute to measure slow processes such as bedrock abrasion (Beer et al., 2017). Given the recent emphasis on the role of riparian processes on fluvial processes, the ability to digitize vegetation in 3D in relation to channel morphology offers a unique perspective in biogeomorphology. However, many of the promises of TLS have not really been fulfilled, and the scientific potential of the TLS dataset remains often untapped. This is largely due to the challenging aspects surrounding the processing of TLS data which, to a large extent, also apply to structure from motion (SfM) surveys (Passalacqua et al., 2015). Three challenges, akin to typical Big Data issues can be identified as follows: 1. Data Complexity: TLS data are 3D data and nearly exhaustive. This makes for very rich data but also extremely complex to process as the relevant information (e.g., ground, grains, riverbanks, vegetation) must be detected prior to scientific analysis (Fig. 1). TLS data is also natively non-regularly sampled, with strong spatial variations in point density and requires processing methods that are more complex than for 2D raster-based data such as satellite imagery. 2. Data Volume: the latest generation of TLS instruments generates billions of points in a day. Manual processing cannot realistically be applied, and automatic processing methods are paramount. This requires good programing skills as well as a culture of machine learning and computer vision approaches that are not necessarily part of the training of geomorphologists and requires bridging the gap with computer sciences. 3. Data Incompleteness: despite the very large field of view of TLS sensors, the resulting 3D data do not sample the entire surface (Fig. 1). The ground-based viewpoint imparts missing data behind obstacles (grains of any size and vegetation) and the laser is generally fully absorbed by water resulting in the lack of bathymetric data, a strong limitation in river environments. Processing methods must account for this lack of information.
关键词： Terrestrial laser scanner,sediment transport,vegetation classification,bank erosion,3D digitization,point cloud processing,bedrock incision,fluvial geomorphology
更新于2025-09-23 15:19:57
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Image-Translation-Based Road Marking Extraction From Mobile Laser Point Clouds
摘要： Road markings are one of the most important safety elements in a road network, and they play a critical role in traffic safety. However, the automatic extraction of road markings remains a technical challenge in the fields of smart city construction and automatic driving. This paper presents an image-translation-based method of obtaining the 3D vectors of typical road markings from mobile laser point clouds. First, ground roughness is used as a criterion to extract ground points based on the topological relationship of adjacent scan lines, and the feature images of a road surface are generated using the adapted inverse distance weighted method. Second, by comparing objective functions based on the pix2pix framework, a finely adjusted image-to-image translation model named P2P_L1 is proposed for the segmentation of road markings. The proposed model outperforms the advanced DeepLab V3+ network in terms of precision, F1-score, and mean Intersection over Union indicators in the comparative segmentation results of ten types of road markings in the Shenzhen test area. Third, methods such as node averaging and optimized iterative closest point are developed for the 3D vectorization of road markings. This study presents a new approach for the automatic extraction of road markings to provide effective technical support for the construction of smart cities.
关键词： image translation,segmentation,road marking,Conditional generative adversarial nets (cGANs),laser point cloud
更新于2025-09-23 15:19:57
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Classifier-Free Extraction of Power Line Wires from Point Cloud Data
摘要： This paper proposes a classi?er-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method ?rst counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and re?ned iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
关键词： power line,point cloud,extraction,wire,modelling
更新于2025-09-19 17:15:36