Laser Scanning Point Clouds Research Articles

A Novel Type of Boundary Extraction Method and Its Statistical Improvement for Unorganized Point Clouds Based on Concurrent Delaunay Triangular Meshes.

Currently, three-dimensional (3D) laser-scanned point clouds have been broadly applied in many important fields, such as non-contact measurements and reverse engineering. However, it is a huge challenge to efficiently and precisely extract the boundary features of unorganized point cloud data with strong randomness and distinct uncertainty. Therefore, a novel type of boundary extraction method will be developed based on concurrent Delaunay triangular meshes (CDTMs), which adds the vertex-angles of all CDTMs around a common data point together as an evaluation index to judge whether this targeted point will appear at boundary regions. Based on the statistical analyses on the CDTM numbers of every data point, another new type of CDTM-based boundary extraction method will be further improved by filtering out most of potential non-edge points in advance. Then these two CDTM-based methods and popular α-shape method will be employed in conducting boundary extractions on several point cloud datasets for comparatively analyzing and discussing their extraction accuracies and time consumptions in detail. Finally, all obtained results can strongly demonstrate that both these two CDTM-based methods present superior accuracies and strong robustness in extracting the boundary features of various unorganized point clouds, but the statistically improved version can greatly reduce time consumption.

Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds

Terrestrial laser scanning (TLS) is a ground-based approach to rapidly acquire 3D point clouds via Light Detection and Ranging (LiDAR) technologies. Quantifying tree-scale structure from TLS point clouds requires segmentation, yet there is a lack of automated methods available to the forest ecology community. In this work, we consider the problem of segmenting a forest TLS point cloud into individual tree point clouds. Different approaches have been investigated to identify and segment individual trees in a forest point cloud. Typically these methods require intensive parameter tuning and time-consuming user interactions, which has inhibited the application of TLS to large area research. Our goal is to define a new automated segmentation method that lifts these limitations. Our Topology-based Tree Segmentation (TTS) algorithm uses a new topological technique rooted in discrete Morse theory to segment input point clouds into single trees. TTS algorithm identifies distinctive tree structures (i.e., tree bottoms and tops) without user interactions. Tree tops and bottoms are then used to reconstruct single trees using the notion of relevant topological features. This mathematically well-established notion helps distinguish between noise and relevant tree features. To demonstrate the generality of our approach, we present an evaluation using multiple datasets, including different forest types and point densities. We also compare our TTS approach with open-source tree segmentation methods. The experiments show that we achieve a higher segmentation accuracy when performing point-by-point validation. Without expensive user interactions, TTS algorithm is promising for greater usage of TLS point clouds in the forest ecology community, such as fire risk and behavior modeling, estimating tree-level biodiversity structural traits, and above-ground biomass monitoring.

Satellite visibility analysis considering signal attenuation by trees using airborne laser scanning point cloud

Satellite visibility analysis considering signal attenuation by trees using airborne laser scanning point cloud

Assessing the Quality of the Leica BLK2GO Mobile Laser Scanner versus the Focus 3D S120 Static Terrestrial Laser Scanner for a Preliminary Study of Garden Digital Surveying

Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity, and history of a people. They also contribute to the ecological balance of the city. Despite the fact that gardens have an historic and social value, they are not protected as much as the rest of the existing heritage, such as architecture and archaeological sites. While methods of built-heritage mapping and monitoring are increasing and constantly improving to reduce built-heritage loss and the severe impact of natural disasters, the documentation and survey techniques for gardens are often antiquated. In addition, inventories are typically made by non-updated/updateable reports, and they are rarely in digital format or in 3D. This paper presents the results of a comprehensive study on the latest technology for laser scanning in gardens. We compared static terrestrial laser scanning and mobile laser scanning point clouds generated by the Focus 3D S120 and the Leica BLK2GO, respectively, to evaluate their quality for documentation, estimate tree attributes, and terrain morphology. The evaluation is based on visual observation, C2C comparisons, and terrain information extraction capabilities, i.e., M3C2 comparisons for topography, DTM generation, and contour lines. Both methods produced useful outcomes for the scope of the research within their limitations. Terrestrial laser scanning is still the method that offers accurate point clouds with a higher point density and less noise. However, the more recent mobile laser scanning is able to survey in less time, significantly reducing the costs for site activities, data post-production, and registration. Both methods have their own restrictions that are amplified by site features, mainly the lack of plans for the geometric alignment of scans and the simultaneous location and mapping (SLAM) process. We offer a critical description of the issues related to the functionality of the two sensors, such as the operative range limit, light dependency, scanning time, point cloud completeness and size, and noise level.

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features.

Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, in this paper, a method is proposed to improve the accuracy and efficiency of the classification of point clouds of transmission lines, which is based on improved Random Forest and multi-scale features. The point clouds are filtered by the optimized progressive TIN densification filtering algorithm, then the elevations of the filtered point cloud are normalized. The features of the point cloud at different scales are calculated according to the basic features of the point cloud and the characteristics of transmission lines. The Relief F and Sequential Backward Selection algorithm are used to select the best subset of features to estimate the parameters of the learning model, then an Improved Random Forest classification model is built to classify the point clouds. The proposed method is verified by using three different samples from the study area and the results show that, compared with the methods based on Support Vector Machines, AdaBoost or Random Forest, our method can reduce feature redundancy and has higher classification accuracy and efficiency.

Estimating canopy height change using machine learning by coupling WorldView-2 stereo imagery with Landsat-7 data

ABSTRACT Canopy height change (CHC) is one of the key characteristics of forest dynamics, associated with the fluctuations in forest above-ground biomass and carbon stocks. Field measurements and Airborne Laser Scanning (ALS) point clouds can be used to detect CHC; however, they have limited availability in space and time, making it challenging to map CHC over large areas. Alternatively, very high-resolution (VHR) satellite stereo imagery plays an increasingly vital role in estimating fine-scaled digital surface models (DSMs) across landscapes. However, its capability and potential to estimate canopy height model (CHM) and CHC has not been widely explored. Using ALS-derived CHM in 2011 and 2015 and the four-year CHC as references, we evaluated stereo-based CHM and CHC from WorldView-2 over five woody parks in Columbus, Ohio, USA. We also integrated stereo-based CHM with vegetation indices from Landsat 7 to improve CHM and CHC estimation with machine learning methods. Our results showed that VHR stereo imagery captured similar spatial patterns of CHM with ALS data but significantly overestimated CHM. Moreover, the ALS-derived CHC ranged from 1.6±1.9 m (mean ± standard deviation) to 3.1±1.2 m, as compared to from -1.3±0.7 m to 1.1±1.7 m for stereo-based CHC, indicating the limitation of CHC estimation by stereo imagery alone. Among six widely used machine learning methods, Gradient Boosting Regression method provided the most reliable estimates of CHM, with a correlation coefficient R of 0.64 and a root-mean-square error (RMSE) of 3.1 m (11.1%). Stereo-based CHM and vegetation indices explained more than 70% of CHM variability, substantially improving the estimation of 4-year CHC. Our results suggested that VHR stereo imagery alone has limitations in estimating CHM and CHC. The combination of remote-sensing structural (stereo-based CHM) and spectral (vegetation indices) information improves the CHM and CHC estimations.

AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS

Abstract. This article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D point coordinates into a semantic representation. The proposed framework has three main parts, including the development of a supervoxel data structure, point cloud segmentation based on local graphs, and using three methods for object-based classification. The results of the point cloud segmentation with an average segmentation error of 0.15 show that the supervoxel structure with an optimal parameter for the number of neighbors can reduce the computational cost and the segmentation error. Moreover, weighted local graphs that connect neighboring supervoxels and examine their similarities play a significant role in improving and optimizing the segmentation process. Finally, three classification methods including Random Forest, Gradient Boosted Trees, and Bagging Decision Trees were evaluated. As a result, the extracted segments were classified with an average precision of higher than 83%.

What Are We Missing? Occlusion in Laser Scanning Point Clouds and Its Impact on the Detection of Single-Tree Morphologies and Stand Structural Variables

Laser scanning has revolutionized the ability to quantify single-tree morphologies and stand structural variables. In this study, we address the issue of occlusion when scanning a spruce (Picea abies (L.) H.Karst.) and beech (Fagus sylvatica L.) forest with a mobile laser scanner by making use of a unique study site setup. We scanned forest stands (1) from the ground only and (2) from the ground and from above by using a crane. We also examined the occlusion effect by scanning in the summer (leaf-on) and in the winter (leaf-off). Especially at the canopy level of the forest stands, occlusion was very pronounced, and we were able to quantify its impact in more detail. Occlusion was not as noticeable as expected for crown-related variables but, on average, resulted in smaller values for tree height in particular. Between the species, the total tree height underestimation for spruce was more pronounced than that for beech. At the stand level, significant information was lost in the canopy area when scanning from the ground alone. This information shortage is reflected in the relative point counts, the Clark–Evans index and the box dimension. Increasing the voxel size can compensate for this loss of information but comes with the trade-off of losing details in the point clouds. From our analysis, we conclude that the voxelization of point clouds prior to the extraction of stand or tree measurements with a voxel size of at least 20 cm is appropriate to reduce occlusion effects while still providing a high level of detail.

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.

Simulation of silvicultural treatments based on real 3D forest data from mobile laser scanning point clouds

Forest management has a direct influence on the structure and stability of forests. In this study, we used the 3D data from mobile laser scanning in real forest stands dominated by European beech (Fagus sylvatica L.) to simulate different silvicultural treatments and assess their impact on the structural complexity and short-term economic return. For the structural assessment, we used the box-dimension (Db), a holistic measure of structural complexity in forest. The expected net revenues of the silvicultural treatments were used as a proxy for short-term economic gain. We simulated six different treatments in 19 different real-world forest stands. The results showed that each treatment had a negative impact on the structural complexity of the stands but with varying severity. The treatments with the smallest effect on stand structural complexity showed the highest net revenue, indicating no trade-offs if a forest owner strives for small stand structural changes and high economic return. The approach used here allows quantifying the structural and economic consequences of different treatments in forest stands prior to the actual application in the real world. This holds large potential for decision making according to the forest owner's objective.

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

Street tree extraction based on the 3D mobile mapping point cloud plays an important role in building smart cities and creating highly accurate urban street maps. Existing methods are often over- or under-segmented when segmenting overlapping street tree canopies and extracting geometrically complex trees. To address this problem, we propose a method based on improved 3D morphological analysis for extracting street trees from mobile laser scanner (MLS) point clouds. First, the 3D semantic point cloud segmentation framework based on Deep Learning is used for pre-classification of the original point cloud to obtain the vegetation point cloud in the scene. Considering the influence of terrain unevenness, the vegetation point cloud is de-terraformed and slice point cloud containing tree trunks is obtained through spatial filtering on height. On this basis, a voxel-based region growing method constrained with the changing rate of convex area is used to locate the stree trees. Then we propose an progressive tree crown segmentation method, which first completed the preliminary individual segmentation of the tree crown point cloud based on the voxel-based region growth constrained by the minimum increment rule, and then optimizes the crown edges by “valley” structure-based clustering. In this paper, the proposed method is validated and the accuracy is evaluated using three sets of MLS Datasets collected from different scenarios. The experimental results show that the method can effectively identify and localize street trees with different geometries and has a good segmentation effect for street trees with large adhesion between canopies. The accuracy and recall of tree localization are higher than 96.08% and 95.83%, respectively, and the average precision and recall of instance segmentation in three datasets are higher than 93.23% and 95.41%, respectively.

Screening of Ginkgo Individuals with Superior Growth Structural Characteristics in Different Genetic Groups Using Terrestrial Laser Scanning (TLS) Data.

With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.

OFFS-Net: Optimal Feature Fusion-Based Spectral Information Network for Airborne Point Cloud Classification

Airborne laser scanning (ALS) point cloud classification is a necessary step for understanding 3-D scenes and their applications in various industries. However, the classification accuracy and efficiency are low: 1) point cloud classification methods lack effective filtering of the large number of traditional features, 2) significant category imbalance and coordinate scale problems in ALS point cloud classification. To address these problems, this article proposes an airborne LiDAR point cloud classification method based on deep learning network with optimal feature fusion-based spectral information. This method involves the following steps: First, multiscale point cloud features are extracted, and random forest method is used to filter the features, while spectral information is fused to obtain a point cloud feature dataset with less but better data. Second, to adapt to the characteristics of the airborne point cloud, the improved RandLA-Net can simultaneously retain the advantages of random sampling and learn deeper semantic information by fusing the constructed point cloud features with the local feature aggregation module in the network. Third, four fusion models are constructed to verify the effectiveness of the optimal feature fusion-based spectral information network (OFFS-Net) model for airborne point cloud classification. Last, these models are trained and tested on Vaihingen 3-D dataset. The OFFS-Net achieves overall accuracy score of 84.9% and F1-score of 72.3%, which are better than the mainstream methods. This also validates that the proposed OFFS-Net point cloud classification method, based on the advantages of geometric feature and spectral information is excellent.

A Content-Adaptive Hierarchical Deep Learning Model for Detecting Arbitrary-Oriented Road Surface Elements Using MLS Point Clouds

Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information of road surface element, we propose a content-adaptive hierarchical deep learning model to detect arbitrary-oriented road surface elements from mobile laser scanning (MLS) point clouds. In the model, we design a densely connected feature integration module (DCFM) to connect and reorganize feature maps of each stage in the backbone network. Besides, we propose a hierarchical prediction module (HPM) to innovatively use the reorganized feature maps to recognize different types of road surface elements, and thus, semantic information of road surface element can be adaptively expressed on multilevel feature maps. We also add a cascade structure (CS) in the head of model to detect the target efficiently, which can learn the offset between the predicted minimum bounding box of road surface element and ground truth. In experiments, we prove that the proposed method mainly contributed by HPM can maintain robust detection performance, even in the cases of unbalanced category number or overlapping of road surface elements. The experiments also prove that the proposed DCFM can improve the recognition effects of small targets. The CS for predicting boundary offset can detect each target more accurately. We also integrate the designed modules into some rotation detectors, e.g., the EAST and R3Det, and achieve the state-of-the-art results in three road scenes with different categories and uneven distribution of road surface elements, which further shows the effectiveness of the proposed method.

Extracting 3-D Structural Lines of Building From ALS Point Clouds Using Graph Neural Network Embedded With Corner Information

The representation quantifies the geometric shape and topology of a building is a necessary procedure for many urban planning applications. A sharp line framework is a high-level structural cue providing a compact building representation. However, accurate and efficient structural line extraction remains a challenging task given the variety and complexity of buildings. This study proposes a general 3-D structural line extraction method from point clouds. The building points are extracted and further divided into various single-building units. In the proposed 3-D structural line extraction method, individual building point cloud is the input. First, the corners are detected by an associative learning module. Next, the curve connection is implemented by a link prediction block based on the graph neural network (GNN) embedded with corner information. After that, the obtained curves are subsequently converted into a topological graph. Finally, the corner points are optimized to achieve precise fitting of the structural lines. The experiments and comparisons on two airborne laser scanning (ALS) point cloud datasets demonstrate the effectiveness of the proposed method and the ability to retrieve ideal structural line results for building point clouds. Furthermore, without reprocessing, the proposed method yielded better results for various dataset types (outdoor building, indoor scene, and furniture point clouds) than the prevalent published methods ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., EC-Net, PIE-Net, and PC2WF), verifying its strength and efficacy. To further verify the accuracy of the obtained structural lines, we also introduce a line-based model reconstruction method that employ these lines for building reconstruction.

Optimized Views Photogrammetry: Precision Analysis and a Large-Scale Case Study in Qingdao

Unmanned aerial vehicle (UAVs) have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure, and accurate data acquisition brings great challenges to 3-D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are nontrivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3-D modeling. Different from oblique photogrammetry, optimized views photogrammetry uses rough models to generate and optimize UAV trajectories, which is achieved through the consideration of model point reconstructability and view point redundancy. Based on the principle of optimized views photogrammetry, this study first conducts a precision analysis of 3-D models by using UAV images of optimized views photogrammetry and then executes a large-scale case study in the urban region of Qingdao City, China, to verify its engineering potential. By using GCPs for image orientation precision analysis and terrestrial laser scanning (TLS) point clouds for model quality analysis, experimental results show that optimized views photogrammetry could construct stable image connection networks and could achieve comparable image orientation accuracy. Benefiting from the accurate image acquisition strategy, the quality of mesh models significantly improves, especially for urban areas with serious occlusions, in which 3 to 5 times of higher accuracy has been achieved. Besides, the case study in Qingdao City verifies that optimized views photogrammetry can be a reliable and powerful solution for the large-scale 3-D modeling in complex urban scenes.

Application of 3D Digital Modeling Technology in the Construction of Digital Cities

The concepts of “digital twins”, “3D real scene”, “metacosm” and others were the technical paths for building digital cities with the development of emerging surveying and mapping science and technology, which was to build a digital and virtualized city that matched the real physical world, to achieve a one-to-one correspondence between all elements of the physical world and the digital virtual world. And one of its basic geographic information data was a highly similar, virtual simulation of the 3D real scene. After exploring the traditional manual 3DsMax modeling, UAV low-altitude digital oblique photogrammetry modeling, airborne laser scanning modeling and other single modeling technologies, this paper discussed the 3D digital modeling technology used by the UAV airborne laser scanning point cloud and low-altitude digital oblique photogrammetry for complementary integration, constructing the 3D scene of the digital city. This paper expounded the technical route and production process of 3D digital modeling, in order to provide technical references for related projects.

Recurrent Residual Dual Attention Network for Airborne Laser Scanning Point Cloud Semantic Segmentation

Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of ALS point clouds for complex laser scanning conditions. Therefore, we rethink KPConv and propose a recurrent residual dual attention network (RRDAN) based on the encoder-decoder structure for the semantic segmentation of ALS point cloud data. In the encoder stage, we design an attention kernel point convolution (AKPConv) block by using a scaling factor of batch normalization to highlight the significant channel information. Then, we use the AKPConv block to develop a recurrent residual kernel attention (RRKA) module to iteratively aggregate the local neighborhood features. In the decoder stage, we design a global and local channel attention (GLCA) module with global connection and local 1D convolution to interact the global and local information after fusing the upsampled high-level representations and the skip-connected low-level features. In addition, to reduce the influence of the long-tail distribution of reflection intensity, we apply gamma transformation to correct the data as normal distribution. The proposed RRDAN can achieve diversified feature aggregation to implement the refined semantic segmentation of ALS point clouds. We evaluate our method on two ALS datasets (i.e., ISPRS and DCF2019) to demonstrate its performance compared to a few advanced methods. Code: https://github.com/SC-shendazt/RRDAN.

A Knowledge-Based Multi-Scale Adaptive Classification Approach for Mobile Laser Scanning Point Clouds in Urban Scenes

A Knowledge-Based Multi-Scale Adaptive Classification Approach for Mobile Laser Scanning Point Clouds in Urban Scenes

Self-Constrained Baseline 3-D Correction Approach for Mobile Laser Scanning Point Cloud in Complex Urban Road Environments

Self-Constrained Baseline 3-D Correction Approach for Mobile Laser Scanning Point Cloud in Complex Urban Road Environments