Airborne Laser Scanning Point Research Articles

Estimating tree species composition from airborne laser scanning data using point-based deep learning models

Airborne laser scanning (ALS) data is increasingly being used for accurate estimations of forest inventory attributes, such as tree height and volume. However, determining tree species information, an important attribute required by inventories for a range of forest management applications, is challenging to extract from ALS data. This is due to complex species assemblages and vertical structures in certain forest environments, and a lack of spectral information for most ALS sensors. Our objective was to estimate tree species compositions in a Canadian boreal forest environment using ALS data and point-based deep learning techniques. To accomplish this, we utilised existing polygonal forest resource information to develop a large comprehensive dataset of tree species compositions across a 630,000 ha forest management area. We then used an adapted PointAugment generative adversarial network (GAN) coupled with the dynamic graph convolutional neural network to estimate tree species proportions. This innovative deep learning approach resulted in an overall R2adj of 0.61 for all tree species proportions. A weighted F1 score of 63% was observed when classifying the leading species of a plot, 66% for leading genus, and 85% when distinguishing between coniferous and deciduous dominated plots. Furthermore, the PointAugment GAN successfully generated augmented point clouds of forest plots which can alleviate issues associated with limited training samples for use in deep learning. This approach demonstrates the capability of point-based deep learning techniques to accurately estimate tree species compositions from ALS point clouds within an expansive forested region, characterized by a complex management history and a diversity of tree species. The source code along with the pretrained models are available at https://github.com/Brent-Murray/point-dl/tree/main/Pytorch/models/PointAugment.

Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With its high point density and the vast majority of points (approximately 99%) measured with the first return, filtering LiDAR-UAS data proves to be a more challenging task when compared to ALS point clouds. Various algorithms have been proposed in the scientific literature to differentiate ground points from non-ground points. Each of these algorithms has advantages and disadvantages, depending on the specific terrain characteristics. The aim of this research is to obtain an enhanced Digital Terrain Model (DTM) based on LiDAR-UAS data and to qualitatively and quantitatively compare three filtering approaches, i.e., hierarchical robust, volume-based, and cloth simulation, on a complex terrain study area. For this purpose, two flights over a residential area of about 7.2 ha were taken at 60 m and 100 m, with a DJI Matrice 300 RTK UAS, equipped with a Geosun GS-130X LiDAR sensor. The vertical and horizontal accuracy of the LiDAR-UAS point cloud, obtained via PPK trajectory processing, was tested using Check Points (ChPs) and manually extracted features. A combined approach for ground point classification is proposed, using the results from a hierarchic robust filter and applying an 80% slope condition for the volume-based filtering result. The proposed method has the advantage of representing with accuracy man-made structures and sudden slope changes, improving the overall accuracy of the DTMs by 40% with respect to the hierarchical robust filtering algorithm in the case of a 60 m flight height and by 28% in the case of a 100 m flight height when validated against 985 ChPs.

Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling

ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.

Performance, effectiveness and computational efficiency of powerline extraction methods for quantifying ecosystem structure from light detection and ranging

ABSTRACT National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average F 1 score = 96%), closely followed by the hybrid method (average F 1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average F 1 score = 82%) and depended on landscape context and vegetation composition (e.g. the F 1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.

Which metrics derived from airborne laser scanning are essential to measure the vertical profile of ecosystems?

AbstractIn a recent perspective (Diversity and Distributions, 29, 39–50), ‘10 variables’ were proposed to measure vegetation structure from airborne laser scanning (ALS) for assessing species distributions and habitat suitability. We worry about this list because the variables predominantly represent variation in vegetation height, the vertical variability of vegetation biomass is insufficiently captured, and variables of vegetation cover are ill‐defined or not ecosystem agnostic. We urge for a better defined, more comprehensive and more balanced list, and for assessing which information from ALS point clouds is truly essential to measure the major dimensions of 3D vegetation structure within and across ecosystems and animal habitats. We think that the currently proposed ‘list of 10 ALS metrics’ is premature and that researchers and stakeholders should be cautious in adopting this list.

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (Salix spp., Betula nana) and trees (Betula pubescens ssp. czerepanovii, Sorbus aucuparia, Populus tremula, Pinus sylvestris, Picea abies) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R2-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones.

Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues

The capability of partially penetrating vegetation canopy and efficiently collecting high-precision point clouds over large areas makes airborne laser scanning (ALS) a valuable tool for various geospatial applications. However, automated ground filtering (GF), one fundamental and challenging step for most ALS applications, has remained a widely researched yet unsolved problem for decades. The recent breakthroughs in supervised deep learning (DL) techniques, which rely on sufficient and high-quality labeled datasets, provide a new solution to better solve this problem. Unfortunately, public 3D geospatial datasets are scarce, especially for those tailored for the landform-scale GF task. Moreover, whether advanced deep neural networks (DNNs) can be well-scaled to the problem of GF remains an open question. To comprehensively advance the development of effective DL-based GF pipelines, we first publish an ultra-large-scale GF dataset built upon open-access ALS point clouds of four different countries worldwide, which covers over 47 km2 and nine different terrain scenes. Then, multiple attractive advantages of DL techniques in GF are evaluated through extensive experimental comparisons with traditional GF methods on the presented dataset. Furthermore, we reveal several issues faced by generalizing existing advanced 3D DNNs into GF tasks with a series of in-depth experimental analyses. Finally, some promising directions for future research are suggested in response to the identified challenges. Our dataset, named OpenGF, is available at https://github.com/Nathan-UW/OpenGF.

Detecting the presence of standing dead trees using airborne laser scanning and optical data

ABSTRACT Deadwood is an important indicator of biodiversity in forest ecosystems. Identifying areas with large density of standing dead trees through field inventory is challenging, and remotely sensed data can provide a more systematic approach. In this study, we used metrics derived from airborne laser scanning (ALS) data (7.1 points m−2) and vegetation indices from optical images (HySpex sensor VNIR-1800: 0.3 m, SWIR-384: 0.7 m) to predict the presence of standing dead trees over a 15.9 km2 managed forest in Southern Norway. The dead basal area (DBA) of 40 sample plots was computed and used to classify the plots into presence/absence of standing dead trees. An area-based approach (ABA) using logistic regression was initially tested, but due to limited ground reference information, no statistically significant models could be formulated. A tree-based approach (TBA) was used to overcome this limitation. It identified trees on the ALS point cloud with a local maxima function and used a vegetation index to determine if the trees were dead. Between 18% and 42% of the predicted area with standing dead trees intersected a field recorded validation dataset. The TBA provided a good alternative to area-based regression models in the context of few standing dead trees.

Vectorial and topologically valid segmentation of forestry road networks from ALS data

Accurate information on road location is critical for forest management and conservation strategies. Road location data supports the analysis of road accessibility and usability and is a critical information layer for forest harvest, financial planning, wildfire suppression, and protection activities. The global expanse of forests, their remoteness, and difficulty to access have necessitated the development of automatic or semi-automatic remote sensing methodologies to map roads using passive optical imagery or Airborne Laser Scanning (ALS). Conventional automatic road mapping methods are raster-based and map roads as patches of disconnected pixels. This paper addresses the limitations of raster-based automatic forest road extraction and presents a method for producing a topologically accurate vectorial road network. Our method, presented as a fully documented and open-source software tool, uses metrics derived from an ALS point cloud to produce a raster of road conductivity. From this conductivity raster, the method “drives” the roads iteratively by detecting and following road intersections. We demonstrate the method’s efficacy using a road network in Quebec, Canada, where 96% of the roads in a binary raster, and 84% using our probability map, are vectorized properly from an ALS point cloud with 4% false positives. Our proposed method may significantly reduce the training requirements of machine learning techniques used to classify roads by being very robust to false positive and false negative classifications.

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.

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.

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.

LiDAR Point Clouds Usage for Mapping the Vegetation Cover of the “Fryderyk” Mine Repository

The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3.

Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data

Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination (R2) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were −0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling.

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

AbstractEcosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up‐to‐date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.

A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data

Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.

Systematic Comparison of Objects Classification Methods Based on ALS and Optical Remote Sensing Images in Urban Areas

Geographical object classification and information extraction is an important topic for the construction of 3D virtual reality and digital twin cities in urban areas. However, the majority of current multi-target classification of urban scenes uses only a single source data (optical remote sensing images or airborne laser scanning (ALS) point clouds), which is limited by the restricted information of the data source itself. In order to make full use of the information carried by multiple data sources, we often need to set more parameters as well as algorithmic steps. To address the above issues, we compared and analyzed the object classification methods based on data fusion of airborne LiDAR point clouds and optical remote sensing images, systematically. Firstly, the features were extracted and determined from airborne LiDAR point clouds and high-resolution optical images. Then, some key feature sets were selected and were composed of median absolute deviation of elevation, normalized elevation values, texture features, normal vectors, etc. The feature sets were fed into various classifiers, such as random forest (RF), decision tree (DT), and support vector machines (SVM). Thirdly, the suitable feature sets with appropriate dimensionality were composed, and the point clouds were classified into four categories, such as trees (Tr), houses and buildings (Ho), low-growing vegetation (Gr), and impervious surfaces (Is). Finally, the single data source and multiple data sources, the crucial feature sets and their roles, and the resultant accuracy of different classifier models were compared and analyzed. Under the conditions of different experimental regions, sampling proportion parameters and machine learning models, the results showed that: (1) the overall classification accuracy obtained by the feature-level data fusion method was 76.2% compared with the results of only a single data source, which could improve the overall classification accuracy by more than 2%; (2) the accuracy of the four classes in the urban scenes can reach 88.5% (Is), 76.7% (Gr), 87.2% (Tr), and 88.3% (Ho), respectively, while the overall classification accuracy can reach 87.6% with optimal sampling parameters and random forest classifiers; (3) the RF classifier outperforms DT and SVM for the same sample conditions. In this paper, the method based on ALS point clouds and image data fusion can accurately classify multiple targets in urban scenes, which can provide technical support for 3D scene reconstruction and digital twin cities in complex geospatial environments.

Optimizing the airborne laser scanning estimation of basal area larger than mean ([formula omitted]): An indicator of cohort balance in forests

Airborne laser scanning (ALS) assisted basal area larger than mean (BALM) estimation measures the cohort balance in forests and provides adequate opportunities to describe forest structure. However, a problem still exists that how the plot size, sample size (number of trees), and ALS point density affect the BALM estimation. We tackled this question by using both field and ALS data from a typical managed boreal forest area in Finland. Various concentric circular plots (1–15 m radii) were simulated within the actual field plots (squared) and the optimal plot size and sample size were selected by observing changes in the absolute correlation between BALM estimates and various ALS metrics. Instability in the correlation was found at the smaller concentric circular plots (1–5 m radii) and sample sizes (less than 6 trees) but as the plot size and sample size increased, the correlation followed a convex curve. The maximum correlation was found between a concentric circular plot size 11–14 m radii (380–615 m2 area) and sample size 50–80 trees which could be the optimal plot size and sample size for a reliable BALM estimation. With regards to the ALS point density, no major effects were observed on the relationship between BALM estimates and various ALS metrics unless the point density is less than at least 5 points m−2. The point density of the current nationwide ALS survey is matching the minimum point density requirement obtained in this study and thus it is suitable for a reliable forest structural assessment.

An Improved Multi‐Task Pointwise Network for Segmentation of Building Roofs in Airborne Laser Scanning Point Clouds

AbstractRoof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data‐sets or they are not automatic and efficient. To tackle these issues, an improved multi‐task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean‐shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data‐set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data‐set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task.