Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools

Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain. Indeed, this paper presents one method of supervised machine learning, which is called Random Forest. This algorithm is discussed, and their primary applications in automatic vegetation extraction and modelling in the LiDAR data area are presented here

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building's footprint. A thin point slice parallel to one coordinate axis around the gravity center was considered, and a vertical cross-section was rotated around a vertical axis passing through the gravity center, to generate the 3D building model. The constructed model was visualized with a matrix composed of three matrices, where the same dimensions represented the X, Y, and Z Euclidean coordinates. Five tower point clouds were used to evaluate the performance of the proposed algorithm. Then, to estimate the accuracy, the point cloud was superimposed onto the constructed model, and the deviation of points describing the building model was calculated, in addition to the standard deviation. The obtained standard deviation values, which express the accuracy, were determined in the range of 0.21 m to 1.41 m. These values indicate that the accuracy of the suggested method is consistent with approaches suggested previously in the literature. In the future, the obtained model could be enhanced with the use of points that have considerable deviations. The applied matrix not only facilitates the modeling of buildings with various levels of architectural complexity, but it also allows for local enhancement of the constructed models

Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach

3D extraction and modelling of building by using lidar data

Pour construire automatiquement un modèle 3D d'une ville à partir de données lidar, deux étapes sont indispensables. La première consiste à segmenter automatiquement le nuage de points pour en extraire des classes (en général le sol, les bâtiments et la végétation). La seconde se base ensuite sur la classe bâtiments pour en modéliser les éléments de manière automatique. L'approche proposée consiste à réaliser une segmentation automatique en deux étapes. La première étape vise à segmenter le Modèle Numérique de Surface (MNS) en deux classes que sont le sol et le sursol . Pour cela, un seuillage local est appliqué par le biais d'un opérateur de convolution. Cette procédure permet de séparer le sursol du sol, même dans les régions de topographie accidentée. La deuxième étape consiste à détecter les bâtiments à partir de la classe sursol . A cet effet, le MNS et le nuage de points sont utilisés conjointement de manière à profiter des atouts de chacun. Pour la modélisation automatique de bâtiments, l'approche proposée est composée de trois étapes essentielles : la modélisation des façades, la modélisation 2D des toits et la modélisation 3D des toits. La technique RANSAC (RANdom SAmple Consensus) a été adaptée et appliquée afin de détecter automatiquement les plans les plus probables du toit. Enfin, la détection des plans des toits, des arêtes et des nœuds de toits permettent des modéliser les toits des bâtiments en 3D.In order to construct automatically a 3D city model from lidar data, two steps are essential. The first one is the automatic segmentation of the point cloud into three mean classes (terrain, vegetation and buildings). Once the buildings are detected, the automatic building construction can start. The proposed approach achieves automatically the segmentation task into two stages. The first one is the segmentation of the Digital Surface Model (DSM) into two classes which are terrain and off-terrain. For this purpose, a local thresholding is applied through a convolution operator. This operation allows to separate the two classes even in rugged topography areas. The second step is the automatic building detection from the off-terrain class. At this stage, the DSM and the point cloud are used together in order to take advantage of each one. For automatic modelling of buildings, the proposed approach consists of three phases: buildings facade modelling by detection and segmentation of building outline polygons, construction of 2D roof model starting from the automatic detection of roof planes and finally, the total 3D building model is calculated by analysing the mutual relationships between adjacent roof planes. The technique RANSAC (Random Sample Consensus) has been extended and applied to detect automatically the roof planes. Finally the detection of roof planes, borders and nodes allows to construct the 3D building model

3D extraction and modelling of building by using lidar data

Pour construire automatiquement un modèle 3D d'une ville à partir de données lidar, deux étapes sont indispensables. La première consiste à segmenter automatiquement le nuage de points pour en extraire des classes (en général le sol, les bâtiments et la vIn order to construct automatically a 3D city model from lidar data, two steps are essential. The first one is the automatic segmentation of the point cloud into three mean classes (terrain, vegetation and buildings). Once the buildings are detected, th

Comparison of LiDAR Building Point Cloud with Reference Model for Deep Comprehension of Cloud Structure

This paper studies the fidelity level of the extracted LiDAR (Light Detection And Ranging) building point cloud in relation to the original building. In this context, the building point cloud is compared with a reference model. This comparison allows a deep understanding of the point cloud structure with respect to both the actual building and the constructed model. Consequently, the source of the incompatibility in a (reference or constructed) building model is determined and described. Thus, this study considers four aspects of the building point cloud. First, the errors of building point cloud extraction and the undesirable points are quantified. Hence, it is found that the percentages of undesirable points are sometimes considerable (between 15% and 70%). Second, the evaluation of the fit of the altimetry with the reference model shows that the roof plane equations calculated from LiDAR data can be more precise than those of reference model, Third, the segmentability level between different point densities and building typologies are variable. Finally, the per plane comparison mentions the incompatibility of the plane boundaries of point cloud with reference model. Moreover, considerable differences are noted between the theoretical and the true point densities

EXTENDED RANSAC ALGORITHM FOR AUTOMATIC DETECTION OF BUILDING <br />ROOF PLANES FROM LIDAR DATA

International audienceAirborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. For this purpose, this paper studies the Random Sample Consensus (RANSAC) algorithm. Its principle and pseudocode - seldom detailed in the related literature - as well as its complete analyse are presented in this paper. Despite all advantages of this algorithm, it gives sometimes erroneous results. That can be explained by the fact that it uses a pure mathematical principle for detecting the roof planes. So it looks for the best plane without taking into account the particularity of the captured object. The extended RANSAC algorithm proposed in this paper allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. It is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Moreover, the adjacency relationships of the neighbouring roof planes are described and analysed. Hence the roof planes are successfully detected and adjacency relationships of the adjacent roof planes are calculate. Finally the automatic building modelling can be carried out easily

Three-Dimensional Modeling and Visualization of Single Tree LiDAR Point Cloud Using Matrixial Form

Tree modeling and visualization still represent a challenge in the light detecting and ranging area. Starting from the segmented tree point clouds, this article presents an innovative tree modeling and visualization approach. The algorithm simulates the tree point cloud by a rotating surface. Three matrices, X, Y, and Z, are calculated by considering the middle of the projected tree point cloud on the horizontal plane. This mathematical form not only allows tree modeling and visualization but also permits the calculation of geometric characteristics and parameters of the tree. The superimposition of the tree point cloud over the constructed model confirms its high accuracy where all the points of the tree cloud are within the constructed model. The tests with multiple single trees demonstrate an overall average fit between 0.3 and 0.89 m. The built tree models are also compliant with the Open Geospatial Consortium CityGML standards at the level of a physical model. This approach opens a door to numerous applications for visualization, computation, and study of forestry and vegetation in urban as well as rural areas

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas