Influence of point cloud density on the results of automated Object-Based building extraction from ALS data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Nowadays there is a plethora of approaches dealing with object extraction from remote sensing data. Airborne Laser scanning (ALS) has become a new method for timely and accurate collection of spatial data in the form of point clouds which can vary in density from less than one point per square meter (ppsm) up to in excess of 200 ppsm. Many algorithms have been developed which provide solutions to object extraction from 3D data sources as ALS point clouds. This paper evaluates the influence of the spatial point density within the point cloud on the obtained results from a pre-developed Object-Based rule set which incorporates formalized knowledge for extraction of 2D building outlines. Analysis is performed with regards to the accuracy and completeness of the resultant extraction dataset. A pre-existing building footprint dataset representing Lake Tahoe (USA) was used for ground truthing. Point cloud datasets with varying densities (18, 16, 9, 7, 5, 2, 1 and 0.5ppsm) where used in the analysis process. Results indicate that using higher density point clouds increases the level of classification accuracy in terms of both completeness and correctness. As the density of points is lowered the accuracy of the results also decreases, although little difference is seen in the interval of 5-16ppsm

Multi-scale conditional random fields for over-segmented irregular 3D point clouds classification

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holderIn this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suterpsilas methods by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved recognition rate. In the model, the raw data points are over-segmented into an improved mid-level representation, ldquosuper-voxelsrdquo. Local and regional features are then extracted from the super-voxel and parameters learnt by the multi-scale Conditional Random Fields. The classification accuracy is improved by 5% to 10% with our proposed model compared to labeling with Conditional Random Fields in (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lower than the previous 3D data labeling approaches.Ee Hui Lim, David Sute

Automated delineation of roof planes from LIDAR data

In this paper, we describe an algorithm for roof line delineation from LIDAR data which aims at achieving models of a high level of detail. Roof planes are initially extracted by segmentation based on the local homogeneity of surface normal vectors of a digital surface model (DSM). A case analysis then reveals which of these roof planes intersect and which of them are separated by a step edge. The positions of the step edges are determined precisely by a new algorithm taking into account domain specific information. Finally, all step edges and intersection lines are combined to form consistent polyhedral models. In all phases of this workflow, decision making is based upon statistical reasoning about geometrical relations between neighbouring entities in order to reduce the number of control parameters and to increase the robustness of the method

Object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user’s accuracy of the flat roof plane 97.75% and pitched roof plane 100%

Robot manipulator self-identification for surrounding obstacle detection

Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithm

RECONSTRUÇÃO TRIDIMENSIONAL DE EDIFICAÇÕES UTILIZANDO DADOS LASER SCANNER AEROTRANSPORTADOS

A building can be reconstructed from its roof, which can be defined by someparameters like orientation, height, ridges and contours. In many models forbuilding extraction, using Laser scanner raw data, plants are requested to accomplish the precise location of building contours. The purpose of this study is topresent a method for building reconstruction using Laser scanner data. For this, the2D Hough transform is applied to the Laser data, represented as a raster image, tofind the borders of the roof. Then, the planes that model the roof are estimated andthe form of the roof is derived. The method proved to be efficient in buildingmodeling. In the pre-processing step, the Hough transform and thinning were usedto estimate the borders of the buildings. The method to find the planes of the roof iseffective and consists of looking for three points that define each plane of the roof.The ridge lines are got by intersecting these planes.Uma edificação pode ser reconstruída a partir de seu telhado, o qual pode serdefinida por parâmetros como orientação, altura, bordas do contorno e da cumeeira.O propósito do presente trabalho é apresentar um método para reconstruçãoautomática de modelos de edificação 3D em áreas construídas utilizando dadosprovenientes do Laser scanner aerotransportado. Para tanto, determina-se asextremidades do telhado, encontra-se os planos e pontos do Laser que compõemcada face do telhado, realiza-se a interseção dos planos gerados para localizar acumeeira e, finalmente, utiliza-se estes parâmetros para reconstruir a edificação.Este método apresentou eficiência na reconstrução das edificações. No préprocessamento,foi utilizada da transformada de HOUGH, para encontrar as retaspertinentes aos limites das edificações, após a utilização de um algoritmo deesqueletonização para o afinamento das bordas, a qual mostrou-se uma ferramentaeficiente que ameniza o deslocamento das bordas e otimiza o processamento datransformada de HOUGH. O método para determinar os planos do telhado consisteem localizar, de modo automático, três pontos que definam um plano eposteriormente a determinação da cumeeira é realizada por meio da interseção dosplanos formados