Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Abstract

Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam.

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

“…Second, in the building modelling algorithm, the filtering operation is applied to the building point cloud before starting the construction of the building model. Hence, the filtering algorithm aims to eliminate the undesirable points before starting the modelling step [34][35][36]. In the two last cases, the filtering algorithm is merged with the classification or modelling approaches.…”

“…Despite this, the influence of the employed filtering operation will appear in the quality of the final product (building mask or building model). This is why the accuracy of the suggested approach is compared with the accuracy of the approaches [7,9,11,[34][35][36] in the context of comparing the efficacity of the suggested approach with other similar approaches suggested in the literature To clarify how this comparison can be achieved, let us take this example. First, the result of an urban point cloud classification is a building mask named m 1 .…”

“…According to Griffiths and Boehm [37], the high trees associated with the building roof are considered the main source of incorrect results among the several LiDAR data procedures such as automatic building extraction using convolutional neural networks [7,11] and building outlines' modelling [34][35][36]. In this context, it is useful to compare the accuracy of the suggested approach with the accuracy of six previous studies (Table 4).…”

A Simple Yet Effective Approach of Building Footprint Extraction in Indonesia

“…Many algorithms have been developed for building class classification from LiDAR point clouds, for instance using input attributes from DSM and ortho image [11], a supervised parametric classification algorithm [12], the theory of Dempster-Shafer for two different stages of classification process [13], the geometry and echo information of LiDAR point cloud to estimate parameters for the learning model [14], and a multiple-entity based classification method [15]. Regarding building extraction, a lot of research found alternative methods for building extraction from LiDAR, for example using OBIA (Object Based Image Analysis) with the use of class modelling methods [16], data fusion of point and grid-based features using a graph cuts algorithm [17], a novel ordered points-aided Hough Transform (OHT) [18], a modified LEGION segmentation [19], automatic process with emphasis on top-down approaches [20], and rule-based segmentation of non-ground LiDAR point clouds [21].…”

The Impact of Point Cloud Density on Building Outline Extraction

“…Various algorithms have been further applied for building outline extraction from point clouds, e.g. random sample consensus (RANSAC) (Jarzabek-Rychard, 2012), Hough transform (Widyaningrum et al, 2019), convex hull (Sampath and Shan, 2007), alpha shape (dos Santos et al, 2019) or combinations of them (Albers et al, 2016). A comprehensive review of the methods for building outline extraction from ALS point clouds has been provided by Tomljenović et al (2015).…”