Outlier detection and robust plane fitting for building roof extraction from LiDAR data

Abstract

Individual roof plane extraction from LiDAR point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection algorithm has been proposed in this paper along with a robust plane-fitting algorithm based on M-estimator SAmple Consensus (MSAC) for detecting individual roof planes. Using two benchmark datasets (Australian and International Society for Photogrammetry and Remote Sensing benchmark) with different numbers of buildings and sizes, trees and point densities, we have evaluated the proposed method. Experimental results show that the method removes outliers and vegetation almost accurately and offer a high success rate in terms of completeness and correctness (between 80% and 100% per-object) for both roof plane extraction and building detection. In most of the cases, the proposed method shows above 90% correctness.

A Robust Performance Evaluation Metric for Extracted Building Boundaries From Remote Sensing Data

“…13 shows these selected buildings with individual reference roof plane boundaries in blue polygons. We used the method proposed by Dey et al [53] to extract the individual roof planes and apply the RCC metric to evaluate the extracted planes. The colour bar indicates the distance estimation davg by the RCC metric and is scaled from the largest (red) to the smallest (green).…”

“…The buildings with area less than 5 m 2 were not considered for evaluation in our experiments. We follow the method proposed by Dey et al [53] for building boundary extraction. Fig.…”

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

“…To relieve these problems, refs. [17][18][19][20][21][22][23][24] combined optical imagery with GIS data, digital surface models (DSMs) obtained from light detection and ranging (lidar), or synthetic aperture radar interferometry to distinguish non-buildings that are highly similar to buildings, increasing the robustness of building extraction, are used. However, obtaining a wide range of corresponding multisource data is always costly.…”

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

“…Estimation of the feature points and lines is a fundamental problem in the field of image and shape analysis as this estimation facilitates better understanding of an object in a variety of areas, e.g., data registration [1], data simplification [2], road extraction [3], and building reconstruction [4]. Specifically, the area of 3D building reconstruction has a broad range of applications, such as building type classification, urban planning, solar potential estimation, change detection, forest management, and virtual tours [5][6][7][8][9][10]. Due to the availability of 3D point cloud data, from both airborne and ground-based mobile laser scanning systems, the extraction of 3D feature points and lines from point cloud data has become an attractive research topic to describe an object shape more accurately.…”

“…Therefore, LiDAR data provide more accurate geometric information than the 2D image and are used as the main input data for automatic building reconstruction [12,13]. The reconstruction approaches from LiDAR data can be broadly categorised into two: model-driven and data-driven [7]. The first approach finds the most similar models previously stored in the database to the input data, whereas the second approach tries to generate any building model from the provided 3D data.…”