Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art

Abstract

Abstract:This article provides an overview of building extraction approaches applied to Airborne Laser Scanning (ALS) data by examining elements used in original publications, such as data set area, accuracy measures, reference data for accuracy assessment, and the use of auxiliary data. We succinctly analyzed the most cited publication for each year between 1998 and 2014, resulting in 54 ISI-indexed articles and 14 non-ISI indexed publications. Based on this, we position some built-in features of ALS to create a comprehensive picture of the state of the art and the progress through the years. Our analyses revealed trends and remaining challenges that impact the community. The results show remaining deficiencies, such as inconsistent accuracy assessment measures, limitations of independent reference data sources for accuracy assessment, relatively few documented applications of the methods to wide area data sets, and the lack of transferability studies and measures. Finally, we predict some future trends and identify some gaps which existing approaches may not exhaustively cover. Despite these deficiencies, this comprehensive literature analysis demonstrates that ALS data is certainly a valuable source of spatial information for building extraction. When taking into account the short civilian history of ALS one can conclude that ALS has become well established in the scientific community and seems to become indispensable in many application fields.

Applications of 3D City Models: State of the Art Review

“…A typical 3D city model is derived from various acquisition techniques, for instance, photogrammetry and laser scanning [5][6][7][8], extrusion from 2D footprints [9,10], synthetic aperture radar [11][12][13][14][15], architectural models and drawings [16][17][18], handheld devices [19,20], procedural modelling [21][22][23][24][25][26], and volunteered geoinformation [27][28][29]. Seemingly, visualisation dominated the early uses of 3D city models.…”

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

“…At present, robustly extracting building points from various and complex urban scenes is still a challenging issue [13,14]. In the last decade, numerous methods have been reported for extracting building information from airborne laser scanning points, including DSM (Digital Surface Model)-based methods [15], point cloud-based methods [16] and methods based on imagery-fusing point clouds [17].…”

“…Supervised learning-based methods [18][19][20][21][22][23] first select some building and non-building data as samples for training classifiers, and then extract building points. However, it is time consuming in selecting samples, and the result is highly dependent on samples [14]. Segmentation-based methods begin by splitting point clouds into disjointed segments, and then extract building segments with some prior knowledge or assumptions [16,[24][25][26][27].…”

Oblique Photogrammetry Supporting 3d Urban Reconstruction of Complex Scenarios

“…Given the necessity to cover large areas, passive or active airborne data still represent the most common source of information for 3D geometry capture in urban scenarios. In fact 3D building and city models are commonly derived either from LiDAR-generated point clouds (Haala and Brenner, 1997;Kada and McKinley, 2009;Tomljenovic et al, 2015) or from airborne imagery and image matching techniques (Suveg and Vosselman, 2004;Kluckner and Bischof, 2010;Bulatov et al, 2014). A combination thereof is also possible (Habib et al, 2011;Sohn et al, 2013;Zhang et al, 2014).…”