K-Plane-Based Classification of Airborne LiDAR Data for Accurate Building Roof Measurement

Kong, Deming; Xu, Lijun; Li, Xiaolu; Li, Shuyang

doi:10.1109/tim.2013.2292310

Cited by 38 publications

(18 citation statements)

References 28 publications

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“…Depending on the author and the developed methodology, the source of additional data varied. This additional data included aerial images [16,19,20,44,[50][51][52][60][61][62]86,[93][94][95], multi-spectral imagery [53,55,57,58,63], purely infrared spectral image [64] or 2D ground plans [35].…”

Section: Use Of Auxiliary Data For Building Object Extractionmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Use Of Auxiliary Data For Building Object Extractionmentioning

confidence: 99%

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

et al. 2015

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“…(3) Clustering. This method first associates each lidar point with a feature vector, which consists of geometric and/or radiometric measures and then segments lidar points in feature spaces by a clustering technique such as k-means, maximum likelihood or fuzzy clustering (Filin, 2002;Hofmann, 2004;Vosselman et al, 2004;Filin and Pfeifer, 2006;Sun and Salvaggio, 2013;Kong et al, 2014;Zhao et al, 2014;Song et al, 2015;He et al, 2016;Kim et al, 2016;Cao et al, 2017). (4) Filtering.…”

Section: Introductionmentioning

confidence: 99%

A greyscale voxel model for airborne lidar data applied to building detection

Wang

Zhao

2018

The Photogrammetric Record

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The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non‐buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non‐buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building façades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success.

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“…The potential of the combination between aerial image and aerial LiDAR data to refine the 3D building models is studied [22][23][24]. As for the increasing requirement of the higher accuracy 3D building models, a new classification method is also proposed to estimate accurate building roofs [25].…”

Section: Introductionmentioning

confidence: 99%

SLAM with 3Dimensional-GNSS

Wada

Hsu

et al. 2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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This paper presents a 3Dimensional-GNSS (3D-GNSS) positioning technique, which is used for localization and mapping simultaneously (SLAM). The 3D building model becomes an important aid to many positioning techniques such as LiDAR and GPS positioning methods. In order to automatically create the accurate map in wide area, the precise position of mobile mapping platform is needed. However, GNSS positioning performance is severely degraded because of the effects of multipath and Non-Line-Of-Sight (NLOS) in the urban area. With the aid of 3D building map, the proposed 3D-GNSS distinguishes whether the received GNSS signal is transmitted as LOS or NLOS path, and can calculate the length of reflection path for the improvement of positioning error. To achieve highly accurate positioning in urban canyon, we further develop 3D-GNSS based integrated vehicle self-localization system, which is comprised of 3D-GNSS, inertial sensor and vision sensor. On the other hand, highly precise positioning needs the accurate 3D building map. Inspired by the idea of 3D-GNSS, this paper proposes to optimize the 3D building map by estimating the reflection of GNSS signals. In addition, the position of mobile mapping platform is estimated from our integrated localization system in the mapping stage. The experimental result demonstrates that sub-meter accuracy is achieved in both localization and mapping.

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K-Plane-Based Classification of Airborne LiDAR Data for Accurate Building Roof Measurement

Cited by 38 publications

References 28 publications

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

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

A greyscale voxel model for airborne lidar data applied to building detection

SLAM with 3Dimensional-GNSS

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