Legion Sementation for Building Extraction From Lidar Based DSM Data

Liu, C.; Shi, Bingjie; Yang, Xiaochun; Li, N.

doi:10.5194/isprsarchives-xxxix-b3-291-2012

Cited by 7 publications

(11 citation statements)

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“…All the parameters for the method are pre-set by the authors. The outlines of the clusters are refined and regularized in order to have rectangular outlines using a least squares estimation method (Liu et al, 2012).…”

Section: Liu Tongji University China (Ton)mentioning

confidence: 99%

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

Rottensteiner¹,

Sohn

Jung

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

408

273

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ABSTRACT:For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

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Section: Liu Tongji University China (Ton)mentioning

confidence: 99%

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

Rottensteiner¹,

Sohn

Jung

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

408

273

View full text Add to dashboard Cite

show abstract

“…In all three VH areas, the proposed method of building detection offered similar or better completeness in both object-and pixel-based evaluation than the alternative methods, namely Dorninger, Niemeyer [35] and Liu [20], except in Areas 2 and 3, in which Niemeyer gave slightly better pixel-and object-based completeness, respectively, than the proposed method (see Table 12). Nevertheless, in terms of object-based correctness, while Niemeyer showed significantly worse results, Dorninger provided the best performance in all three areas.…”

Section: Comparison With Other Methodsmentioning

confidence: 91%

“…The results of the existing methods on the VH data set, shown in Tables 12 and 13, are available in [20,35,40]. The first three methods in Table 11 are used to compare building detection results, and the remaining two and Dorninger [26] are used to compare roof plane extraction results to those of the proposed method.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…A major drawback with this method was that it required user interaction to a certain level in order to set the algorithmic parameters appropriately for different situations. Liu et al [20] employed a neural network approach to extract buildings from a DSM derived from LIDAR data. The grey level co-occurrence matrix and height texture were used for the separation of buildings and trees.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

2014

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Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a "building mask". The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m 2 . Thus, the proposed method can be exploited in various applications.

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“…Single-source-data-based methods, where the data include Airborne Laser Scanning (ALS) point clouds [9,10], ALS-based Digital Surface Model (DSM) grids [11,12], and images [13,14].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Buildings from Multiple-View Aerial Images Using a Feature-Level-Fusion Strategy

et al. 2018

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Aerial images are widely used for building detection. However, the performance of building detection methods based on aerial images alone is typically poorer than that of building detection methods using both LiDAR and image data. To overcome these limitations, we present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching (DIM) point clouds, orthophoto and original aerial images. The proposed framework is divided into three stages. In the first stage, the features from the original aerial image and DIM points are fused to detect buildings and obtain the so-called blob of an individual building. Then, a feature-level fusion strategy is applied to match the straight-line segments from original aerial images so that the matched straight-line segment can be used in the later stage. Finally, a new footprint generation algorithm is proposed to generate the building footprint by combining the matched straight-line segments and the boundary of the blob of the individual building. The performance of our framework is evaluated on a vertical aerial image dataset (Vaihingen) and two oblique aerial image datasets (Potsdam and Lunen). The experimental results reveal 89% to 96% per-area completeness with accuracy above almost 93%. Relative to six existing methods, our proposed method not only is more robust but also can obtain a similar performance to the methods based on LiDAR and images.

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Legion Sementation for Building Extraction From Lidar Based DSM Data

Cited by 7 publications

References 0 publications

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

The Isprs Benchmark on Urban Object Classification and 3d Building Reconstruction

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Extraction of Buildings from Multiple-View Aerial Images Using a Feature-Level-Fusion Strategy

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