Segmentation and classification of airborne laser scanner data for ground and building detection

Tolt, Gustav; Persson, Åsa; Landgård, Jonas; Söderman, Ulf

doi:10.1117/12.665451

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…Although most of the segmentation method used in geospatial and building-related applications are based on 2D segmentation (Mian et. al (2006) ;Tolt et. al (2006); Miliaresis and Kokkas (2007); Sampath and Shan (2010) ;Cheng et.…”

Section: D Segmentation On 3d Buildingsmentioning

confidence: 98%

Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

Sharkawi

Abdul-Rahman

2013

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

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(khafiz4, alias)@utm.my KEY WORDS: 3D City Models, CityGML, Semantic Updating, 3D Model Segmentation. ABSTRACT:Cities and urban areas entities such as building structures are becoming more complex as the modern human civilizations continue to evolve. The ability to plan and manage every territory especially the urban areas is very important to every government in the world. Planning and managing cities and urban areas based on printed maps and 2D data are getting insufficient and inefficient to cope with the complexity of the new developments in big cities. The emergence of 3D city models have boosted the efficiency in analysing and managing urban areas as the 3D data are proven to represent the real world object more accurately. It has since been adopted as the new trend in buildings and urban management and planning applications. Nowadays, many countries around the world have been generating virtual 3D representation of their major cities. The growing interest in improving the usability of 3D city models has resulted in the development of various tools for analysis based on the 3D city models. Today, 3D city models are generated for various purposes such as for tourism, location-based services, disaster management and urban planning. Meanwhile, modelling 3D objects are getting easier with the emergence of the user-friendly tools for 3D modelling available in the market. Generating 3D buildings with high accuracy also has become easier with the availability of airborne Lidar and terrestrial laser scanning equipments. The availability and accessibility to this technology makes it more sensible to analyse buildings in urban areas using 3D data as it accurately represent the real world objects. The Open Geospatial Consortium (OGC) has accepted CityGML specifications as one of the international standards for representing and exchanging spatial data, making it easier to visualize, store and manage 3D city models data efficiently. CityGML able to represents the semantics, geometry, topology and appearance of 3D city models in five well-defined Level-of-Details (LoD), namely LoD0 to LoD4. The accuracy and structural complexity of the 3D objects increases with the LoD level where LoD0 is the simplest LoD (2.5D; Digital Terrain Model (DTM) + building or roof print) while LoD4 is the most complex LoD (architectural details with interior structures). Semantic information is one of the main components in CityGML and 3D City Models, and provides important information for any analyses. However, more often than not, the semantic information is not available for the 3D city model due to the unstandardized modelling process. One of the examples is where a building is normally generated as one object (without specific feature layers such as Roof, Ground floor, Level 1, Level 2, Block A, Block B, etc). This research attempts to develop a method to improve the semantic data updating process by segmenting the 3D building into simpler parts which will make it easier for the users to select and update the semantic information. The me...

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Section: D Segmentation On 3d Buildingsmentioning

confidence: 98%

Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

Sharkawi

Abdul-Rahman

2013

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

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“…We use a technique similar to the one proposed in [13], the main difference being that it now operates on non-interpolated data, i.e. can cope with missing data The input to the algorithm is a gridded elevation image.…”

Section: Detection Of Vertical Structuresmentioning

confidence: 99%

Registration and change detection techniques using 3D laser radar data from natural environments

et al. 2006

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In this paper, we present techniques related to registration and change detection using 3D laser radar data. First, an experimental evaluation of a number of registration techniques based on the Iterative Closest Point algorithm is presented. As an extension, an approach for removing noisy points prior to the registration process by keypoint detection is also proposed. Since the success of accurate registration is typically dependent on a satisfactorily accurate starting estimate, coarse registration is an important functionality. We address this problem by proposing an approach for coarse 2D registration, which is based on detecting vertical structures (e.g. trees) in the point sets and then finding the transformation that gives the best alignment. Furthermore, a change detection approach based on voxelization of the registered data sets is presented. The 3D space is partitioned into a cell grid and a number of features for each cell are computed. Cells for which features have changed significantly (statistical outliers) then correspond to significant changes.

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“…These can be divided into several categories, including methods based on mathematical morphology [4]- [7], linear prediction [8], [9], progressive triangulated irregular network (TIN) [10], [11], and segmentation [12], [13]. Sithole and Vosselman [2] tested several algorithms and determined that none could process every type of terrain well.…”

Section: Introductionmentioning

confidence: 99%

Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

Zhang

2013

IEEE Geosci. Remote Sensing Lett.

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The fast filtering of massive point cloud data from light detection and ranging (LiDAR) systems is important for many applications, such as the automatic extraction of digital elevation models in urban areas. We propose a simple scan-line-based algorithm that detects local lowest points first and treats them as the seeds to grow into ground segments by using slope and elevation. The scan line segmentation algorithm can be naturally accelerated by parallel computing due to the independent processing of each line. Furthermore, modern graphics processing units (GPUs) can be used to speed up the parallel process significantly. Using a strip of a LiDAR point cloud, with up to 48 million points, we test the algorithm in terms of both error rate and time performance. The tests show that the method can produce satisfactory results in less than 0.6 s of processing time using the GPU acceleration.

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Segmentation and classification of airborne laser scanner data for ground and building detection

Cited by 18 publications

References 6 publications

Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

Improving Semantic Updating Method on 3d City Models Using Hybrid Semantic-Geometric 3d Segmentation Technique

Registration and change detection techniques using 3D laser radar data from natural environments

Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

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