Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds

Tran, Trung Hieu; Ressl, Camillo; Pfeifer, Norbert

doi:10.3390/s18020448

Cited by 70 publications

(70 citation statements)

References 65 publications

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“…In recent years, studies on building extraction, reconstruction and change detection have made significant advances and a wide range of methods have been proposed on façade segmentation and opening area detection [2], building extraction [3][4][5], change detection and map database update [1,[6][7][8], roof plane extraction [9] and 3-D reconstruction [10]. A number of techniques [11,12] have also been proposed for evaluation of these methods.…”

Section: Related Workmentioning

confidence: 99%

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An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

2018

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Three-dimensional (3-D) reconstruction of building roofs can be an essential prerequisite for 3-D building change detection, which is important for detection of informal buildings or extensions and for update of 3-D map database. However, automatic 3-D roof reconstruction from the remote sensing data is still in its development stage for a number of reasons. For instance, there are difficulties in determining the neighbourhood relationships among the planes on a complex building roof, locating the step edges from point cloud data often requires additional information or may impose constraints, and missing roof planes attract human interaction and often produces high reconstruction errors. This research introduces a new 3-D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. It identifies any missing planes through an analysis using the 3-D plane intersection lines between adjacent planes. Then, it generates new planes to fill gaps of missing planes. Finally, it obtains complete building models through insertion of approximate wall planes and building floor. The reported research in this paper then uses the generated building models to detect 3-D changes in buildings. Plane connections between neighbouring planes are first defined to establish relationships between neighbouring planes. Then, each building in the reference and test model sets is represented using a graph data structure. Finally, the height intensity images, and if required the graph representations, of the reference and test models are directly compared to find and categorise 3-D changes into five groups: new, unchanged, demolished, modified and partially-modified planes. Experimental results on two Australian datasets show high object-and pixel-based accuracy in terms of completeness, correctness, and quality for both 3-D roof reconstruction and change detection techniques. The proposed change detection technique is robust to various changes including addition of a new veranda to or removal of an existing veranda from a building and increase of the height of a building.

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Section: Related Workmentioning

confidence: 99%

“…Raw LiDAR data or LiDAR-derived Digital Surface Model (DSM) is also used as the only source of information to detect the 3-D building changes [6,23]. Tran et al [6] proposed a method where, in addition to the ground and tree, they classified buildings into new, demolished and unchanged types.…”

Section: -D Building Change Detectionmentioning

confidence: 99%

An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

2018

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“…(Scaioni et al, 2013), machine learning (Tran et al, 2018) and iterative closest point (ICP) algorithm (Zhang et al, 2015). Point to triangle method tackles irregular point density and occlusion problems, also eliminates false detections on penetrable objects.…”

Section: Introductionmentioning

confidence: 99%

“…The grid anomalies have been compared to detect the changes in time series measurement data (Scaioni et al, 2013). Tran et al (2018) exploited machine learning technique to object classification and change detection in 3D data. The study area was defined in eight classes, and the final result was achieved an overall accuracy of over 90% for both epochs of eight classes.…”

Section: Introductionmentioning

confidence: 99%

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Urban Area Change Detection Using Time Series Aerial Images

Altuntas

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The urban area should be imaged in three-dimensional (3D) for planning, inspection and management. In addition fast urbanisation requires detection the urban area changes which have been occurred with new buildings, additional floor to current buildings and excavations. 3D surface model of urban area enables to extracting high information from them. On the other hand high density spatial data should be measured to creating 3D digital terrain surface model. The dense image matching method makes 3D measurement with high density from the images in a short time. The aim of this study is detection the urban area changes via comparison of time series point cloud data from historical stereoscopic aerial images. The changes were detected with the difference of these digital elevation models. The study area was selected from Konya city in Turkey, and it has a large number of new buildings and changes in topography. Dense point cloud data were created from historical aerial images belong to years of 1951, 1975 and 2010. Every threedimensional point cloud data were registered to global georeferenced coordinate system with ground control points created from the imaged objects such as building corner, fence, wall and etc. Then urban changes were detected with comparing the dense point cloud data by exploiting the iterative closest point (ICP) algorithm. Consequently, the urban changes were detected from point to surface distances between image based point clouds.

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3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

Gao,

Guo,

Wang

et al. 2024

Advcd Theory and Sims

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Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.

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Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds

Cited by 70 publications

References 65 publications

An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

An Effective Data-Driven Method for 3-D Building Roof Reconstruction and Robust Change Detection

Urban Area Change Detection Using Time Series Aerial Images

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

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