Urban Site Modeling from LiDAR

You, Suya; Hu, Jinhui; Neumann, Ulrich; Fox, Pamela

doi:10.1007/3-540-44842-x_59

Cited by 53 publications

(40 citation statements)

References 4 publications

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“…Aerial and ground-based LiDAR data is being used to create virtual cities [25,10,16], terrain models [23], and classify different vegetation types [3]. Typically, these datasets are quite large and require some sort of automatic processing.…”

Section: Introductionmentioning

confidence: 99%

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Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Lodha

Kreps

Helmbold

et al. 2006

Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)

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Section: Introductionmentioning

confidence: 99%

“…In relatively flat regions which contain few trees this may yield reasonable results, e.g. the USC campus [25]; however in areas which are forested or highly-sloped, labor-intensive manual input and correction is essential with current methods in order to obtain an useful classification.…”

Section: Introductionmentioning

confidence: 99%

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Lodha

Kreps

Helmbold

et al. 2006

Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)

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show abstract

“…Previous studies showed that the evaluation of methods for 3D building reconstruction is a challenging task [1,8,9,27,64]. Nevertheless, there are still some simple indices that can be used for validation, such as the distance of the input points to the 3D model.…”

Section: Performancementioning

confidence: 99%

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

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“…Most of the demand for these urban models is centered around the creation of navigable 3D virtual models of cities [18] [8] [24].…”

Section: Previous Workmentioning

confidence: 99%

“…Kim and Nevatia [13] propose a similar method but instead they use Bayesian networks to combine edge detection information from multiple images to determine probable building walls. You et al [24] models buildings by means of a user-driven combination of primitive roof shapes that are assembled to form complex building models. We prefer to work only from LIDAR data and with as little manual input as possible.…”

Section: Previous Workmentioning

confidence: 99%

A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data

Wang

Lodha

Helmbold

2006

Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)

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Building footprints have been shown to be extremely useful in urban planning, infrastructure development, and roof modeling. Current methods for creating these footprints are often highly manual and rely largely on architectural blueprints or skilled modelers. In this work we will use aerial LIDAR data to generate building footprints automatically. Existing automatic methods have been mostly unsuccessful due to large amounts of noise around building edges. We present a novel Bayesian technique for automatically constructing building footprints from a pre-classified LI-DAR point cloud. Our algorithm first computes a boundederror approximate building footprint using an application of the shortest path algorithm. We then determine the most probable building footprint by maximizing the posterior probability using linear optimization and simulated annealing techniques. We have applied our algorithm to more than 300 buildings in our data set and observe that we obtain accurate building footprints compared to the ground truth. Our algorithm is automatic and can be applied to other man-made shapes such as roads and telecommunication lines with minor modifications.

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Urban Site Modeling from LiDAR

Cited by 53 publications

References 4 publications

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data

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