Automated derivation of urban building density information using airborne LiDAR data and object-based method

Yu, Bailang; Liu, Hongxing; Wu, Jianping; Hu, Yingjie; Zhang, Li

doi:10.1016/j.landurbplan.2010.08.004

Cited by 211 publications

(100 citation statements)

References 49 publications

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“…Approaches based on thresholding consist of the application of a threshold value to the nDSM to discriminate buildings and vegetation, combined with a threshold applied to the normalized difference vegetation index (NDVI) image in order to mask the vegetation. Then, buildings can be accepted or rejected according to different conditions, such as size and shape [73][74][75][76], spectral values [77] or texture features [78][79][80]. In addition to these two main approaches, some other specific methods have been reported.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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Abstract:In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

et al. 2011

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“…Separation of buildings and streets by the classifier will allow us to determine where mosquitoes are less likely to find suitable habitat. The benefits of combining multispectral NAIP and LiDAR-derived products to produce highly accurate LULC classifications will reach beyond mosquito habitat research, informing issues such as urban planning [38], urban heat island, and habitat fragmentation.…”

Section: Discussionmentioning

confidence: 99%

Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat

2011

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Abstract:Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues.

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“…It is computed as the difference between the digital surface model (DSM) and the digital elevation model (DEM). In our study, the DSM was generated from the airborne LiDAR point clouds by using the linear triangulated irregular network (TIN) interpolation method [23][24][25][26]. The DEM was then interpolated from the ground points which was classified using a progressive morphological filter [55].…”

Section: Building Contours Generationmentioning

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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Automated derivation of urban building density information using airborne LiDAR data and object-based method

Cited by 211 publications

References 49 publications

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat

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

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