Automatic building detection is important for 3D city modeling.Airborne light detection and ranging(LIDAR) point cloud data are dense

georeferenced as well as 3D

they are the natural choice for 3D object detection and extraction

e.g.

building.Point cloud

raster grid

and triangulated irregular network(TIN)

which are the commonly used methods to represent scattered LIDAR point cloud data for building detection

have defects;for example

their model representations are complex

and thus using the data processing algorithm is difficult

and the results are not accurate and unable to represent multiple returns LIDAR data.To overcome the restrictions of existing point-

grid-

and TIN-based approaches

this paper focuses on "establishing voxel structure model for airborne LIDAR point cloud data and developing a new building detection algorithm based on the constructed voxel model" and proposes a Voxel-based building detection(VBD) algorithm for separating buildings from non-buildings. The proposed VBD algorithm consists of three steps.First

LIDAR point clouds is regularized into binary 3D voxel structure

which can be obtained by dividing the entire scene volume into a 3D regular grid(the 3D sub-volumes

called voxels)

remapping the LIDAR points to 3D voxels and assigning the voxel value 1 or 0 when a voxel contains LIDAR points or not.Second

the voxels with voxel value 1 are separated into ground and unground voxels utilizing a voxel-based 3D filtering algorithm.Third

a group of non-ground voxels with almost straight line and jump features are selected as building edge seed voxels and then their 3D connected set are labeled as building voxels.The proposed algorithm is based on the idea of 3D connectivity construct and is designed based on a binary voxel structure which is a simpler 3D structure

in which topological and adjacent relations between voxels can be established much easier.The advantage of the proposed algorithm lies in utilizing connectivity and hidden elevation information between voxels. ISPRS urban LIDAR datasets

which are representative of buildings of diverse types

are used to analyze the sensitivity of "adjacency size" parameter in the model and assess the accuracy of VBD algorithm quantitatively.The quantitative evaluation results indicate that:1) the 56-adjacency is the optimal adjacent size;2) an over 95% average quality of building-detection

achieving 95.61% average completeness and 95.97% average correctness

which report promising performance.The qualitative evaluation results indicate that large

dense

and irregularly shaped buildings or buildings with eccentric roofs are all successfully captured.The outstanding merit of the VBD algorithm based on the idea of 3D connected set is that some roof elements(e.g.

small dormers

chimneys) can also be detected. The proposed building detection algorithm of 3D connected-component label can effectively detect all kinds of buildings

especially urban buildings.The detected building results can directly serve as building model that is a new form of 3D voxel model with certain accuracy and are easy to build a 3D building model.However

the proposed algorithm suffers from separating trees and buildings if both are adjacent to each other

thereby forming a 3D connected set.The reason for this is that the regularized binary 3D voxel data cannot integrate with multispectral

hyperspectral

or other optical data source.Optical images can provide a variety of information

such as intensity

colours and textures

which can provide detailed information for preventing the spreading of the building-regions throughout the neighboring objects

and thus further improving the accuracy of building detection.Nevertheless

we could conclude that the proposed algorithm is promising for automatic building detection.