A Case Study about the Use of Voxelization Technique for Urban Afforestation 3D Generalization in ALS Data
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Abstract
Reducing computational effort for extraction of information from three-dimensional (3D) geospatial data, one option is to perform 3D generalization. Featuring a subjective process of data derivation in which criteria and rules are established, 3D generalization allows to create compact and realistic models for 3D representation, impacting lower data storage, visualization and analysis requirements. In this context, the present paper aims to evaluate use of voxelization technique as a method to generalize delimited urban trees in LiDAR (Light Detection and Ranging) point cloud. Acquired on an airborne platform, data used refer to Pituba district in Salvador city, Bahia state, have an average density of 8 points/m2. In order to delimit trees, data were filtered and classified by processing routines implemented in pointcloud softwares. For voxelization, four different minimum cell dimensions were defined: 0.5 m, 1 m, 1.5 m and 2 m. The obtained results showed decrease in number of elementary units and reduction in computational storage space, kept efficiency for extraction of information about delimited trees location and spatial distribution. Voxelization is thus validated as an effective 3D generalization method for urban afforestation from ALS data.
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