Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W1, 71-75, 2013
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W1/71/2013/
doi:10.5194/isprsarchives-XL-4-W1-71-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
06 May 2013
CHARACTERIZING URBAN VOLUMETRY USING LIDAR DATA
T. Santos, A. M. Rodrigues, and J. A. Tenedório e-GEO – Research Centre for Geography and Regional Planning, Faculdade de Ciências Sociais e Humanas (FCSH), Universidade Nova de Lisboa, 1069-061 Lisboa, Portugal
Keywords: Built Volume, Urban indicators, LiDAR, Census data, Density, Land Planning Abstract. Urban indicators are efficient tools designed to simplify, quantify and communicate relevant information for land planners. Since urban data has a strong spatial representation, one can use geographical data as the basis for constructing information regarding urban environments. One important source of information about the land status is imagery collected through remote sensing. Afterwards, using digital image processing techniques, thematic detail can be extracted from those images and used to build urban indicators.

Most common metrics are based on area (2D) measurements. These include indicators like impervious area per capita or surface occupied by green areas, having usually as primary source a spectral image obtained through a satellite or airborne camera. More recently, laser scanning data has become available for large-scale applications. Such sensors acquire altimetric information and are used to produce Digital Surface Models (DSM). In this context, LiDAR data available for the city is explored along with demographic information, and a framework to produce volumetric (3D) urban indexes is proposed, and measures like Built Volume per capita, Volumetric Density and Volumetric Homogeneity are computed.

Conference paper (PDF, 635 KB)


Citation: Santos, T., Rodrigues, A. M., and Tenedório, J. A.: CHARACTERIZING URBAN VOLUMETRY USING LIDAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W1, 71-75, doi:10.5194/isprsarchives-XL-4-W1-71-2013, 2013.

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