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

Building density information is fundamentally important for urban design, planning and management and for urban environmental studies. This paper demonstrates that Building Coverage Ratio (BCR), Floor Area Ratio (FAR), and other building density indicators can be numerically and automatically derived from high-resolution airborne LiDAR data. An object-based method is proposed to process the LiDAR data for the building density information. The method consists of a sequence of numerical operations: generating the normalized Digital Surface Model (nDSM), extracting building objects, deriving object attributes, associating objects with the corresponding land lots, and computing building density indicators at land lot and urban district scales. The algorithms for these operations have been implemented as an ArcGIS extension module. The object-based method is applied to the processing of airborne LiDAR data over downtown Houston. Various attributes have been derived to quantify the building density, urban physical structure, and landscape morphological characteristics of the downtown area at three different spatial scales.

[1]  Hinnik Eggers,et al.  Two Fast Euclidean Distance Transformations in Z2Based on Sufficient Propagation , 1998, Comput. Vis. Image Underst..

[2]  Jianggang Xu,et al.  Estimating floor area ratio based on SRTM3 and land use data , 2007, Geoinformatics.

[3]  T. Oke.  Street design and urban canopy layer climate , 1988 .

[4]  Yasushi Asami,et al.  Evaluating Land-Use Restrictions concerning the Floor Area Ratio of Lots , 2006 .

[5]  Li Zhang,et al.  An object-based two-stage method for a detailed classification of urban landscape components by integrating airborne LiDAR and color infrared image data: A case study of downtown Houston , 2009, 2009 Joint Urban Remote Sensing Event.

[6]  Jefferey A. Shufelt,et al.  Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  C. Somerville,et al.  Site Density Restrictions: Measurement and Empirical Analysis , 2001 .

[8]  Ronald M. Cionco,et al.  High resolution urban morphology data for urban wind flow modeling , 1998 .

[9]  Shu-Ching Chen,et al.  Automatic Construction of Building Footprints From Airborne LIDAR Data , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  H. Swaid,et al.  The role of radiative-convective interaction in creating the microclimate of urban street canyons , 1993 .

[11]  M. A. Weiss,et al.  Skyscraper Zoning: New York's Pioneering Role , 1992 .

[12]  Tatsuhito Kono,et al.  Optimization of floor area ratio regulation in a growing city , 2009 .

[13]  James G. MacKinnon,et al.  Measuring the costs of height restrictions with a general equilibrium model , 1977 .

[14]  Nicolas Moussiopoulos,et al.  Influence of Building Density and Roof Shape on the Wind and Dispersion Characteristics in an Urban Area: A Numerical Study , 2000 .

[15]  Uwe Soergel,et al.  Potential and limits of InSAR data for building reconstruction in built-up areas , 2003 .

[16]  Thomas S. Huang,et al.  Image processing , 1971 .

[17]  Matthieu Cord,et al.  Building Detection and Reconstruction from Mid- and High-Resolution Aerial Imagery , 1998, Comput. Vis. Image Underst..

[18]  K M Grace Wong.  Vertical cities as a solution for land scarcity: the tallest public housing development in Singapore , 2004 .

[19]  H. Liu,et al.  Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods , 2004 .

[20]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[21]  Min-Shun Wang,et al.  ENVIRONMENTAL BEHAVIOUR ANALYSIS OF HIGH-RISE BUILDING AREAS IN TAIWAN , 1998 .

[22]  Jianping Wu,et al.  Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston , 2009 .

[23]  Wolfgang Förstner,et al.  Towards automatic building extraction from high-resolution digital elevation models , 1995 .

[24]  Claus Brenner,et al.  Extraction of buildings and trees in urban environments , 1999 .

[25]  Franz Rottensteiner,et al.  Automatic Generation of High-Quality Building Models from Lidar Data , 2003, IEEE Computer Graphics and Applications.

[26]  G. Priestnalla,et al.  Extracting urban features from LiDAR digital surface models , 2022 .

[27]  Kwong-Wing Chau,et al.  Determining Optimal Building Height , 2007 .

[28]  Dong-Chen He,et al.  A new approach to building identification from very‐high‐spatial‐resolution images , 2009 .

[29]  Yu Bailang.  A Method for Urban Vegetation Classification Using Airborne LiDAR Data and High Resolution Remote Sensing Images , 2010 .

[30]  D. Streutker,et al.  Satellite-measured growth of the urban heat island of Houston, Texas , 2003 .

[31]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[32]  Luc Adolphe,et al.  A Simplified Model of Urban Morphology: Application to an Analysis of the Environmental Performance of Cities , 2001 .

[33]  Han Xue-pei,et al.  A Study on Estimating Urban FAR Based on High-resolution Satellite Images , 2005 .

[34]  Tatsuhito Kono,et al.  Necessity of minimum floor area ratio regulation: a second-best policy , 2010 .

[35]  Primo Zingaretti,et al.  Complete classification of raw LIDAR data and 3D reconstruction of buildings , 2006, Pattern Analysis and Applications.

[36]  Joseph C. Lam,et al.  Shading effects due to nearby buildings and energy implications , 2000 .

[37]  Ruijin Ma.  DEM Generation and Building Detection from Lidar Data , 2005 .

[38]  William Grey,et al.  Urban building height variance from multibaseline ERS coherence , 2003, IEEE Trans. Geosci. Remote. Sens..

[39]  D. Robinson.  Urban morphology and indicators of radiation availability , 2006 .

[40]  Uwe Soergel,et al.  Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Amnon Frenkel,et al.  Spatial Distribution of High-rise Buildings within Urban Areas: The Case of the Tel-Aviv Metropolitan Region , 2004 .

[42]  Zhen-Dong Cui,et al.  Evaluation of the geology-environmental capacity of buildings based on the ANFIS model of the floor area ratio , 2010 .

[43]  H. Feng,et al.  The generation of polygonal outlines of objects from gray level pictures , 1975 .

[44]  Paolo Gamba,et al.  Digital surface models and building extraction: a comparison of IFSAR and LIDAR data , 2000, IEEE Trans. Geosci. Remote. Sens..

[45]  P. Gamba,et al.  Joint analysis of SAR, LIDAR and aerial imagery for simultaneous extraction of land cover, DTM and 3D shape of buildings , 2002 .

[46]  J. Brueckner,et al.  Analyzing building-height restrictions: predicted impacts and welfare costs , 2005 .

[47]  Francis Miguet,et al.  A daylight simulation tool for urban and architectural spaces—application to transmitted direct and diffuse light through glazing , 2002 .

[48]  Bo Sun,et al.  Analyzing the Variation of Building Density Using High Spatial Resolution Satellite Images: the Example of Shanghai City , 2008, Sensors.

[49]  Gerald Mills,et al.  BUILDING DENSITY AND INTERIOR BUILDING TEMPERATURES: A PHYSICAL MODELING EXPERIMENT , 1997 .

[50]  A. Mochida,et al.  Wind tunnel tests on the relationship between building density and pedestrian-level wind velocity: Development of guidelines for realizing acceptable wind environment in residential neighborhoods , 2008 .

[51]  Scott Greer,et al.  Urban renewal and American cities , 1965 .

[52]  Yuh-Tay Liow.  A contour tracing algorithm that preserves common boundaries between regions , 1991, CVGIP Image Underst..

[53]  Borden D. Dent,et al.  Interpreting the City: An Urban Geography , 1980 .