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Author Kokkas, N. ♦ Smith, M.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword City Modelling ♦ Importance Quality Assurance Technique ♦ Lidar Data ♦ Lidar Point Cloud ♦ Vertical Accuracy ♦ Surrounding Region ♦ Neighbouring Plane ♦ Linear Segment ♦ Final Refinement ♦ Squares-plane Fitting Algorithm ♦ Automated Building Reconstruction ♦ Telecommunication Network Planning ♦ Leica Ads40 ♦ Leica Headquarter Facility ♦ Reference Building Model ♦ Reference Dsms ♦ Raster Digital Surface Model ♦ Robust Method ♦ Building Outline ♦ Boolean Operation ♦ Vertical Accuracy Assessment ♦ Aerial Digital Imagery ♦ Statistical Parameter ♦ Stereo Model ♦ Overall Quality ♦ Mean Vertical Shift Of-14cm ♦ Robust Estimation ♦ Solid Feature ♦ Filtered Stereo ♦ Urban Planning ♦ Flight Simulation ♦ New Method ♦ Airborne Optical Data ♦ Building Detection Percentage ♦ Roof Reconstruction ♦ Least Square Adjustment ♦ Vehicle Navigation ♦ Building Hypothesis ♦ Standard Deviation ♦ Urban Area
Abstract Building reconstruction is essential in applications such as urban planning, telecommunication network planning, flight simulation and vehicle navigation which are of increasing importance in urban areas. This paper introduces a new method for automated building reconstruction by fusing airborne optical data with LiDAR point clouds. The data consists of aerial digital imagery acquired with the Leica ADS40, and LiDAR data from the ALS50, representing Leica’s headquarter facilities in Heerbrugg, Switzerland and the surrounding region. The method employs a semi automated technique for generating the building hypothesis by fusing LiDAR data with stereo matched points extracted from the stereo model. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares adjustment. The roof reconstruction is achieved by implementing a least squares-plane fitting algorithm on the LiDAR point cloud and subsequently neighbouring planes are merged using Boolean operations for the generation of solid features. The report proposes a robust method for the estimation of the vertical accuracy that includes the generation of raster Digital Surface Models for each building. With the use of DSMs, functions such as overlaying and subtraction with the reference DSMs can provide a robust estimation of the vertical accuracy using statistical parameters. The assessment is particularly encouraging with the building detection percentage of 96 % and the overall quality in the range of 89-90%. Based on the reference building models a vertical accuracy assessment is performed, for a selection of 17 buildings, indicating a mean vertical shift of-14cm and a standard deviation of 45cm.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article