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Project Objective — The project objective is to generate accurate 2.5 dimension and true 3 dimensional models of the University of Texas at Dallas campus utilizing digital orthophotos and LIDAR data sets useful for Geospatial Analysis. — Interoperability between newly developed software such as SketchUp and ESRI’s ArcView allowed for a detailed development of a digital surface model. The accuracy of the model was assessed via a laser range finder.
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MASTER PROJECT PRESENTED BY:
LAURENCE P. CLINTON
ADVISER:DR. AIKEN
2.5 and 3D Digital Surface Model Development
Introduction
The use of LIDAR data, in combination with digital orthophotos provides an accurate means to model real world buildings
The human experience in viewing objects in three dimensions allows us to recognize features of buildings and other objects by shape, depth, or their variation in height.
Different methodologies have been used to generate three-dimensional models. However, for precise measurements and quality results studies have shown that utilizing both multi-spectral images in combination with LIDAR data yield better results for extractions of buildings.
Recent development in software such as interoperability between ArcMap and 3D modeling software SketchUp allow for detailed development of DSM. Additionally, it allows GIS users the ability to migrate from 2.5 dimension Digital Surface Models to true 3D Digital Surface Models.
Project Objective
The project objective is to generate accurate 2.5 dimension and true 3 dimensional models of the University of Texas at Dallas campus utilizing digital orthophotos and LIDAR data sets useful for Geospatial Analysis.
Interoperability between newly developed software such as SketchUp and ESRI’s ArcView allowed for a detailed development of a digital surface model. The accuracy of the model was assessed via a laser range finder.
Literature Review
Santos, Antonio M. G. Tommaselli, and Quintino Dalmolin study compared classical methods for generating digital surface model via stereo image matching to extract buildings with laser scanning technology.
With stereo image matching, shadows and other effects caused a smoothing effect in the 3D data and proved difficult for the generation of algorithms for due to different slope angles of building roofs.
The laser scanner technology, however, provided accurate, dense, and reliable information about the object’s surface, which allowed edges to be detected for building modeling
Literature Review
Halla and Brenner (1999) study showed that for precise measurements the utilization of both multi-spectral images in combination with LIDAR data yielded better results for extractions of buildings, better discrimination in trees and provided an accurate means for developing DSMs.
Ibrahim (2005) utilized LIDAR data and digital orthos to generate a DSM city model for the city of Dallas. The research evaluated and compared different methods of automatic feature extraction either from LIDAR or the combination of LIDAR and digital orthophotos in a GIS environment.
Her results concluded that methods utilizing elevation data from LIDAR data in form of a normalized DSM and spectral and edge information from digitalorthos for building extraction yielded the best results.
Literature Review
Alistair Ford (2007)evaluated the use of 3D modeling in a geodatabase using multipatch feature classes for the oil and gas industry. The multipatch was found to be of use for pipelines that cannot be analyzed in 2D.
For assessment purposes, Abkinfenwa (2005 MS Project) utilized various scanning technologies such as laser scanner, and laser range finder to assess building heights.
Data Sources
LIDAR Reflective LIDAR data (2001) in the form of an ASCII file was
purchased from North Central Texas Council of Government. Average point cloud distance was 12 ft.
LIDAR (Light Detection And Ranging) is a remote-sensing technique that uses a laser light source below an aircraft in form of pulses, to measure the distance, and speed of objects. LIDAR data is georeferenced from onboard GPS equipment, scan angle of the LIDAR, and laser pulse time. Information about the object is derived from back-scattered reflectance from the object back to the sensor located underneath the aircraft.
2 foot Contour Lines developed from LIDAR data
Data Sources
Digital Orthos Digital Orthos (2003 & 2005) 2005 six inch resolution from Collin County 2003 six inch resolution provided by North Central Texas Council of
Government. A digital ortho is a raster file that has distortions removed from the
image caused by the aircraft’s flight, as well as the removal of terrain distortions by using a digital elevation model (DEM).
All of the above will be used to generate the following below: 3DS and VRML files generated from SketchUp Shape Files
Generated via ArcCatalog, edited in ArcMap Multipatch files stored in Geodatabase
Digital Ortho: 2005 + 2003
0 1,000 2,000 3,000 4,000500Feet
Software
ESRI ArcMap – 2D development and editing of shape files Spatial Analyst 3D Analyst
ESRI ArcScene – 2.5D and 3D development
Corel Photoshop Pro Sketch Up
3D modeling Photo editing 3ds conversion
Equipment
Laser Atlanta Advantage Cl Reflectorless Laser Range finder Used to obtain building heights and assess the
accuracy of the Lidar DSM
Cannon Power Shot A560 7.1 megapixel Used to capture all building on UTD campus
Methodology and Analysis
Raster Layer Vector Layer
Raster Lidar Layers
Reflective Bare Earth Height Layer
Digital Orthos
Vector TIN Building foot prints
Shape files
Georeferenced to DOQ Multipatch files in
Geodatabase
Raster and Vector Preprocessing
Converting Data Sets to Grids
LIDAR Conversion Convert ASCII reflective data to a GRID via IDW
(Inverse Distance Weighted Interpolation) Utilize to extract z-values
Convert 2 ft contour to TIN Convert contour TIN to GRID and utilize as Bare
Earth Model
Raster Preprocessing: Bare Earth
Bare Earth Result
Lidar Contour TIN Bare Earth Lidar Grid
Reflective Lidar Grid
Reflective Lidar Grid
IDW Layer: Reflective Grid
Difference Grid (Building Height)
Vector Processing: Create New Data Set
Generate ESRI Shape files
Edit according to DOQs in order to generate building footprint
Vector Processing:Create Building Footprints
Calculation of Attribute Fields
BUILD_CODE AREA PERIM LENGTH Ground Height
Calculate via raster subtraction
Z-VALUE IDW Grid
VB Script for Calculating Fields
AREA Dim dblArea as double Dim pArea as Iarea Set pArea = [shape] dblArea = pArea.area
PERIMETER Dim dblPerimeter as double Dim pCurve as Icurve Set pCurve = [shape] dblPerimeter = pCurve.Length
LENGTH Dim dblLength as double Dim pCurve as ICurve Set pCurve = [shape] dblLength = pCurve.Length
Calculating fields
Development of DSM in ArcScene
ArcScene for 2.5D Utilized Bare Earth LIDAR
grid for base layer Convert shape files to 3D
shape files via ESRI 3D Analyst
Overlay Digital Ortho Overlay shape files Edit shape files according
to building structure Extrude shape files to
building height attribute determined from difference grid
0 550 1,100 1,650 2,200275Feet
UTD 2.5D DIGITAL SURFACE MODEL
NAD83 State Plane Lambert Conformal
3D Development
Migration toward 3D SketchUp
3D modeling software now interoperable with ArcView 9.2
ArcView 9.2 3D conversion tool from .3ds or vrml Multipatch feature classes loaded into geodatabase
3D Workflow
ArcScene •Export ArcScene 2.5D model into SketchUp
SketchUp •Edit, photofinish, and add textures•Covert to .3ds or vrml file
ArcTool •Convert .3ds or vrml to multipatch•Load into geodatabase
Export 2.5 D into SketchUp
3DModels edited and photo-finished in Sketch Up
Considerations: Editing according to bare earth
Capturing Differences from Bare Earth Difference in building
heights due to ground elevations changes that were not intuitively obvious from 2D perspective had to be accounted for in editing
3D Models
Campus View
Conversion to multipatch
From SketchUp files can be saved in .3ds or .vrml format
Utilizing 3D conversion tool bar in ArcMap 9.2 files are then imported and converted into multipatch format and placed in a geodatabase.
Multipatch files in geodatabase in ArcScene
Campus view NSERL
Multipatch filesNatural Science and Research Laboratory Callier Richardson
True 3D Multipatch vrs 2.5D shapefile
True 3D allowing for side view hole in Ida Green Center building
2.5 D Ida Green Center building
Accuracy Assessment
The Atlanta Laser Range finder was used to determine building heights to assess accuracy of the Lidar Model.- Accuracy +- 10 cm
Accuracy Assessment Results
Corners of buildings were scanned for height and compared to LidarData model.
Standard Deviation = 2.388 ftMean Difference = -.902 ftMax difference = -10.15 ft
AB AS BE BK EP FN GC GR JO MC NLWST
C NB
-20
-10
0
10
20
30
40
50
60
Lidar
LidarLaserDifference
Results and Conclusions
Based on the accuracy assessment, an accurate digital representation in the form of 2.5D and 3D can be made of campus settings from Lidar data sets and digital orthos
For accurate representation of the side view of buildings, 3D models are needed. In this study, .3ds, and multipatch files were generated for this purpose
Conclusions
Assessment Both 2.5 and 3D Digital Surface Models of the UTD campus were
generated utilizing digital orthophotos, LIDAR data sets, and shape files.
The use of LIDAR data, in combination with digital orthophotos was shown to provide an accurate means to model real world buildings. The average difference between the laser range finder and the lidar data set was 0.902 feet.
Conclusions
Contributions The development of a 3D model of the UTD campus in a multipatch
feature class located in a geodatabase is unique. Calculated area, and perimeter via VB scripts for buildings Furthermore, assessing the results of Lidar with a Laser range finder
provides confidence to other analysts with regard to development of 3D models
Conclusions
Future Research For an even more accurate DSM several ground
control points could be taken at the corners of all buildings and center points on the campus to create an accurate bare earth model
Acknowledgements
Office of Strategic PlanningNCTCOG for dataSpecial thanks to Dr. Aiken and his
Cybermapping laboratory Tarig Ahmed Mohammed Alfarhan Lionel White
References
http://rst.gsfc.nasa.gov/Sect11/Sect11_1.html
Cartography and Geographic Information Science, Semi-Automatic Modeling of Buildings from Digital Surface Models Daniel R. dos Santos, Antonio M. G. Tommaselli, and Quintino Dalmolin Vol. 31, No. 3, 2004,
pp. 179-187 N. Haala, C. Brenner, “Extraction of building and trees in urban environments,” ISPRS Photogrammetry and Remote
Sensing, vol. 54 pp. 339-346, 1999. Ibrahim, S. 2005. Feature extraction and 3D city modeling using airborne LIDAR and high-resolution digital
orthophotos: a comparative study. GIS Master thesis. Geographic Information Sciences. The University of Texas at Dallas. http://charlotte.utdallas.edu/mgis/prj_mstrs/2005/Fall/Ibrahim/Sulafa%20Ibrahim_MastersWebsite_Fall2005/index.htm
Lidar Elevation Data for Surface Hydrologic Modeling: Resolution and Representation Issues Christopher P. Barber and Ashton Shortridge Cartography and Geographic Information Science, Vol. 32, No. 4, 2005, pp. 401-410
Christopher P. Barber and Ashton Shortridge ,”Lidar Elevation Data for Surface Hydrologic Modeling: Resolution and Representation Issues” Cartography and Geographic Information Science, Vol. 32, No. 4, 2005, pp. 401-410
Daniel R. dos Santos, Antonio M. G. Tommaselli, and Quintino Dalmolin, Cartography and Geographic Information Science, Semi-Automatic Modeling of Buildings from Digital Surface Models Vol. 31, No. 3, 2004, pp. 179-187
Jenson, John. Remote sensing of the environment: an earth resource perspective. Prentice Hall. 2000, p.326-329 Arc User (2007 Vol 19) “Visualizing Integrated Three-Dimensional Data Sets: Modeling in the geodatabase using
multipatch feature. Ford, Alistair
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