1. MASTER PROJECT PRESENTED BY: LAURENCE P. CLINTON ADVISER: DR. AIKEN 2.5 and 3D Digital Surface Model Development

2. 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.

3. 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.

4. 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

5. 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.

6. 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.

7. 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

8. 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

9. Digital Ortho: 2005 + 2003

10. 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

11. 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

12. Methodology and Analysis 3D Development SketchUpMultipatch files in Geodatabase 2.5 D Development in ArcScene Bare Earth BaseShape File Extrusion Preprocessing Raster LayersVector Layers

13. 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

14. 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

15. Raster Preprocessing: Bare Earth Bare Earth Grid Lidar Contour Conversion to TIN TIN interpolation To Grid Bare Earth Grid

16. Bare Earth Result Lidar Contour TIN Bare Earth Lidar Grid

17. Reflective Lidar Grid Reflective Lidar Grid ASCII Lidar Point Cloud Inverse Distance Weighted Interpolation Lidar Grid

18. Reflective Lidar Grid IDW Layer: Reflective Grid

19. Difference Grid (Building Height) Building Height Layer Grid Lidar Reflective Grid Spatial Analyst: Raster Subtraction Bare Earth Grid

20. Vector Processing: Create New Data Set — Generate ESRI Shape files — Edit according to DOQs in order to generate building footprint

21. Vector Processing: Create Building Footprints

23. 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

24. Calculating fields

25. 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

26. UTD 2.5D DIGITAL SURFACE MODEL NAD83 State Plane Lambert Conformal

27. 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

28. 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

29. Export 2.5 D into SketchUp

30. 3DModels edited and photo-finished in Sketch Up

31. 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

32. 3D Models Campus View

33. 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.

34. Multipatch files in geodatabase in ArcScene Campus view NSERL

35. Multipatch files Natural Science and Research Laboratory Callier Richardson

36. 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

37. Accuracy Assessment The Atlanta Laser Range finder was used to determine building heights to assess accuracy of the Lidar Model. - Accuracy +- 10 cm

38. Accuracy Assessment Results Corners of buildings were scanned for height and compared to Lidar Data model. Standard Deviation = 2.388 ft Mean Difference = -.902 ft Max difference = -10.15 ft

39. 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

40. 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.

41. 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

42. 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

43. Acknowledgements Office of Strategic Planning NCTCOG for data Special thanks to Dr. Aiken and his Cybermapping laboratory  Tarig Ahmed  Mohammed Alfarhan  Lionel White

44. 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