Creation of a 3D Urban GIS Database: Data Fusion Approach Technical Session on "Photogrammetry and 3D Visualization"


Commerce City-Denver Acquisition and Processing
The Commerce City Denver study area included the simultaneous acquisition of high-resolution LIDAR data using Spectrum's RAMS LIDAR system, which provided 1.0-meter resolution point spacing data, the acquisition of 0.5-foot pixel Digital Color imagery and the collection of hyperspectral imagery using the SPECTIR hyperspectral system. The Commerce City area is characterized as an industrial urban zone and is dominated by industrial warehouses and structures, paved urban streets and highways, and to the west a large residential subdivision. Vegetation cover is strictly urban in nature, primarily dominated by individual trees and some small clusters of riparian tree cover along the riverine area.

The Color Digital Camera imagery was collected a pixel resolution of 0.5-ft, and was used as the cartographic base from which both the LIDAR and hyperspectral imagery were tied in terms of their base coordinate projection system. The digital color imagery was orthophoto processed using the LIDAR data as the DEM base and laid out in a project-tiling scheme and with the resultant 9 orthophotos being mosaiced to form the final project ortho base.

Once the ortho base was generated feature extraction was initiated using the RAMS LIDAR data, which has a spatial resolution of 1.0-meter point spacing. This included the extraction of the following earth surface features:
  • Bare Earth surface generation
  • Building footprint generation
  • Vegetation canopy generation
Features were extracted using Spectrum's LID-MAS software using a TIN and Fast Fourier Filter (FFT) in combination. The resultant data included extracted bare-earth surface, buildings and vegetation (trees) in a LAS elevation point cloud format, figure 5. In this case most buildings were clearly well defined due to their size and rectilinear morphology. Vegetation cover was relatively sparse with little overhand existing on buildings with the exception of the residential areas.


Figure 5. (Left) Raw Reflectance LIDAR, (right) TIN/FFT filtered extracted bare-earth surface, buildings and vegetation elevation point clouds.

The LAS extracted elevation point cloud features were then converted to their appropriate feature formats in which they would be delivered. The bare earth surface elevation points were first converted to an ESRI GRID format using an IDW interpolation algorithm and then exported into the ARA deliverable format, which is a 32-bit GeoTIFF file.

LIDAR did an excellent job in defining and capturing tree canopies. Tree canopies were relatively sparse in this urban environment and mainly confined to residential and riparian areas. Tree Canopy point clouds were converted to ArcView Shape files in three distinct formats:
  • Individual Tree Points
  • Individual Trees as polygons
  • Tree Clusters (Riparian Forest) as Polygons
The hyperspectral imagery was used to define and classify the following classes of features:
  • Water bodies (rive and man-made ponds)
  • Building rooftop Material
  • Road Pavement (Asphalt)
The hyperspectral imagery used was captured using a SPECTIR 63 band system that captured imagery at a pixel resolution of 1.0-meters. Samples of this imagery can be reviewed in figure 11. Features were classified using image-processing techniques in the form of a semi-controlled unsupervised classification. All classes were verified and examined in detail using the derived class statistics and associated continuous spectral curves. All hyperspectral-classified data was converted from a raster classification to ArcView shape files. A semi-controlled unsupervised classification was then generated that produced three main roof type materials recognizable in that area, figure 6 and 7:
  • Asphalt-tar
  • Gravel-tar
  • Metallic Roofs
All extracted feature data was converted to an ArcView Shapefile format and attributed accordingly as dictated by ARA for use in the Terrex Software system. Figure 8 shows the classification as a 3D perspective.

In Summary the fusion of high-resolution Digital Camera Imagery, LIDAR and hyperspectral data proved to be highly effective in mapping both urban and rural areas. Such data allowed the user to develop land cover classification maps that included a highly accurate cartographic base, a digital terrain model and a material classification map. Such processing techniques allowed the ready development of a 3D urban and rural database with associated feature classes, material composition attribution, linked to highly accurate x,y positional and elevation information at the pixel level of 1.0-meter resolution.


Figure 6. (left) Color Digital Camera data of building sample area. (right) LIDAR building footprint masked and extracted hyperspectral imagery of building rooftops.


Figure 7. Hyperspectral building rooftop classification (left). (Right) Continuous spectral curves of rooftops showing spectral endmembers (Metallic - Asphalt).


Figure 8. Final Commerce City Area extracted Land Cover Features. This includes LIDAR extracted buildings, and tree cover; and Hyperspectral extracted, non-tree vegetation cover, road-asphalt and water classes and digitized road and railroad centerlines.

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