Patent Application
20080021683
The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and completer and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout.
Referring initially to
By way of example, the geospatial data may be captured using various techniques such as stereo optical imagery, Light Detecting and Ranging (LIDAR), Interferometric Synthetic Aperture Radar (IFSAR), etc. Generally speaking, the data will be captured from overhead (e.g., nadir) views of the geographical area of interest by airplanes, satellites, etc., as will be appreciated by those skilled in the art. However, oblique images of a geographical area of interest may also be used in addition to (or instead of) the nadir images to add additional 3D detail to a geospatial model. The raw image data captured using LIDAR, etc., may be processed upstream from the geospatial model database 21 into a desired format, such as a digital elevation model (DEM), or this may be done by the processor 22.
Turning now to
More particularly, in the example shown in
To determine whether the identified points are part of a flat roof, the processor 22 determines a standard deviation for the localized peak data points over a selected building roof area 52, at Block 33. The building roof area 52 may be selected based upon an aggregation of localized peak data points Selection is automatic based on a low (near zero slope) value provided by Robert's Cross Operator. If the selected building roof area 52 is greater than a threshold portion of a total building roof area, and if the standard deviation is below a threshold standard deviation, then the processor 22 identifies the building roof type as a flat roof type, at Blocks 34-35. By way of example, the threshold portion of the total building roof area may be greater than about 65%), and the threshold standard deviation may be less than about one meter. Of course, different thresholds may be used in different embodiments. The processor 22 may accordingly substitute one or more geometric objects/shapes in place of the building data points that approximates the flat roof building, such as the building 100 in
If either of the above conditions is not met, however, then the processor 22 will proceed to determine whether the building has one or more sloped roof portions. Referring more particularly to
If a given length of a contiguous localized peak data point group remains, at Block 37, and this length has an area that is significantly less than the entire area of the entire roof but is significantly greater than the lengths of any other remaining groups, then the processor 22 identifies the roof as being of a single sloped roof type (Block 39) if no more than one such group is present, at Block 38. In the illustrated example, the groups 63a and 63b meet this criteria and therefore represent the apex of the sloped roof structure. However, if there are more than four corners, this identifies the roof as a complex sloped roof (Block 40). That is, a complex sloped roof has multiple sloped roof features with multiple apexes.
To construct a sloped or complex sloped roof, the processor 22 generates sloped roof/building shapes 65 (
More particularly, the process of rendering different sloped roof shapes is further illustrated in
The edge points 67 are then classified based upon the number of their respective neighbors. That is, an edge point having one neighboring edge point is classified as an end point 67a. An edge point with three neighboring edge points, two of which are end points 67a, is classified as a sloped end point 67b. Moreover, an edge point with three neighboring edge points, one of which is an end point 67a, is classified as a corner point 67c. Further, an edge point with three neighboring edge points and no adjacent end points 67a is classified as a T-junction point 67d. Additionally, an edge point having four neighboring edge points is classified as a cross-junction point (i.e., it is located at the center of a “+” shaped complex sloped building roof structure), which is not shown in the example illustrated in
For each edge without an end point, a rectangle is generated as shown in
If it is determined that the roof type is neither flat nor a single/complex sloped roof, the processor 22 then determines whether the building roof data points define a circular shape, at Block 41, as will be appreciated by those skilled in the art. Referring to
Once the appropriate building roof type is identified and rendered (or if the roof type does not fall into one of the above-described categories), the illustrated method is concluded (Block 45). Of course, in an actual implementation the process may then be repeated to identify other building roof types for different sets of building rood data points.
It should be noted that while the above-described exemplary approach discusses an order of determining roof shapes beginning with flat roof shapes, then sloped roof shapes, and lastly doomed roof shapes, this order may be changed in different embodiments. Generally speaking, however, if a large urban area is being classified then it is desirable to start with the most common building roof type, as this may conserve processing resources. For example, in a downtown area, many of the buildings may have flat roofs, so it may be advantageous to first check if a given set of building roof data points corresponds to a flat roof. In a suburban area, many of the buildings may be houses, which are more likely to have sloped roof structures, and therefore in such applications it may be desirable to start with sloped roof identification.
Moreover, the processor 22 need not check for every possible roof type in every embodiment. For example, it may be desirable to identify only buildings that have a desired type of roof in some examples, for example. Also, the actual shapes of the buildings need not necessarily be rendered for display That is, the above-described approach may be used to automatically identify building roof types with or without subsequent automatic roof/building rendering.
Many modifications and other embodiments of the invention will come to the mind of one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings Therefore, it is understood that the invention is not to be limited to the specific embodiments disclosed, and that modifications and embodiments are intended to be included within the scope of the appended claims.
