Patent Publication Number: US-2013231897-A1

Title: Systems and methods for efficient analysis of topographical models

Description:
BACKGROUND 
     1. Statement of the Technical Field 
     The invention concerns computing systems. More particularly, the invention concerns computing systems and methods for efficient analysis of topographical models. 
     2. Description of the Related Art 
     A common topographical model is a Digital Elevation Model (“DEM”). A DEM is a sampled matrix representation of a geographical area which may be generated in an automated fashion by a computer. In a DEM, coordinate points are made to correspond with a height value. DEMs are typically used for modeling terrain where the transitions between different elevations (e.g., valleys and mountains) are generally smooth from one to a next. That is, DEMs typically model terrain as a plurality of curved surfaces and any discontinuities therebetween are thus “smoothed” over. In a typical DEM, no distinct objects are present on the terrain. This type of DEM is referred to as a Digital Terrain Model (“DTM”). A DTM is a model that represents a terrain&#39;s surface (e.g., the Earth&#39;s surface) without any objects on it (e.g., plants and buildings). DEMs are not limited to DTMs. For example, a DEM may also comprise a Digital Surface Model (“DSM”). A DSM is a model that represents a terrain&#39;s surface (e.g., the Earth&#39;s surface) with all objects on it (e.g., plants and buildings). 
     There are many known systems that generate DEMs. These systems include, but are not limited to, aerial photography based systems, satellite photography based systems, Electro Optical (“EO”) based systems, Synthetic Aperture Radar (“SAR”) based systems, Light Detection And Ranging (“LiDAR”) based systems, and Geiger-Mode Avalanche PhotoDiode (“GMAPD”) based systems. Each of the listed systems is configured to collect terrain elevation data defining a terrain&#39;s surface (e.g., the Earth&#39;s surface, the moon&#39;s surface or an asteroid&#39;s surface). The terrain elevation data defines position measurement values (e.g., longitude and latitude values), height measurement values and correspondences between the same. The terrain elevation data is then processed to generate one or more topographical models, such as DSMs and/or DTMs. Often times, the topographical models comprise surface model errors (e.g., spikes and wells), artifacts, obscurations and voids. Voids may result from the extraction of foliage and/or cultural features (e.g., buildings) from a topographical model, a blind spot of a data collector, and clouds over a geographical area of interest. Therefore, the topographical models are typically analyzed for quality control purposes. 
     The analysis often involves manually analyzing the topographical models to detect errors which need to be corrected. Such manual analysis is often achieved using a computer executing topographical model analysis software (e.g., ESRI® ArcMap® Geospatial Information System (“GIS”) software, SOCET SET® software, FALCONVIEW® software, computer aided design software, computer aided manufacturing software, and GEOCUE® software). In this scenario, only a portion of a topographical model may be displayed to an operator at any given time. As such, the software provides a pan function and a zoom function. The pan function allows the operator to change a viewport from one part of a topographical model to another part of the topographical model. The zoom function allows the operator to change from a distant view of a topographical model to a more close-up view (zoom in) of the topographical model, and vice versa (zoom out). The pan and zoom operations are typically enabled by the operator using a computer mouse, joy stick and/or gestures. 
     After the errors are detected via the above described manual process, the errors are manually corrected by the operator. The error detection and correction processes are complex, labor intensive, time consuming, ad hoc and subject to human error. In addition, it is often difficult to assess the cost of performing topographical model processing given variability and quality in various raw source data. 
     SUMMARY 
     Embodiments of the present invention concern implementing systems and methods for efficiently analyzing topographical models. The methods involve receiving a user input selecting a first content type from a plurality of content types. In response to the reception of the user input, a feature analysis plug-in simultaneously generates a plurality of first model chips using terrain elevation data defining a first topographical model. Each of the first model chips comprises at least one of a panned-only view, a zoomed-only view, and a panned-and-zoomed view of the first topographical model including at least one item of the first content type that is different from all other items of all other first model chips. Thereafter, a first screen page is displayed on a display screen of a computing device. The first screen page comprises a first array defined by a plurality of first cells. Each of the first cells has one of the first model chips presented therein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will be described with reference to the following drawing figures, in which like numerals represent like items throughout the figures, and in which: 
         FIG. 1  is a schematic illustration of an exemplary system. 
         FIG. 2  is a block diagram of an exemplary computing device. 
         FIGS. 3A-3E  collectively provide a flow diagram of an exemplary method for efficient topographical model analysis. 
         FIG. 4  is a schematic illustration of an exemplary three dimensional (“3D”) topographical model. 
         FIG. 5  is a schematic illustration of an exemplary two dimensional (“2D”) topographical model. 
         FIG. 6  is a schematic illustration of an exemplary desktop window. 
         FIG. 7  is a schematic illustration of an exemplary application window. 
         FIG. 8  is a schematic illustration of an exemplary drop down menu of an application window. 
         FIG. 9  is a schematic illustration of an exemplary plug-in window. 
         FIG. 10A  is a schematic illustration of an exemplary toolbar of a plug-in window. 
         FIGS. 10B-10C  each provide a schematic illustration of an exemplary drop down box. 
         FIG. 11  is a schematic illustration of a concavity scale for classifying terrain of a topographical model. 
         FIG. 12  is a schematic illustration of terrain parameters useful for classifying terrain of a topographical map. 
         FIG. 13  is a schematic illustration of an exemplary displayed screen page of model chips. 
         FIGS. 14 ,  16  and  18  each provide a schematic illustration of an exemplary selected model chip and menu of commands. 
         FIGS. 15 ,  17  and  19  each provide a schematic illustration of at least one exemplary marked model chip. 
         FIGS. 20 and 22  each provide a schematic illustration of an exemplary screen page of model chips comprising unfilled voids. 
         FIGS. 21 and 23  each provide a schematic illustration of an exemplary screen page of model chips comprising filled voids. 
         FIG. 24  is a schematic illustration of an exemplary grey scale color ramp. 
         FIG. 25  is a schematic illustration of an exemplary color scale color ramp. 
         FIG. 26  is a schematic illustration of a plurality of exemplary screen pages of sorted model chips. 
         FIG. 27  is a schematic illustration of an exemplary displayed screen page of sorted model chips. 
         FIG. 28  is a schematic illustration of an exemplary screen page of filtered model chips. 
         FIG. 29  is a schematic illustration of an exemplary screen page of sampled model chips. 
         FIG. 30  is a schematic illustration of an exemplary screen page of “fixed zoomed” model chips. 
         FIG. 31  is a schematic illustration of an exemplary screen page of “auto zoomed” model chips. 
         FIG. 32  is a flow diagram of an exemplary method for efficiently analyzing different types of terrain of a topographical model. 
         FIG. 33  is a schematic illustration of exemplary displayed topographical models. 
         FIG. 34  is a schematic illustration of an exemplary screen page of model chips. 
         FIG. 35  is a flow diagram of an exemplary method for efficient terrain and feature data change detection. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention is described with reference to the attached figures. The figures are not drawn to scale and they are provided merely to illustrate the instant invention. Several aspects of the invention are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the invention. One having ordinary skill in the relevant art, however, will readily recognize that the invention can be practiced without one or more of the specific details or with other methods. In other instances, well-known structures or operation are not shown in detail to avoid obscuring the invention. The present invention is not limited by the illustrated ordering of acts or events, as some acts may occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the present invention. 
     The word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is if, X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. 
     The present invention concerns implementing systems and methods for efficient analysis of topographical models. The topographical models include, but are not limited to, 2D models and 3D models. In this regard, the present invention implements data driven pan operations, data driven zoom operations, parallel pan/zoom operations for facilitating the simultaneous visual inspection of numerous areas of interest of a topographical model, operations for toggling/flickering between uncorrected topographical models and corrected topographical models, color difference image generation operations, sorting operations for sorting model chips based on attributes thereof (e.g., size and fill accuracy), and filtering operations for filtering model chips based on attributes thereof. The listed operations will become more evident as the discussion progresses. Still, it should be understood that the present invention overcomes various drawbacks of conventional topographical model analysis techniques, such as those described above in the background section of this document. For example, the present invention provides more efficient, less time consuming and less costly data analysis processes as compared to those of conventional topographical model analysis techniques. 
     The present invention can be used in a variety of applications. Such applications include, but are not limited to, environmental applications, engineering applications, military applications, and any other application in which topographical models needs to be analyzed, corrected and used. Topographical models are used for a variety of purposes. Such purposes include, but are not limited to, flight simulations, planning military missions, cellular antenna placement, urban planning, and disaster preparedness. Exemplary implementing system embodiments of the present invention will be described below in relation to  FIGS. 1 ,  2 ,  4 ,  5 ,  7 ,  9 , and  10 A- 12 . Exemplary method embodiments of the present invention will be described below in relation to  FIGS. 3A-35 . 
     Exemplary Systems 
     Referring now to  FIG. 1 , there is provided a block diagram of an exemplary system  100  that is useful for understanding the present invention. The system  100  comprises a computing device  102 , a network  104 , a server  106 , a data source  108 , and at least one data store  110 ,  112 . The system  100  may include more, less or different components than those illustrated in  FIG. 1 . However, the components shown are sufficient to disclose an illustrative embodiment implementing the present invention. 
     The hardware architecture of  FIG. 1  represents one embodiment of a representative system configured to facilitate topographical model analysis for feature display, quality control, and change detection. As such, system  100  implements a method for efficient topographical model analysis in accordance with embodiments of the present invention. The method will be described in detail below in relation to  FIGS. 3A-35 . However, it should be understood that the method implements a data driven approach for enabling an efficient evaluation of topographical models. The topographical models include, but are not limited to, DSMs and DTMs. Schematic illustrations of exemplary topographical models  400 ,  500  are provided in  FIGS. 4-5 . As shown in  FIGS. 4-5 , the topographical models include, but are not limited to, 2D topographical models and 3D topographical models. 
     The topographical models and any data associated therewith are stored in data store  110 . The data includes, but is not limited to: terrain elevation data defining a terrain&#39;s surface (e.g., the Earth&#39;s surface, the moon&#39;s surface or an asteroid&#39;s surface); data identifying Surface Model Errors (“SMEs”), obscurations, voids, cultural features, and foliage; and data defining the locations of SMEs, obscurations, voids, cultural features and foliage within topographical models; and data defining the correspondences between the previously described identifying data and the previously described location data. The terrain elevation data defines position measurement values (e.g., longitude and latitude values), height measurement values, and correspondences between the same. The SMEs include spikes and wells each representing a one pixel error. A schematic illustration of an exemplary spike  402  and an exemplary well  404  is provided in  FIG. 4 . The obscurations can result from fog, mist, haze, smoke, dust, snow and foliage. The voids can result from the extraction of cultural features and/or foliage from a topographical model. A void is an area of a topographical model comprising an empty set of terrain elevation data. Schematic illustrations of exemplary voids  406 ,  502  are provided in  FIGS. 4-5 . 
     The terrain elevation data can be collected by data source  108 . Data source  108  includes any type of Terrain Elevation Data (“TED”) collection system. Such TED collection systems include, but are not limited to, aerial photography based systems, satellite photography based systems, EO based systems, SAR based systems, LIDAR based systems, and GMAPD based systems. The TED collection system may be disposed on or in a satellite, an Unmanned Aerial Vehicle (“UAV”), a plane or a vehicle. Also, the terrain elevation data can be communicated to the data store  110  via network  104  and server  106 . 
     The computing device  102  facilitates topographical model analysis. Accordingly, the computing device  102  has installed thereon a Topographical Model (“TM”) software application and at least one feature analysis plug-in. The TM software application includes, but is not limited to, ESRI® ArcMap® Geospatial Information System (“GIS”) software, SOCET SET® software, FALCONVIEW® software, computer aided design software, computer aided manufacturing software, and GEOCUE® software. Each of the listed TM software applications is well known in the art, and therefore will not be described in detail herein. However, it should be understood that the TM software applications facilitate the display of topographical models in an application window. The TM software applications also facilitate the panning and zooming of the displayed topographical models. 
     The feature analysis plug-in is a set of software components that adds specific abilities to the TM software application. For example, the feature analysis plug-in provides the ability to: concurrently and/or simultaneously generate a plurality of model chips using terrain elevation data defining a topographical model; and display all or a portion of the generated model chips in a display area of a plug-in window at the same time. The phrase “model chip”, as used herein, refers to a panned view and/or a zoomed view of a topographical model. A model chip may include, but is not limited to, a visual representation of an SME, an obscuration, a void, a cultural feature, foliage, vegetation, a particular type of terrain (e.g., sinkhole, trench, ridge and mound), and/or a difference indication indicating a change of a scene&#39;s content. The term “cultural feature”, as used herein, refers to a representation of an object. Such objects include, but are not limited to, manmade objects (e.g., bridges, water towers, boats, planes, roads, lakes, buildings, gas stations, restaurants, malls, stores, vehicles, and cisterns). 
     Notably, the model images may be displayed in the plug-in window in a grid format or a matrix format. In the grid scenario, each cell of a grid includes one model chip. As a result of such a grid arrangement of model chips, a user can perform topographical model analysis in a shorter period of time as compared to that needed to perform a topographical model analysis using the conventional technique employed by the TM software applications. This conventional technique generally involves manually panning and zooming to each instance of an SME, obscuration, void, cultural feature, foliage, vegetation, type of terrain, and/or difference indication. 
     Referring now to  FIG. 2 , there is provided a block diagram of an exemplary embodiment of the computing device  102 . The computing device  102  can include, but is not limited to, a notebook, a desktop computer, a laptop computer, a personal digital assistant, and a tablet PC. The server  106  of  FIG. 1  can be the same as or similar to computing device  102 . As such, the following discussion of computing device  102  is sufficient for understanding server  106  of  FIG. 1 . Notably, some or all the components of the computing device  102  can be implemented as hardware, software and/or a combination of hardware and software. The hardware includes, but is not limited to, one or more electronic circuits. 
     Notably, the computing device  102  may include more or less components than those shown in  FIG. 2 . However, the components shown are sufficient to disclose an illustrative embodiment implementing the present invention. The hardware architecture of  FIG. 2  represents one embodiment of a representative computing device configured to facilitate topographical model analysis in an efficient manner. As such, the computing device  102  of  FIG. 2  implements improved methods for topographical model analysis in accordance with embodiments of the present invention. 
     As shown in  FIG. 2 , the computing device  102  includes a system interface  222 , a user interface  202 , a Central Processing Unit (“CPU”)  206 , a system bus  210 , a memory  212  connected to and accessible by other portions of computing device  102  through system bus  210 , and hardware entities  214  connected to system bus  210 . At least some of the hardware entities  214  perform actions involving access to and use of memory  212 , which can be a Random Access Memory (“RAM”), a disk driver and/or a Compact Disc Read Only Memory (“CD-ROM”). 
     System interface  222  allows the computing device  102  to communicate directly or indirectly with external communication devices (e.g., server  106  of  FIG. 1 ). If the computing device  102  is communicating indirectly with the external communication device, then the computing device  102  is sending and receiving communications through a common network (e.g., the network  104  shown in  FIG. 1 ). 
     Hardware entities  214  can include a disk drive unit  216  comprising a computer-readable storage medium  218  on which is stored one or more sets of instructions  220  (e.g., software code) configured to implement one or more of the methodologies, procedures, or functions described herein. The instructions  220  can also reside, completely or at least partially, within the memory  212  and/or within the CPU  206  during execution thereof by the computing device  102 . The memory  212  and the CPU  206  also can constitute machine-readable media. The term “machine-readable media”, as used here, refers to a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions  220 . The term “machine-readable media”, as used here, also refers to any medium that is capable of storing, encoding or carrying a set of instructions  220  for execution by the computing device  102  and that cause the computing device  102  to perform any one or more of the methodologies of the present disclosure. 
     In some embodiments of the present invention, the hardware entities  214  include an electronic circuit (e.g., a processor) programmed for facilitating efficient topographical model analysis through data-driven spatial sampling and data-driven spatial re-expansion of terrain elevation data. In this regard, it should be understood that the electronic circuit can access and run TM software applications (not shown in  FIG. 2 ), feature analysis plug-ins (not shown in  FIG. 2 ) and other types of applications installed on the computing device  102 . The TM software applications are generally operative to facilitate the display of topographical models in an application window, the panning of displayed topographical models, and the zooming of displayed topographical models. The listed functions and other functions implemented by the TM software applications are well known in the art, and therefore will not be described in detail herein. A schematic illustration of an exemplary application window  704  is provided in  FIG. 7 . 
     The feature analysis plug-ins are generally operative to display a plug-in window on a display screen of the computing device  102 . A schematic illustration of an exemplary plug-in window  902  is provided in  FIG. 9 . Various types of information can be presented in the plug-in window. Such information includes, but is not limited to, model chips and attribute information. The attribute information can include, but is not limited to, SME characteristics, obscuration characteristics, accuracy metrics, void sizes, prediction errors, maximum height statistical values, minimum height statistical values, void characteristics (e.g., 3D void perimeter complexity characteristics), foliage characteristics, vegetation characteristics, terrain characteristics, scene change characteristics, and information describing attributes of an object which a cultural feature visually represents (e.g., heights, lengths, diameters, longitudes, latitudes, addresses, names, and text). 
     The feature analysis plug-ins are also operative to perform one or more of: automatically and simultaneously generate a plurality of model chips in response to a user software interaction; generate at least one screen page of model chips arranged in a grid or matrix format; display screen pages of model chips in a plug-in window; update a view of a topographical model displayed in an application window to show at least the visual contents of a selected one of a plurality of model chips displayed in a plug-in window; sort a plurality of model chips based on at least one attribute of the contents thereof; generate and display at least one screen page of model chips which are arranged in a sorted order; filter model chips based on at least one attribute of the contents thereof; randomly select and display only a percentage of a plurality of model chips; change a grid size in response to a user software interaction; change a zoom level of scale or resolution of displayed model chips in response to a user software interaction; pan a topographical model displayed in an application window such that the content of a model chip displayed in a plug-in window is shown in the application window; zoom a topographical model displayed in an application window such that the content of a model chip is shown at a particular zoom resolution within the application window; cycle through screen pages of model chips that were generated using a plurality of topographical maps; generate and display model chips comprising areas that are common to two or more topographical models; mark model chips in response to user software interactions; unmark model chips in response to user software interactions; setting a mode of a model chip to a color scale mode or a grey scale mode in response to a user-software interaction; adjusting a contrast of a model chip using a histogram thereof in response to a user-software interaction; and remember various settings that a user sets for each content class (e.g., SMEs, obscurations, voids, foliage, vegetation, terrain, cultural features and difference indications) bridges, water towers and gas stations) during at least one session. The listed functions and other functions of the feature analysis plug-ins will become more apparent as the discussion progresses. Notably, one or more of the functions of the feature analysis plug-ins can be accessed via a toolbar, menus and other Graphical User Interface (“GUI”) elements of the plug-in window. 
     A schematic illustration of an exemplary toolbar  1004  of a plug-in window (e.g., plug-in window  902  of  FIG. 9 ) is provided in  FIG. 10A . As shown in  FIG. 10A , the toolbar  1004  comprises a plurality of exemplary GUI widgets  1002 - 1032 . Each of the GUI widgets  1002 - 1032  is shown in  FIG. 10A  as a particular type of GUI widget. For example, GUI widget  1002  is shown as a drop down menu. Embodiments of the present invention are not limited in this regard. The GUI widgets  1002 - 1032  can be of any type selected in accordance with a particular application. 
     GUI widget  1002  is provided to facilitate the display of an array of model chips including content of a user selected content class (e.g., SMEs, obscurations, voids, foliage, vegetation, terrain, cultural features, or difference indication) and/or a user selected content sub-class. Terrain sub-classes can include, but are not limited to, a sinkhole sub-class, a trench sub-class, a hyperbolic terrain sub-class, a planar terrain sub-class, a ridge sub-class, and a mound sub-class. Cultural feature sub-classes can include, but are not limited to, a bridge sub-class, a building sub-class, and a water tower sub-class. Difference indication sub-classes can include, but are not limited to, positive elevation changes, negative elevation changes, matched content, new content, and deleted content. The array of model chips is displayed in the display area (e.g., display area  906  of  FIG. 9 ) of the plug-in window (e.g., plug-in window  902  of  FIG. 9 ) in a grid format. In the embodiment shown in  FIG. 10A , the GUI widget  1002  includes, but is not limited to, a drop down list that is populated with the content classes identified in a previously generated content list. Drop down lists are well known in the art, and therefore will not be described herein. 
     GUI widget  1004  is provided to facilitate moving through screen pages of model chips associated with a single content class. If a selected content class has more than the maximum number of individual items and/or clusters of items that can fit in a grid of a selected grid size (e.g., three cells by three cells), then the feature analysis plug-in generates a plurality of screen pages of model chips. Each screen page of model chips includes a grid with model chips contained in the cells thereof. As shown in the embodiment of  FIG. 10A , the GUI widget  1004  includes, but is not limited to, a text box, a forward arrow button and a backward arrow button. Text boxes and arrow buttons are well known in the art, and therefore will not be described herein. This configuration of the GUI widget  1004  allows a user to move forward and backward through the screen pages of model chips associated with a single topographical model. Paging forward or backward will cause the model chip in an upper left corner grid cell of the new screen page to be selected. The screen page context is displayed in the text box as the numerical range of model chips displayed (e.g., model chips one through nine) and the total number of model chips (e.g., twenty) providing visual representations of items of a selected content class. 
     GUI widget  1006  is provided to facilitate jumping to a desired screen page of model chips for review. As shown in the embodiment of  FIG. 10A , GUI widget  1006  includes, but is not limited to, a text box and a search button. The text box is a box in which to enter a screen page number (e.g., three). Clicking the search button will cause the screen page of model chips having the entered screen page number to be displayed in the display area (e.g., display area  906  of  FIG. 9 ) of the plug-in window (e.g., plug-in window  902  of  FIG. 9 ). 
     GUI widget  1008  is provided to facilitate a selection of a grid size from a plurality of pre-defined grid sizes. As shown in  FIG. 10A , the GUI widget  1008  includes, but is not limited to, a drop down list listing a plurality of pre-defined grid sizes. In some embodiments, the pre-defined grid sizes include one cell by one cell, two cells by two cells, three cells by three cells, four cells by four cells, five cells by five cells, six cells by six cells, seven cells by seven cells, eight cells by eight cells, nine cells by nine cells, and ten cells by ten cells. The grid size of two cells by two cells ensures that a maximum of four model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of three cells by three cells ensures that a maximum of nine model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of four cells by four cells ensures that a maximum of sixteen model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of five cells by five cells ensures that a maximum of twenty-five model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of six cells by six cells ensures that a maximum of thirty-six model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of seven cells by seven cells ensures that a maximum of forty-nine model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of eight cells by eight cells ensures that a maximum of sixty-four model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of nine cells by nine cells ensures that a maximum of eight-one model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. The grid size of ten cells by ten cells ensures that a maximum of one hundred model chips will be simultaneously or concurrently displayed in the display area of the plug-in window. Embodiments of the present invention are not limited to grid having an equal number of cells in the rows and columns thereof. For example, a grid can alternatively have a grid size of four cells by three cells such that each column thereof comprises four cells and each row thereof comprises three cells, or vice versa. 
     Notably, the display area for each model chip is different for each grid size. For example, the display area for each model chip in a grid having a grid size of two cells by two cells is larger than the display area for each model chip in a grid having a grid size of three cells by three cells. Also, if each model chip has the same zoom level of scale or resolution, then the portion of a topographical model contained in a model chip displayed in a two cell by two cell grid is larger than the portion of a topographical model contained in a model chip displayed in a three cell by three cell grid. It should also be noted that, in some embodiments, a selected model chip of a first grid will reside in an upper left corner cell of a second grid having an enlarged or reduced grid size. 
     GUI widget  1012  is provided to facilitate a selection of content for display in the display area (e.g., display area  706  of  FIG. 7 ) of the plug-in window (e.g., plug-in window  702  of  FIG. 7 ) based on its attributes. As shown in  FIG. 10A , the GUI widget  1012  includes a “filter control” button and a “filter setting” drop down button. The “filter control” button facilitates the enablement and disablement of an attribute filter function of the feature analysis plug-in. The “filter setting” drop down button facilitates the display of a drop-down box for assembling a query phrase defining an attribute filter (e.g., [“HEIGHT”=‘100 Feet’], [“HEIGHT”&lt;‘100 Feet’], [“HEIGHT”&lt; &gt;‘100 Feet’], [“HEIGHT” IS NULL], [“HEIGHT” IS NOT NULL], [“HEIGHT”≧‘100 Feet’ AND “DIAMETER”&gt;‘40 Feet’], [“HEIGHT”≦‘100 Feet’ OR “DIAMETER”&gt;‘40 Feet’], [“CONCAVITY INDEX”≦‘0.50’], [“WIDTH”≦‘10 Meters’], [“VOID SIZE”≧‘5 Meters’], [“VOID FILL ACCURACY”≧‘8’], [“VOLUMETRIC DIFFERENCE OF EACH VOID”=‘0.2 Meters’], or [“VOID AREA”≧‘10 Meters’]). A schematic illustration of an exemplary drop-down box  1050  is provided in  FIG. 10B . When the attribute filter function is enabled, the query phrase takes effect immediately. 
     Notably, the feature analysis plug-in remembers the filter query phrase that a user sets for each content class and/or content sub-class during a session. Accordingly, if the user changes a content class or a content sub-class from a first content class or sub-class (e.g., SMEs) to a second content class or sub-class (e.g., voids) during a session, then the previously set filter query for the second content class or sub-class will be restored. Consequently, only items of the second content class or sub-class (e.g., voids) which have the attribute specified in the previously set filter query (e.g., “VOID FILL ACCURACY”≧‘8’) will be displayed in the plug-in window. 
     GUI widget  1014  is provided to facilitate the sorting of model chips based on one or more attributes of the items contained therein. For example, a plurality of model chips are sorted into an ascending or descending order based on the heights and/or diameters of sinkholes visually represented by the content contained therein. As shown in  FIG. 10A , the GUI widget  1014  includes a drop down list. Embodiments of the present invention are not limited in this regard. For example, the GUI widget  1014  can alternatively include a button and a drop down arrow for accessing a drop down box. The button facilitates the enablement and disablement of a sorting function of the feature analysis plug-in. The drop down box allows a user to define settings for sorting model chips based on one or more attributes of an active content class. As such, the drop down box may include a list from which an attribute can be selected from a plurality of attributes. The drop down box may also include widgets for specifying whether the model chips should be sorted in an ascending order or a descending order. 
     Notably, the feature analysis plug-in remembers the sort settings that a user defines for each content class and/or content sub-class during a session. Accordingly, if the user changes a content class or sub-class from a first content class or sub-class (e.g., SMEs) to a second feature class (e.g., obscurations) during a session, then the previously defined sort settings for the second content class or sub-class will be restored. Consequently, model chips containing items of the second content class or sub-class (e.g., obscurations) will be displayed in a sorted order in accordance with the previously defined sort settings. 
     GUI widget  1020  is provided to facilitate the display of a random sample of model chips of items of a particular content class for visual inspection and quality control testing. As such, the GUI widget  1020  includes, but is not limited to, a button for enabling/disabling a random sampling function of the feature analysis plug-in and a drop down menu from which a percentage value can be selected. 
     GUI widget  1022  is provided to facilitate the toggling of histogram stretch of model chips on and off. Histogram stretch leads to brighter and higher-contrast imagery. Accordingly, histogram stretch is achieved by adjusting the global contrasts of model chips or topographical models using histograms thereof. As such, the GUI widget  1022  includes, but is not limited to, a button for enabling and disabling a histogram stretch function of the feature analysis plug-in. Histogram stretch is well known in the art, and therefore will not be described herein. Still, it should be understood that histogram stretch can be turned on and off for terrain elevation data and/or for imagery. 
     GUI widget  1032  is provided to facilitate the filtering of features which lie outside of an area (e.g., a geographical area) defined either by the area that a plurality of topographical models have in common or by the area covered by a plurality of topographical models taken together. As such, the GUI widget  1032  includes, but is not limited to, a button for enabling and disabling an intersection function of the feature analysis plug-in. 
     GUI widget  1010  is provided to facilitate the selection of a topographical model from a plurality of topographical models. As shown in  FIG. 10A , the GUI widget  1010  includes, but is not limited to, a text box and a drop down list populated with the names of topographical models. If a user selects a new item from the drop down list, then the feature analysis plug-in generates and displays at least one screen page of model chips using the topographical model identified by the newly selected item. The model chips contain items of the same content class as the immediately preceding displayed model chips. The text box displays information identifying the topographical model from which the currently displayed model chips were generated. The contents of the text box can be updated in response to a user selection of a new item from the drop down list. The contents of the text box can also be updated by the feature analysis plug-in during the performance of topographical model cycling operations, which will be described below in relation to GUI widget  1024 . Accordingly, the information contained in the text box always identifies the topographical model from which the currently displayed model chips were generated. 
     GUI widget  1024  is provided to facilitate the cycling through screen pages of model chips for a plurality of topographical models. A user may want to cycle through such screen pages for change detection purposes. The GUI widget  1024  is configured to allow manual cycling and/or automatic cycling between screen pages for a plurality of topographical models. As such, the GUI widget  1024  includes, but is not limited to, a check box for enabling and disabling topographical model cycling operations of the feature analysis plug-in, a slider for setting the rate at which the topographical models automatically cycle, and/or a button for manually commanding when to change the topographical model. 
     GUI widget  1026  is provided to facilitate the performance of manual-scale operations by the feature analysis plug-in. The manual-scale operations are operative to adjust the zoom level of scale of all of the displayed model chips from a first zoom level of scale to a second zoom level of scale in response to a user-software interaction. The first zoom level of scale is a default zoom level of scale (e.g., 100%) or a previously user-selected zoom level of scale (e.g., 50%). The second zoom level of scale is a new user-selected zoom level of scale (e.g., 75%). As such, the GUI widget  1926  includes, but is not limited to, a drop down list populated with a plurality of whole number percentage values. The percentage values include, but are not limited to, whole number values between zero and one hundred. 
     GUI widget  1028  is provided to facilitate the viewing of each displayed item at its best-fit zoom level of scale or its pre-defined maximum zoom level of scale. As such, the GUI widget  1028  includes, but is not limited to, a button for enabling and disabling auto-scale operations of the feature analysis plug-in. When the auto-scale operations are enabled, the manual-scale operations are disabled. Similarly, when the auto-scale operations are disabled, the manual-scale operations are enabled. 
     GUI widget  1016  is provided to facilitate the writing of all “marked” model chips to an output file stored in a specified data store (e.g., data store  110  of  FIG. 1 ). GUI widget  1018  is provided to facilitate the saving of all model chips which have been “marked” during a session to a user-named file. The user-named file can include, but is not limited to, a shapefile. Shapefiles are well known in the art, and therefore will not be described herein. In some embodiments of the present invention, a model chip is “marked” by right clicking on the model chip to obtain access to a “chip context” GUI and selecting a “mark” item from the “chip context” GUI. 
     GUI widget  1030  is provided to facilitate the classification of terrain of a first topographical model and the generation of a second topographical model clearly distinguishing the different types of terrain visually represented in the first topographical model. The types of terrain can be distinguished by color coding terrain elevation data of a topographical map in accordance with a defined color scheme. Methods for color coding terrain elevation data are well known in the art, and therefore will not be described herein. However, it should be understood that any known method for color coding terrain elevation data can be used with the present invention without limitation. One such method generally involves: calculating a shape index of each post using interpolated surface normals of each point; determining a size of each patch to be interpolated; thresholding a concavity index to classify each post; segmenting the posts into groups; and culling insignificant feature groups; and creating a polygon shapefile. 
     As shown in  FIG. 10A , the GUI widget  1030  includes, but is not limited to, a button for enabling and disabling terrain classification functions of the feature analysis plug-in. In response to the depression of the button a drop down box is presented to the user of the computing device  102 . The drop down box is provided to allow a user to set certain parameters for terrain classification. A schematic illustration of an exemplary terrain classification drop down box  1060  is provided in  FIG. 10C . As shown in  FIG. 10C , the drop down box  1060  includes a plurality of GUI widgets for defining a minimum trench/ridge concavity index value, a minimum trench/ridge length value, a minimum trench/ridge width value, a minimum sinkhole/mound concavity index value, and a minimum sinkhole/mound diameter value. The drop down box also comprises a GUI widget for initiating the performance of the terrain classification functions of the feature analysis plug-in. Embodiments of the present invention are not limited to the particularities of  FIG. 10C . 
     An exemplary color scheme employed by the feature analysis plug-in is schematically illustrated in  FIGS. 11-12 . As shown in  FIGS. 11-12 , different types of terrain are classified into a plurality of categories based on the concavity index, height, diameter, length and/or width thereof. For example, terrain having a concavity index of one, a pre-defined height, and a pre-defined diameter is classified as a sinkhole. Terrain having a concavity index of one half, a pre-defined height, a pre-defined length and a pre-defined width is classified as a trench. Terrain having a concavity index of zero is classified as hyperbolic terrain or planar terrain. Terrain having a concavity index of negative one half, a pre-defined height, a pre-defined length and a pre-defined width is classified as a ridge. Terrain having a concavity index of negative one, a pre-defined height and a pre-defined diameter is classified as a mound. After the terrain has been classified into a plurality of terrain categories, the second typographical model is generated by color coding the terrain elevation data of the first topographical map. For example, red coded terrain elevation data represents a sinkhole. Yellow coded terrain elevation data represents a trench. Green coded terrain elevation data represents hyperbolic terrain or planar terrain. Blue coded terrain elevation data represents a ridge. Purple coded terrain elevation data represents a mound. Once all of the terrain elevation data has been color coded, the second topographical map is generated and displayed in a plug-in window. Embodiments of the present invention are not limited to the particularities of  FIGS. 11-12 . 
     As evident from the above discussion, the system  100  implements one or more method embodiments of the present invention. The method embodiments of the present invention provide implementing systems with certain advantages over conventional topographical model analysis systems. For example, the present invention provides a system in which an analysis of topographical models can be performed in a shorter period of time as compared to the amount of time needed to analyze topographical models using conventional pan/zoom techniques. The present invention also provides a system in which topographical models are analyzed much more efficiently than in conventional topographical model analysis systems. The manner in which the above listed advantages of the present invention are achieved will become more evident as the discussion progresses. 
     Exemplary Methods 
     Referring now to  FIGS. 3A-3E , there is provided a flow diagram of an exemplary method  300  for efficient topographical model analysis that is useful for understanding the present invention. As shown in  FIG. 3A , the method  300  begins with step  301  and continues with step  302 . In step  302 , terrain elevation data is collected. The terrain elevation data can include, but is not limited to, data defining position measurement values (e.g., longitude values and latitude values), height measurement values and correspondences between the position measurement values and the height measurement values. After the terrain elevation data is collected, it is stored in a data store (e.g., data store  110  of  FIG. 1 ) that is accessible by a computing device (e.g., computing device  102  of  FIG. 1 ), as also shown by step  302 . 
     In a next step  303 , the terrain elevation data is processed to generate at least one first topographical model. Schematic illustrations of exemplary topographical models  400 ,  500  are provided in  FIGS. 4-5 . Methods for generating topographical models based on terrain elevation data are well known in the art, and therefore will not be described herein. However, it should be understood that any such method can be used with the present invention without limitation. One such method that can be employed by the present invention is described in U.S. Pat. No. 6,654,690 to Rahmes et al. 
     Once the first topographical model has been generated, it is analyzed for purposes of identifying SMEs, obscurations, voids, cultural features, foliage and/or vegetation therein, as shown by step  304 . Methods for identifying SMEs, obscurations, voids, cultural features, foliage and/or vegetation within a topographical model are well known in the art, and therefore will not be described herein. Any such method can be used with the present invention without limitation. One such method for identifying voids within a topographical model is described in U.S. Patent Publication No. 2008/0319723 to Smith et al. An exemplary method for identifying SMEs (e.g., spikes and wells) within a topographical model is described in U.S. Pat. No. 7,376,513 to Rahmes et al. 
     Referring again to  FIG. 3A , the method  300  continues with step  305  where data is generated and stored in a data store (e.g., data store  110  of  FIG. 1 ). The data can include, but is not limited to: data identifying SMEs, obscurations, voids, cultural features, foliage and vegetation of a topographical model; and data defining the locations and characteristics of the SMEs, obscurations, voids, cultural features, foliage and vegetation of a topographical model. Step  305  can also involve generating a list of content categories identifying the types of content contained in the topographical model. For example, the list can include, but is not limited to, the following content categories: SME; obscuration; void; cultural feature; foliage; and/or vegetation. This list of content categories will be subsequently used by a feature analysis plug-in to allow a user to select a particular type of content to analyze at any given time. 
     In a next step  306 , a TM analysis software application is launched. The TM software application can be launched in response to a user software interaction. For example, as shown in  FIG. 6 , a TM software application can be launched by accessing and selecting a “Topographical Model Analysis Software Program” entry  604  on a start menu  602  of a desktop window  600 . 
     In a next step  307 , an application window is displayed on top of the desktop window. A schematic illustration of an exemplary application window is provided in  FIG. 7 . As shown in  FIG. 7 , the application window  704  includes a toolbar  710  including GUI widgets for at least displaying a topographical model, panning a topographical model, zooming a topographical model, and launching a plug-in. The application window  704  also includes a display area  706  in which a topographical model can be presented to a user of the computing device (e.g., computing device  102  of  FIG. 1 ). 
     Referring again to  FIG. 3A , a topographical model is displayed in the application window, as shown in step  308 . A schematic illustration showing an exemplary topographical model  400  of  FIG. 4  displayed in an application window  704  is provided in  FIG. 7 . As shown in  FIGS. 4 and 7 , the topographical model  400  contains a plurality of items. The items include spikes  402 , wells  404  and voids  406 . Notably, data defining the items may be color coded such that the items are visually distinguishable from other content of the topographical model  400 . For example, the items are presented in purple and all other content is presented in green. 
     After the topographical model is presented to a user of the computing device (e.g., computing device  102  of  FIG. 1 ), a feature analysis plug-in is launched, as shown by step  309 . The feature analysis plug-in can be launched in response to a user-software interaction. For example, as shown in  FIG. 8 , a feature analysis plug-in is launched by selecting an item  802  of a drop down menu of a toolbar  710 . Once the feature analysis plug-in is launched, step  310  is performed where a plug-in window is displayed on top of the desktop window and/or application window. A schematic illustration of an exemplary plug-in window  902  is provided in  FIG. 9 . As shown in  FIG. 9 , the plug-in window  902  comprises a toolbar  904 , a display area  906 , an attribute pane  908 , and a scrollbar  910 . A schematic illustration of the toolbar  904  is provided in  FIG. 10A . As shown in  FIG. 10A , the toolbar  904  comprises a plurality of exemplary GUI widgets  1002 - 1032 . Each of the GUI widgets  1002 - 1032  is described above in detail. 
     Referring again to  FIG. 3A , a next step  311  involves receiving a user input for selecting a first “item” contained in the first topographical model displayed in the application window. The “item” can include, but is not limited to, an SME, an obscuration, a void, a cultural feature, or foliage. In response to the user-software interaction of step  311 , the feature analysis plug-in automatically and simultaneously generates a plurality of first model chips, as shown by step  312 . The first model chips are generated using the data generated in previous step  304  and the data defining the first topographical model. The first model chips comprise panned and/or zoomed views of the first topographical model including content that was previously identified as the selected “item” and “items” of the same type as the selected “item”. Each of the model chips can include a single “item” or a cluster of “items”. Also, data defining the “items” of the first model chips can be color coded such that the “items” are visually distinguishable from other content thereof. 
     Upon completing step  312 , step  313  is performed where at least one first screen page of first model chips is created by the feature analysis plug-in. The model chips are arranged on the first screen page in a grid or matrix format. The grid or matrix of the first model chips has a default size (e.g., ten cells by ten cells) or a user-specified size (e.g., three cells by three cells). In a next step  314 , the first screen page of first model chips is displayed in a plug-in window. A schematic illustration of a displayed screen page of model chips  1302  is provided in  FIG. 13 . As shown in  FIG. 13 , the screen page  1302  comprises a grid  1306  defined by a plurality of grid cells  1308 . A different model chip  1304  is presented within each grid cell  1308  of the grid  1306 . Each model chip  1304  comprises a single void  1310  or a cluster of voids  1312 . Embodiments of the present invention are not limited to the particularities of  FIG. 13 . 
     Subsequent to displaying the first screen page of first model chips in the plug-in window, the method  300  continues with step  315  of  FIG. 3B . In step  315 , the first model chips are analyzed by a user of the computing device (e.g., computing device  102  of  FIG. 1 ). The first model chips are analyzed for purposes of determining which of the “items” (e.g., SMEs, obscurations, voids, and/or cultural features) previously identified in step  304  need to be repaired, filled or removed. During or subsequent to this analysis, steps  316 - 320  can be performed. 
     Step  316  involves receiving a user input selecting at least one of the first model chips by the computing device (e.g., computing device  102  of  FIG. 1 ). The first model chip that is selected in step  316  comprises at least one item (e.g., an SME, obscuration, void, foliage or cultural feature) that needs to be repaired, filled or removed from the first topographical model. The model chip can be selected by moving a mouse cursor over the model chip and clicking a mouse button. A schematic illustration of a selected model chip  1402  is provided in  FIG. 14 . As shown in  FIG. 14 , the selected model chip  1402  is annotated with a relatively thick and distinctly colored border. Embodiments of the present invention are not limited in this regard. Any type of mark or annotation can be used to illustrate that a particular model chip has been selected. 
     In response to this user input, step  317  is performed where attribute information for the item(s) contained in the selected model chip is displayed in an attribute pane (e.g., attribute pane  908  of  FIG. 9 ) of the plug-in window (e.g., plug-in window  902  of  FIG. 9 ). A schematic illustration of an exemplary plug-in window  902  is provided in  FIG. 14  which has attribute information a 1 , a 2  for a void  1404  displayed therein. 
     Although not shown in  FIG. 3B , the first topographical model displayed in the application window may also be updated in step  317  such that the contents of the selected model chip are displayed in the application window. In this regard, the application window can be updated to include a new panned and/or zoomed view of the first topographical model. In some embodiments of the present invention, the updated view of the first topographical model is: (a) a panned only view having the same zoom resolution as the original view; or (b) a panned and zoomed view having a zoom resolution that is different than that of the original view, wherein the zoom resolution of the updated view is a default zoom resolution or a user defined zoom resolution. Also, the updated view of the first topographical model may include the “item” (e.g., void  1404  of  FIG. 14 ) at a pre-determined location therein, e.g., the center thereof. Embodiments of the present invention are not limited in this regard. 
     Referring again to  FIG. 3B , the method  300  continues with step  318  where a user input is received by the computing device (e.g., computing device  102  of  FIG. 1 ) for marking at least one model chip of the displayed first model chips. Step  318  can involve selecting a model chip. The model chip can be selected by moving a mouse cursor over the model chip and clicking a mouse button. In response to the click of the mouse button, a menu is presented to the user of the computing device. The menu includes a list of commands, such as a command for enabling “Mark/Unmark” operations, “Mark/Unmark FW” operations, and “Mark/Unmark BW” operations of the feature analysis plug-in. By enabling the “Mark/Unmark” operations, only the selected model chip will have a mark or annotation added thereto. By enabling the “Mark/Unmark FW” operations, the selected model chip and all model chips that precede the selected model chip in an order will have a mark or annotation added thereto. By enabling the “Mark/Unmark BW” operations, the selected model chip and all model chips that succeed the selected model chip in an order will have a mark or annotation added thereto. 
     Schematic illustrations of an exemplary selected model chip  1402  and an exemplary menu  1408  are provided in  FIGS. 14 ,  16  and  18 . As shown in  FIGS. 14 ,  16  and  18 , the selected model chip  1404  is annotated with a relatively thick and distinctly colored border. Also, a selected command “Mark/Unmark”, “Mark/Unmark FW” or “Mark/Unmark BW” of the menu  1408  is annotated by bolding the text thereof. Embodiments of the present invention are not limited in this regard. Any type of mark or annotation can be used to illustrate that a particular mode chip has been selected and/or that a particular command of a menu has been selected. 
     In response to the reception of the user input in step  318  of  FIG. 3B , the feature analysis plug-in performs step  319 . In step  319 , at least the selected model chip is automatically marked with a pre-defined mark. Schematic illustrations of model chips  1402 ,  1702 ,  1704 ,  1706 ,  1708 ,  1902 ,  1904 ,  1906 ,  1908  marked with marks  1502  is provided in  FIGS. 15 ,  17  and  19 . Embodiments of the present invention are not limited to the particularities of  FIGS. 15 ,  17  and  19 . Any type of mark or annotation can be employed to illustrate that a model chip has been marked. 
     Referring again to  FIG. 3B , the method  300  continues with step  320  where all of the “marked” model chips are exported to a table or file. The exportation can be initiated by a user of the computing device using a GUI widget (e.g., GUI widget  1016  or  1018  of  FIG. 10A ) of the plug-in window. 
     In a next step  321 , a TM editing software application is launched. TM editing software applications are well known in the art, and therefore will not be described herein. Any such TM editing software application can be used with the present invention without limitation. However, it should be understood that the TM editing software application is configured to facilitate the repair of SMEs, the filling of voids, the removal of cultural features from a topographical model, and the removal of foliage from a topographical model. The TM editing software application can be launched in response to a user software interaction. For example, a TM editing software application can be launched by accessing and selecting an entry (not shown) on a start menu (e.g., start menu  602  of  FIG. 6 ) of a desktop window (e.g., desktop window  600  of  FIG. 6 ). 
     After the TM editing software application has been launched, a second topographical model is generated in step  322 . Step  322  involves performing manual and/or automatic operations to edit the first topographical model. The edits can include, but are not limited to, repairing SMEs of the first topographical model, filling voids of the first topographical model, removing cultural features from the first topographical model, and/or removing foliage/vegetation from the first topographical model. The second topographical model is stored in a data store (e.g., data store  110  of  FIG. 1 ), as shown by step  323 . 
     In a next step  324 , the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for viewing screen pages of model chips comprising content of the first topographical model including “items” of interest, and for viewing screen pages of model chips comprising the corresponding repaired “items” of the second topographical model. In response to this user input, the feature analysis automatically and simultaneously generates a plurality of second model chips using the data of the second topographical model, as shown by step  325 . The second model chips comprise panned and/or zoomed views of the second topographical model including repaired, filled and/or removed “items”. The “items” can include, but are not limited to, repaired SMEs of a topographical model, repaired obscurations of the topographical model, filled voids of the topographical model, removed cultural features of the topographical model, and/or removed foliage/vegetation of the topographical model. Each of the second model chips includes a single “item” or a cluster of “items”. 
     Upon completing step  325 , the method  300  continues with step  326  of  FIG. 3C . As shown in  FIG. 3C , step  326  involves creating at least one screen page of second model chips. The screen page of second model chips is created by the feature analysis plug-in. The second model chips are arranged in the screen page in a grid or matrix format. The grid or matrix of the first model chips has a default size (e.g., ten cells by ten cells) or a user-specified size (e.g., three cells by three cells). Schematic illustrations of exemplary screen pages of second model chips are provided in  FIGS. 21 and 23 . As shown in  FIG. 21 , the model chips  2102 - 2150  of screen page  2100  each comprise a filled void. Similarly, as shown in  FIG. 22 , the model chips of screen page  2300  each comprise a filled void. 
     In a next step  327  of  FIG. 3C , the feature analysis plug-in creates a second screen page of first model chips including content comprising the “items” which correspond to the repaired/filled/removed “items” of the second topographical model. In some embodiments, the first model chips include, but are not limited to, content comprising SMEs which correspond to the repaired SMEs/obscurations of the second topographical model, the voids which correspond to the filled voids of the second topographical model, cultural features which correspond to bare surface locations of the second topographical model where the cultural features were removed, and/or foliage/vegetation which correspond to bare surface locations of the second topographical model where the foliage/vegetation was removed. 
     Schematic illustrations of exemplary second screen pages of first model chips are provided in  FIGS. 20 and 22 . As shown in  FIG. 20 , the model chips of the screen page  2000  comprise unfilled voids. The unfilled voids of screen page  2000  correspond to the filled voids of screen page  2100  of  FIG. 21 . For example, the void of model chip  2002  of  FIG. 20  corresponds to the filled void of model chip  2102  of  FIG. 21 . Similarly, the void of model chip  2004  of  FIG. 20  corresponds to the filled void of model chip  2104  of  FIG. 21 , and so on. As similarly shown in  FIG. 22 , the model chips of the screen page  2200  comprise unfilled voids which correspond to the filled voids of screen page  2300 . Although the unfilled voids are shown as black patches in  FIG. 20  and white patches in  FIG. 22 , embodiments of the present invention are not limited in this regard. Any type of coloration or other indicator can be used to schematically illustrate an unfilled void. 
     Referring again to  FIG. 3C , the method  300  continues with step  328  where the second screen page of first model chips is displayed in the plug-in window (e.g., plug-in window  704  of  FIG. 7 ). In a next step  329 , the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for setting a mode of at least one first model chip and/or at least one second model chip to a color scale mode or a grey scale mode. In the color scale mode, the terrain elevation data is color coded in accordance with a color ramp of a color scale. A schematic illustration of an exemplary color ramp  2500  of a color scale is provided in  FIG. 25 . In grey scale mode, the terrain elevation data is color coded in accordance with a color ramp of a grey scale. A schematic illustration of an exemplary color ramp  2400  of a grey scale is provided in  FIG. 24 . The mode of a model chip can be set using a GUI widget of a toolbar (e.g., toolbar  710  of  FIG. 7 ) of the plug-in window (e.g., plug-in window  704  of  FIG. 7 ), and/or by selecting a command from a menu of commands accessed via a mouse click of the model chip. 
     After completing step  329 , the method  300  continues with step  330  where the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for toggling between the second screen page of first model chips and the screen page of second model chips. The user input is facilitated by a GUI widget (e.g., GUI widget  1024  of  FIG. 10A ) of the plug-in window. The GUI widget is configured to allow manual cycling and/or automatic cycling between screen pages for a plurality of topographical models. As such, the GUI widget may include, but is not limited to, a check box for enabling and disabling automatic topographical model cycling operations of the feature analysis plug-in, a slider for setting the rate at which the topographical models automatically cycle, and/or a button for manually commanding when to change the topographical model. In response to this user input of step  330 , the content of the plug-in window is alternated between the second screen page of first model chips and the screen page of second model chips, as shown by step  331 . 
     In a next step  332 , the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for sorting or filtering at least the second model chips based on at least one attribute of “items” thereof. The “items” can include, but are not limited to, repaired SMEs, repaired obscurations, filled voids, removed cultural features, removed foliage/vegetation, and/or bare surfaces. The attribute can include, but is not limited to, a void size, a void fill accuracy, an SME repair accuracy, an obscuration repair accuracy, prediction error, a maximum height statistical value, a minimum height statistical value, and void characteristics. The user input is facilitated by a GUI widget (e.g., GUI widget  1014  or GUI widget  1012  of  FIG. 10A ) of the plug-in window (e.g., the plug-in window  702  of  FIG. 7 ). The GUI widget may be configured to allow a user to specify the attribute(s) that the sorting or filtering should be based on. The GUI widget may also be configured to allow a user to specify whether the model chips should be sorted in an ascending order or a descending order. 
     In response to the user input of step  332 , the feature analysis plug-in performs operations for sorting or filtering the second model chips based on at least one user-selected attribute, as shown by step  333 . Thereafter in step  334 , at least one screen page of sorted/filtered model chips is created by the feature analysis plug-in. The sorted/filtered model chips are arranged on the screen page in a pre-defined grid format or a matrix format. The screen page of sorted/filtered model chips is then displayed in the plug-in window, as shown by step  335 . A schematic illustration of an exemplary screen page of sorted model chips  2600  displayed in a plug-in window  902  is provided in  FIG. 27 . 
     Notably, a screen page of sorted model chips may or may not include the same views as the screen page from which it is derived. For example, if the first grid has a grid size of five cells by five cells as shown in  FIG. 21 , then views one through twenty five are presented therein. These twenty five views are referred to in  FIG. 21  by reference numbers  2102 - 2150 . Thereafter, an ordered list is generated by sorting the twenty five panned and/or zoomed views by at least one attribute (e.g., void fill accuracy). Also, a new grid size of three cells by three cells is selected. In this scenario, three screen pages of sorted model chips  2600 ,  2602 ,  2606  of  FIG. 26  are created by the feature analysis plug-in. Each of the screen pages  2600 ,  2602 ,  2606  includes nine views identified in the ordered list. These nine views of the ordered list may include select ones of the original unsorted views  2102 - 2150 . Notably, two cells of screen page  2606  are absent of model chips. This is due to the fact that there are less model chips then there are cells of the three screen pages  2600 ,  2602 ,  2606 . Embodiments of the present invention are not limited to the particularities of  FIGS. 21 and 26 . 
     A screen page of filtered model chips is created by removing at least one model chip from the displayed screen page of second model chips in accordance with the results of the filtering operations performed in previous step  333 . A schematic illustration of an exemplary screen page of filtered model chips  2800  is provided in  FIG. 28 . As shown in  FIG. 28 , the screen page of filtered model chips  2800  includes the model chips  2108 - 2112 ,  2116 ,  2120 ,  2122 ,  2126 ,  2130 - 2136 ,  2140 ,  2144 - 2148  contained in the screen page of model chips  2100  of  FIG. 21 . However, the screen page of filtered model chips  2800  does not include model chips  2102 - 2106 ,  2114 ,  2118 ,  2124 ,  2128 ,  2138 ,  2142 ,  2150  in grid cells thereof. In this regard, it should be understood that model chips  2102 - 2106 ,  2114 ,  2118 ,  2124 ,  2128 ,  2138 ,  2142 ,  2150  have been removed from the screen page of model chips  2100  of  FIG. 21  to obtain the screen page of filtered model chips  2800 . Embodiments of the present invention are not limited in this regard. 
     Referring again to  FIG. 3C , the method  300  continues with an optional step  336  of  FIG. 3D  upon the completion of step  335 . As shown in  FIG. 3D , step  336  involves optionally marking at least one of the sorted/filtered model chips in accordance with a user-software interaction to indicate that an “item” thereof was repaired, filled or removed unsatisfactorily. For example, the mark can indicate that: an SME/obscuration repair was unsatisfactory; a void fill was unsatisfactory; a cultural feature removal was unsatisfactory; or a foliage/vegetation removal was unsatisfactory. Schematic illustrations of exemplary sorted/filtered model chips  2126  with marks  2702 ,  2804  added thereto are provided in  FIGS. 27-28 . 
     In a next step  337 , the computer device (e.g., computing device  102  of  FIG. 1 ) receives a user input for viewing only a portion (e.g., a percentage) of the second model chips generated in previous step  325 . The user input is facilitated by a GUI widget (e.g., GUI widget  1020  of  FIG. 10A ) of the plug-in window (e.g., the plug-in window  902  of  FIG. 9 ). The GUI widget is configured to facilitate the display of a random sample of model chips of “items” of a particular content class for visual inspection. As such, the GUI widget may include, but is not limited to, a button for enabling/disabling a random sampling function of the feature analysis plug-in and a drop down menu from which a percentage value can be selected. 
     In response to the reception of the user input in step  337 , step  338  is performed where “N” model chips of the second model chips generated in previous step  325  are randomly selected. The value of “N” is determined based on the percentage value selected in previous step  337 . For example, if one hundred second model chips were generated in step  325  and the percentage value of twenty was selected in step  337 , then twenty model chips would be randomly selected from the one hundred second model chips. Embodiments of the present invention are not limited in this regard. 
     Upon completing step  338 , step  339  is performed where the feature analysis plug-in creates at least one screen page of sampled model chips including all or a portion of the “N” model chips arranged in a grid or matrix format. In step  340 , the screen page of sampled model chips is displayed in the plug-in window. Notably, the screen pages of sampled model chips can have a default grid size or a user-specified grid size. For example, if a grid size is four cells by four cells and “N” equals twenty, then two screen pages of sampled model chips would be created in step  339  since each screen page can contain a maximum of sixteen model chips. In contrast, if the grid size is five cells by five cells and “N” equals twenty, then only one screen page of sampled model chips would be created in step  339  since the screen page can contain a maximum of twenty-five model chips. Embodiments of the present invention are not limited in this regard. 
     A schematic illustration of an exemplary screen page of sampled model chips  2900  is provided in  FIG. 29 . As shown in  FIG. 29 , the screen page of sampled model chips  2900  includes only three model chips  2124 ,  2138 ,  2116 . In this regard, the total number of second model chips is twenty five and the percentage selected in step  337  is twelve percent. Accordingly, the value of “N” is three. The three model chips contained in the screen page  2900  were randomly selected from the twenty five second model chips  2102 - 2150  of  FIG. 21 . Embodiments of the present invention are not limited to the particularities of this example. 
     Referring again to  FIG. 3D , the method  300  continues with step  341  where the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for viewing “items” (e.g., SMEs, obscurations, voids, cultural features, and/or foliage/vegetation) of the first model chips at a user-specified zoom level of scale. The user input is facilitated by a GUI widget (e.g., GUI widget  1026  of  FIG. 10A ) of the plug-in window (e.g., plug-in window  902  of  FIG. 9 ). The GUI widget is configured to facilitate the performance of manual-scale operations by the feature analysis plug-in. The manual-scale operations are operative to adjust the zoom level of scale of all of the displayed model chips from a first zoom level of scale to a second zoom level of scale in response to a user-software interaction. The first zoom level of scale is a default zoom level of scale (e.g., 100%) or a previously user-selected zoom level of scale (e.g., 50%). The second zoom level of scale is a new user-selected zoom level of scale (e.g., 75%). As such, the GUI widget may include, but is not limited to, a drop down list populated with a plurality of whole number percentage values. 
     After the reception of the user input in step  341 , the feature analysis plug-in performs operations for automatically and concurrently generating a plurality of “fixed zoomed” model chips comprising “items” (e.g., SMEs, obscurations, voids, cultural features, and/or foliage/vegetation) at the user-specified zoom level of scale, as shown by step  342 . In a next step  343 , the feature analysis plug-in performs operations to create a screen page of “fixed zoomed” model chips. Thereafter in step  344 , the screen page of “fixed zoomed” model chips is displayed in the plug-in window. 
     A schematic illustration of an exemplary screen page of “fixed zoomed” model chips  3002  is provided in  FIG. 30 . As shown in  FIG. 30 , all of the model chips  3004  have the same zoom level of scale. As such, the smallest item V 1  (e.g., a first void) appears smaller than the larger items V 2 , V 3 , V 4  (e.g., a second, third and fourth void). Similarly, the largest item V 3  (e.g., a third void) appears larger than the smaller items V 1 , V 2 , V 4  (e.g., a first, second and fourth void). Embodiments of the present invention are not limited to the particularities of  FIG. 30 . 
     Referring again to  FIG. 3D , the method  300  continues with step  345 . In step  345 , the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for viewing the items of the first model chips (e.g., SMEs, obscurations, voids, cultural features, and/or foliage/vegetation) at a best-fit zoom level of scale. The user input is facilitated by a GUI widget (e.g., GUI widget  1028  of  FIG. 10A ) of the plug-in window (e.g., plug-in window  902  of  FIG. 9 ). The GUI widget is configured to facilitate the viewing of each displayed item at its best-fit zoom level of scale or its pre-defined maximum zoom level of scale. As such, the GUI widget may include, but is not limited to, a button for at least enabling auto-scale operations of the feature analysis plug-in and disabling the manual-scale operations of the feature analysis plug-in. 
     In response to the reception of the user input in step  345 , the feature analysis plug-in performs operations to automatically and concurrently generate a plurality of “auto zoomed” model chips comprising the currently displayed items at the best-fit zoom level of scale, as shown by step  346  of  FIG. 3D . In a next step  347  of  FIG. 3E , the feature analysis plug-in performs operations to create a screen page of “auto zoomed” model chips. Thereafter in step  348 , the screen page of “auto zoomed” model chips is displayed in the plug-in window. 
     A schematic illustration of an exemplary screen page of “auto zoomed” model chips  3102  is provided in  FIG. 31 . As shown in  FIG. 31 , each of the model chips  3104  has a different zoom level of scale. As such, all of the “items” V 1 , V 2 , V 3 , V 4  (e.g., a first, second, third and fourth void) appear to be of the same size regardless of their actual relative physical sizes. Embodiments of the present invention are not limited to the particularities of  FIG. 31 . 
     Referring again to  FIG. 3E , the method  300  continues with step  349 . In step  349 , the computing device (e.g., computing device  102  of  FIG. 1 ) receives a user input for adjusting a contrast of at least one of the “auto zoomed” model chips using a histogram thereof. The user input can be facilitated by a GUI widget (e.g., GUI widget  1022 ) of the plug-in window. The GUI widget facilitates the toggling of histogram stretch of model chips on and off. Histogram stretch leads to brighter and higher-contrast imagery. Accordingly, histogram stretch is achieved by adjusting the global contrasts of model chips or topographical models using histograms thereof. As such, the GUI widget includes, but is not limited to, a button for enabling and disabling a histogram stretch function of the feature analysis plug-in. Histogram stretch is well known in the art, and therefore will not be described herein. Still, it should be understood that histogram stretch can be turned on and off for terrain elevation data and/or for imagery. 
     In response to the user input of step  349 , the feature analysis plug-in performs operations to adjust the contrast of the “auto zoomed” model chip(s). In a next optional step  351 , the feature analysis plug-in performs operations for marking at least one of the “auto zoomed” model chips in accordance with a user-software interaction to indicate that the “item” repair/fill/removal thereof is unsatisfactory. A schematic illustration of an exemplary “auto zoomed” model chip  3104  with a mark  3106  added thereto is provided in  FIG. 31 . 
     Upon completing step  351 , step  352  is performed. Step  352  involves generating a third topographical model by satisfactorily repairing/filling/removing the “item” of the marked model chips of the second topographical model. In some scenarios, step  352  can involve satisfactorily repairing the SMEs/obscurations of marked model chips of the second topographical model, satisfactorily filling the voids of marked model chips of the second topographical model, and/or satisfactorily removing cultural features and/or foliage/vegetation so as to accurately show the bare surface thereunder. Subsequent to the generation of the third topographical model, step  353  is performed where the method  300  ends or other processing is performed. Example of the “other processing” that can be performed in step  353  will be described below in relation to  FIGS. 32-35 . 
     Referring now to  FIG. 32 , there is provided a flow diagram of an exemplary method  3200  for efficiently analyzing different types of terrain of a topographical model. As shown in  FIG. 32 , the method  3200  begins with step  3202  and continues with step  3204  where a first topographical model (e.g., topographical model  3304  of  FIG. 33 ) is displayed in an application window (e.g., application window  704  of  FIG. 33 ) of a computing device (e.g., computing device  102  of  FIG. 1 ). In a next step  3206 , a computing device receives a user input for viewing a second topographical model in which the terrain data of the first topographical model has been color coded in accordance with a classification thereof. The user input can be facilitated by a GUI widget (e.g., GUI widget  1030  of  FIG. 10A ). The GUI widget is provided to facilitate the classification of terrain of a first topographical model (e.g., topographical model  3304  of  FIG. 33 ) and the generation of a second topographical model (e.g., topographical model  3306  of  FIG. 33 ) clearly distinguishing the different types of terrain visually represented in the first topographical model. The types of terrain can be distinguished by color coding terrain elevation data of the first topographical map in accordance with a defined color scheme. Methods for color coding terrain elevation data are well known in the art, and therefore will not be described herein. However, it should be understood that any known method for color coding terrain elevation data can be used with the present invention without limitation. The GUI widget can include, but is not limited to, a button for enabling and disabling terrain classification functions of the feature analysis plug-in. 
     In response to the user input of step  3206 , the feature analysis plug-in generates the second topographical model, as shown by step  3208 . In a next step  3210 , the feature analysis plug-in creates a screen page comprising the second topographical model. This screen page is then displayed in a plug-in window of the computing device, as shown by step  3212 . A schematic illustration of an exemplary screen page  3302  comprising an exemplary color coded topographical model  3306  is provided in  FIG. 33 . 
     As shown in  FIG. 32 , the method  3200  continues with step  3214 . In step  3214 , the computing device receives a user input for viewing a plurality of model chips comprising terrain of a user-specified type (e.g., a sinkhole, a trench, a ridge or a mound). In response to this user input, the feature analysis plug-in automatically and concurrently generates a plurality of model chips, as shown by step  3216 . The model chips comprise panned and/or zoomed views of the second topographical model comprising visual representations of terrain of the user-specified type. Thereafter, the feature analysis plug-in creates at least one screen page comprising the model chips arranged in a pre-defined grid format, as shown by step  3218 . The screen page is then displayed in the plug-in window of the computing device, as shown by step  3220 . A schematic illustration of an exemplary screen page  3302  is provided in  FIG. 34 . As shown in  FIG. 34 , the screen page  3302  comprises a grid  3404  defined by a plurality of grid cells  3406 . Each of the grid cells  3406  has a model chip  3402  presented therein. 
     Referring again to  FIG. 32 , the method  3200  continues with step  3222  where other functions of the feature analysis plug-in are performed in accordance with a particular application. In this regard, step  3222  can involve performing one or more of the following: sorting functions for sorting model chips based on at least one attribute of the content thereof; filtering functions for filtering model chips based on at least one attribute of the content thereof; sampling functions for presenting a random sample of a plurality of model chips in a plug-in window; grid size change functions for changing a grid size of a grid; zoom resolution modification functions for changing a zoom resolution of one or more model chips; application window automatic panning/zooming functions for automatically panning and/or zooming a view of a topographical model displayed in an application window such that the contents of a selected model chip of a plug-in window is presented in the application window; screen page cycling operations for cycling through screen pages for a plurality of topographical models; topographical model intersection functions for filtering items, features, terrain, and/or foliage/vegetation which lie outside of an area (e.g., a geographical area) defined either by the area that a plurality of topographical models have in common or by the area covered by a plurality of topographical models taken together; marking functions for marking one or more model chips with a mark or annotation; and exporting functions for exporting marked model chips to a table or file. Subsequently, step  3224  is performed where the method  3200  ends or other processing is performed. 
     Referring now to  FIG. 35 , there is provided a flow diagram of an exemplary method  3500  for efficient terrain and feature data change detection. As shown in  FIG. 35 , the method  3500  begins with step  3504  and continues with step  3506  where a first topographical model is displayed in an application window (e.g., application window  704  of  FIG. 7 ) of a computing device (e.g., computing device  102  of  FIG. 1 ). In a next step  3506 , the computing device receives a user input for viewing a second topographical model showing which terrain and/or objects of a scene have changed. In response to this user input, the feature analysis plug-in performs steps  3508 - 3512  for generating the second topographical model. Methods for generating a topographical model including difference indication indicating a change of a scene&#39;s content are well known in the art. Any such method can be used with the present invention without limitation. One such method is described in U.S. Pat. No. 7,391,899 to Rahmes et al. This method generally involves the operations of steps  3508 - 3512 . 
     Step  3508  involves automatically identifying changes of the terrain and/or objects of the first topographical model by comparing the contents thereof with the contents of a third topographical model. Thereafter, in step  3510 , the identified changes are classified into various categories. Such categories include, but are not limited to: a positive elevation change category or a new content category including objects that are new to a scene; negative elevation change category or a deleted content category including objects that are removed from a scene; and a modified size category or a matched content category including objects whose size has increased or decreased. In a next step  3512 , the second topographical model is generated based on the results of steps  3508  and  3510 . The second topographical model includes different indications indicating changes of a scene&#39;s content. In some embodiments, the second topographical model shows changes to a scene in a topographical model as being a different color from parts of the scene that have not changed. For example, positive elevation changes are shown in green. Negative elevation changes are shown in red. Changes in object&#39;s sizes are shown in yellow. Embodiments of the present invention are not limited in this regard. 
     After the second topographical model has been generated, step  3514  is performed where the feature analysis plug-in creates a screen page comprising the second topographical model. This screen page is then displayed in a plug-in window (e.g., plug-in window  902  of  FIG. 9 ), as shown by step  3516 . Upon completing step  3516 , the method  3500  continues with step  3518 . 
     In step  3518 , the computing device receives a user input for viewing a plurality of model chips comprising visual representations of terrain and/or objects having changes of a user-specified type. In response to this user input, the feature analysis plug-in automatically and concurrently generates a plurality of model chips, as shown by step  3520 . The model chips comprise panned and/or zoomed views of the second topographical model comprising visual representations of terrain and/or objects having changes of the user-specified type. Next, in step  3522 , the feature analysis plug-in creates at least one screen page comprising the model chips arranged in a pre-defined grid format. Thereafter, the screen page is displayed in the plug-in window, as shown by step  3524 . 
     Upon completing step  3524 , the method  3500  continues with step  3526  where other functions of the feature analysis plug-in are performed in accordance with a particular targeted change detection process to verify that the changes are not relevant changes to the terrain and/or objects represented thereby. In this regard, step  3526  can involve performing one or more of the following: sorting functions for sorting model chips based on at least one attribute of the content thereof; filtering functions for filtering model chips based on at least one attribute of the content thereof; sampling functions for presenting a random sample of a plurality of model chips in a plug-in window; grid size change functions for changing a grid size of a grid; zoom resolution modification functions for changing a zoom resolution of one or more model chips; application window automatic panning/zooming functions for automatically panning and/or zooming a view of a topographical model displayed in an application window such that the contents of a selected model chip of a plug-in window is presented in the application window; screen page cycling operations for cycling through screen pages for a plurality of topographical models; topographical model intersection functions for filtering items, features, terrain, and/or foliage/vegetation which lie outside of an area (e.g., a geographical area) defined either by the area that a plurality of topographical models have in common or by the area covered by a plurality of topographical models taken together; marking functions for marking one or more model chips with a mark or annotation; and exporting functions for exporting marked model chips to a table or file. Subsequently, step  3528  is performed where the method  3500  ends or other processing is performed. 
     Although the screen pages have been described above as comprising model chips generated using terrain elevation data defining a single topographical model, embodiments of the present invention are not limited in this regard. For example, a screen page can comprise an array defined by a plurality of cells. Each cell has a model chip presented therein. The model chips can comprise panned views, zoomed views, and/or panned-and-zoomed views of one or more topographical models. For example, a first model chip comprises a panned view, a zoomed view, or a panned-and-zoomed view of a first topographical model. A second model chip comprises a panned view, a zoomed view, or a panned-and-zoomed view of a second topographical model which is different than the first topographical model. 
     Also, in some embodiments, at least two model chips include at least one item that is the same. For example, a first model chip comprises a cluster of items (e.g., cultural features). A second model chip comprises one of the items of the cluster and/or content reflecting a repaired one of the items, a filled one of the items, or a removed one of the items. In this scenario, the first and second model chips can be highlighted when the first model chip is selected. Additionally, the intensities of all other model chips can be subdued. 
     All of the apparatus, methods and algorithms disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the invention has been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the apparatus, methods and sequence of steps of the method without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain components may be added to, combined with, or substituted for the components described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined.