Digital Evaluation and Analysis Method for Color-Coded Urban Landscape Outlook System

Abstract

The present invention discloses a digital evaluation and analysis method for an urban landscape view system based on color coding, including: collecting map data of the research area and making a basic base map; determining the form of the research scope and the urban landscape view system; constructing a background environment for the view object by constructing geographical environment, background building environment and other required visual elements; respectively performing color coding on the view object and the background environment of the view system to obtain a three-dimensional urban information model; automatically simulating and collecting street view data in the urban simulation environment according to the sampling viewpoints to obtain scene photos; performing image processing on the scene photos and interpreting them according to the image color coding information; data statistics and application in planning and design; the present invention solves the problem that the limitation factors of quantitative evaluation and analysis from the perspective of people are large and affect the evaluation accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of visual analysis, and in particular to a digital evaluation and analysis method for an urban landscape viewing system based on color coding. Background Art

[0002] Visual analysis of urban landscape viewing systems has been studied both at home and abroad. The main methods of quantitative evaluation are as follows: one is landscape visual calculation and visibility analysis based on the GIS platform, and the other is quantitative calculation of visual elements through data analysis of Internet street view images. The main defects of the above two methods are as follows:

[0003] 1. GIS analysis focuses on the visibility of "objects" and ignores the information perception of "people". Spatial perception is a process in which people comprehensively perceive the concrete contents such as the form, color, and environment of visual objects. The basic principle of the GIS analysis method is to abstract the viewpoints of people into single points (static viewpoints) or multiple points (moving viewpoints), and then analyze and judge whether the surrounding space is visible with the point as the center, and then accumulate the number of times the space is visible by all viewpoints to achieve quantitative evaluation. This method can evaluate the visibility degree of regions or building bodies, but it cannot quantitatively describe the visual information at the viewpoints, that is, it cannot give a quantitative evaluation of the visual landscape quality from the perspective of people.

[0004] 2. The data source is limited, and the analysis breadth and accuracy are greatly restricted. Another way of landscape visual analysis is to use image recognition for analysis based on street view photos. Compared with GIS analysis, it can reflect the concrete information of visual objects, but it is restricted by the location and quality of data collection, and the analysis is greatly restricted. The availability of Internet street view photo data depends on the locations of the data collected by Internet companies, and the data quality (the clarity of the photos and whether they are blocked by irrelevant elements) directly affects the accuracy of the image recognition results. Image recognition technology also requires a large number of physical photos of the viewing object to be collected and trained to ensure the effective recognition of the object in street view photos. Summary of the Invention

[0005] (I) Technical Problems to be Solved

[0006] Based on the above problems, the present invention provides a digital evaluation and analysis method for an urban landscape viewing system based on color coding, which solves the problems of inability to conduct quantitative evaluation from the perspective of people and large restriction factors in analysis that affect the evaluation accuracy.

[0007] (II) Technical Solutions

[0008] Based on the above technical problems, the present invention provides a digital evaluation and analysis method for an urban landscape viewing system based on color coding, including:

[0009] S1. Collect the map data of the research area and produce a basic base map;

[0010] S2. Determine the form of the research scope and the urban landscape view system, where the urban landscape view system includes sampling viewpoints and view objects;

[0011] S3. Construct the background environment of the view system by building the geographical environment, background building environment and other required visual elements;

[0012] S4. Perform color coding on the view objects and the background environment of the view system respectively to obtain a three-dimensional model with color visual information;

[0013] S5. Automatically simulate and collect street view data in the urban simulation environment according to the sampling viewpoints to obtain scene photos, where the urban simulation environment includes view objects and the background environment of view objects;

[0014] S6. Perform image processing on the scene photos and interpret them according to the picture color coding information;

[0015] S7. Data statistics and application in planning and design.

[0016] (III) Beneficial effects

[0017] The above technical solutions of the present invention have the following advantages:

[0018] (1) The present invention focuses the research perspective of the landscape system on people, makes up for the technical defect of traditional GIS visual analysis centered on "objects", uses GIS technology to build a simplified urban white model environment of the real space, introduces the method of color coding, and uses the CGA rule modeling method to assign different color visual information to landscape objects according to a unique ID value, making up for the problems of excessive visual interference in traditional street view images, insufficient information breadth and accuracy in recognition effects. Finally, relying on the viewpoints, automatic collection is carried out in the simulation environment, and image processing technology is used to accurately identify the colors with coding information to transmit visual information, providing a basis for the quantitative evaluation of the urban landscape system and assisting in planning and design;

[0019] (2) The present invention can independently collect and generate data for analysis, expand the spatial freedom, reduce the limitations of the location and quality of data collection on the analysis, and improve the accuracy of evaluation; at the same time, unnecessary visual elements are simplified through the model, and the accuracy of image processing is increased;

[0020] (3) The present invention avoids the complex process of understanding mathematical principles, has a simple principle and conforms to the human visual cognitive process. At the same time, it can increase the characteristics of view objects according to evaluation requirements in the scene, enhancing the visual information volume. Brief description of the drawings

[0021] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as imposing any limitations on the present invention. In the drawings:

[0022] Figure 1 It is a flowchart of the digital evaluation and analysis method of the color-coded urban landscape view system according to an embodiment of the present invention;

[0023] Figure 2 It is a detailed flowchart of step S2 according to an embodiment of the present invention;

[0024] Figure 3 It is a detailed flowchart of steps S3-S4 according to an embodiment of the present invention;

[0025] Figure 4 It is a detailed flowchart of steps S5-S6 according to an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of point sampling for planar range viewpoints according to an embodiment of the present invention;

[0027] Figure 6 It is a schematic diagram of point sampling for linear range viewpoints according to an embodiment of the present invention;

[0028] Figure 7 It is a script of the function mkColor according to an embodiment of the present invention;

[0029] Figure 8 It is a schematic diagram of information model generation according to an embodiment of the present invention;

[0030] Figure 9 It is a script of the 3D information model function mkExtrusion according to an embodiment of the present invention;

[0031] Figure 10 It is a script of the color parsing function colorhsv according to an embodiment of the present invention;

[0032] Figure 11 It is a schematic diagram of the visual expression of the evaluation of the visible degree of regional landscapes according to an embodiment of the present invention. Detailed implementation manners

[0033] The following will further describe in detail the specific implementation manners of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0034] An embodiment of the present invention is a digital evaluation and analysis method for a color-coded urban landscape view system, as Figures 1-4 shown, including the following steps:

[0035] S1. Collect map data of the research area and produce a basic base map;

[0036] For the research area, data is collected through Internet maps, oblique photography, etc. to produce a data base map. The map data involved mainly includes open-source data such as Internet map images, building outlines, road alignments, DEM terrain, POI, AOI, etc., as well as street view photos or landmark building photos collected independently as information reference supplements. Among them, most of the building outline data can be downloaded from open-source maps (such as OpenStreetMap). The downloaded building outline data comes with a height attribute field. For some missing buildings, they can be obtained by manual drawing, and the height attribute can be determined according to the length of the building shadow in the map image or street view photos.

[0037] S2. Determine the form of the research scope and the urban landscape view system. The urban landscape view system includes sampling viewpoints and view objects:

[0038] S21. Determine the form of the research scope;

[0039] According to the evaluation and planning requirements, there are various forms of the research scope, including area, line, or point-shaped scopes. Among them, the area-shaped scope is to delimit a certain area for simulating the situation of a person freely moving in the area space and overlooking the surrounding environment; the line-shaped scope refers to along a linear space, such as along a road or a riverbank, for simulating the situation of a person walking along the linear space and overlooking the surrounding environment; the point-shaped scope is to simulate the situation of a person overlooking the surrounding environment at a specific location, such as an observation point of a landscape. The above scopes create area, line, and point layers in the ArcGIS software and add corresponding elements.

[0040] S22. According to the form of the research scope, determine the sampling viewpoints of the urban landscape view system:

[0041] The sampling viewpoints are determined according to the form of the research scope, that is, different forms of area, line, or point-shaped scopes. The determination methods are as follows:

[0042] Among them, for the area-shaped scope, several viewpoints are generated through the Create Fishing Net tool in ArcGIS. By deleting the grid points covered by buildings and then supplementing points as needed to form sampling viewpoints, as Figure 5 shown;

[0043] For the line-shaped scope, the following operations are performed on the linear elements in the ArcGIS software: 1) Sequentially perform the "Feature to Point" operation to obtain the midpoint of the line element, and then perform the operation of "Split Line at Feature to Point" to evenly divide the original line segment by length; 2) Then repeat operation 1) on the divided line segments. According to the density of the required sampling viewpoints, determine the number of repetitions N to obtain 2N equally divided sampling viewpoints with equal intervals, as Figure 6 shown;

[0044] For the point-shaped scope, directly draw and add the determined sampling viewpoints as point elements.

[0045] The above-mentioned various sampling viewpoints are uniquely numbered by adding an attribute field ID for easy recording.

[0046] S23. Extract the overlooking objects of the urban landscape overlooking system:

[0047] The overlooking objects refer to the landscape elements in the urban overlooking system, including artificial landscape elements (such as landmark buildings) and natural landscape elements (such as mountain landscapes). The determination methods of the above landscape elements are as follows:

[0048] S231. Extract natural landscape elements, and the natural landscape elements include mountain landscapes.

[0049] Determine the geographical location of the landscape elements according to the POIs and AOIs of the landscape type, and extract the natural landscape element data through the following methods:

[0050] S2311. Convert the terrain raster data into vector line data with elevation information, and then generate closed contour surface element data through the feature to polygon tool;

[0051] In the ArcGIS software, through the 3D Analyst tool \ Raster Surface \ Contour tool, convert the terrain raster data into vector line data with elevation information, and then generate closed contour surface element data through the feature to polygon tool;

[0052] S2312. Delimit the natural landscape element range surface based on the landscape AOI range information.

[0053] Delimit the natural landscape element range (surface element) based on the landscape AOI range information. For some natural mountains without AOI data, contour lines can be generated from the terrain raster data, and the closed contour lines are selected at the location of the landscape POI points to generate the surface element for determination;

[0054] S2313. Clip the contour lines (surface elements) within the natural landscape element range surface as the natural landscape element data;

[0055] S2314. Cut the natural landscape elements from the terrain and fuse and update the terrain raster data.

[0056] After extracting the natural landscape elements (mountain elements), in order to avoid overlapping with the terrain raster data within the range after superposition, the original terrain raster data needs to be updated. The specific method is as follows:

[0057] Traverse the contour line values within the mountain landscape element range surface in sequence, filter out the minimum value, convert the mountain landscape element range surface into raster data according to the minimum elevation value, and then fuse it with the terrain raster data through the mosaic tool, so as to achieve the effect of cutting the mountain landscape elements from the terrain.

[0058] The above realizes the extraction of natural landscape elements and generates terrain raster data for separating natural landscape elements. Among them, AOI (Area of Interest) refers to the area of interest in the Internet electronic map, which is mainly used to express regional geographical entities in the map, such as a residential community, a university, an office building, an industrial park, a comprehensive shopping mall, a hospital, a scenic spot or a stadium, etc.; POI (Point of Interest), on the electronic map, POI represents restaurants, supermarkets, government agencies, tourist attractions, transportation facilities, and so on.

[0059] S232. Extract artificial landscape elements, and the artificial landscape elements include landmark buildings.

[0060] The general geometric features of landmark buildings are high building height and large building land area. Their functions are generally for commercial office, public service, and cultural heritage uses, and they also have a certain popularity locally. According to the above features, the following methods can be used to determine landmark buildings:

[0061] S2321. Screen the building outlines according to the geometric properties of the buildings;

[0062] Obtained by taking the top 10% of the data sorted from high to low by the building height (BuildingHeight) and area (Shape_Area) fields of the building outline data.

[0063] S2322. Use reverse geocoding to obtain the building functions and screen out the building outlines for commercial office and public buildings;

[0064] Taking the longitude and latitude of the center point of the building outline (WGS84 coordinates) as the request parameters, first convert them to the coordinate values in the Internet map coordinate system through the Internet map coordinate system conversion API, and then use these coordinate values to obtain building information including building use, address, building name, etc. through the Internet map reverse geocoding and record them by establishing corresponding data attribute fields; screen out the relevant outline data for commercial office and public buildings, etc.

[0065] S2323. Through POI, AOI, and the information collected and sorted, supplemented by a travel software APP, screen out the landmark building outlines of historical and cultural categories;

[0066] Through POI, AOI, and the information collected and sorted, supplemented by a travel software APP, find out the landmark building outlines of historical and cultural categories and record their attribute information such as building use, address, building name, etc.

[0067] S2324. After verifying the popularity of the screened building outlines, use them as landmark building elements;

[0068] Verify the local popularity of the landmark buildings and related information selected above through means such as on-site investigations and the Internet.

[0069] S3. Construct the background environment of the object to be overlooked by constructing the geographical environment, the background building environment, and other required visual elements:

[0070] S31. Construct the geographical environment, including terrain raster data, road element data, and block data

[0071] a) Load the terrain raster data of the base map;

[0072] b) Load roads: Use the buffer tool to perform buffer operations on the road line elements according to the width values determined by the road levels to generate road surface element data;

[0073] c) Load block data: Use the feature to polygon tool in ArcGIS software to generate block surfaces from the enclosed areas of the closed road line elements, establish a block ID attribute field to uniquely identify each block surface; assign the attribute information of the block ID where the building outline data is located according to the spatial position.

[0074] S32. Generate the background building environment:

[0075] Erase the landmark building outlines from the original building outline data as the outline data of the background buildings, import the building outline vector data into the scene, and use the Extrude function in the CGA rules of CityEngine software to automatically stretch it according to the attribute values of the height attributes in the building outline vector data to form building blocks, generating the background building environment.

[0076] S33. Import the object to be overlooked:

[0077] 1) Encode the mountain landscape data (natural landscape elements) and landmark building (artificial landscape elements) data. Uniformly encode the mountain landscape data and landmark building data with attribute information generated in step S2.

[0078] 2) Import the integrated data into CityEngine software. The above landscape element data will be modeled according to the color coding method in step S4 later.

[0079] S34. Create other required visual elements:

[0080] Other required visual elements refer to the model elements that actually exist in the scene and affect the evaluation results when simulating the overlooking behavior of a human, such as street trees with an obstructive effect in the visual evaluation scene along the road, and woods with a relatively high density in the park landscape evaluation. These elements can, according to the evaluation needs, first determine the element positions, and then import the point element vector data into the scene of the CityEngine software, and add the corresponding white models at the point positions by writing a CGA rule file.

[0081] S35. Load the terrain raster data according to the terrain layer, load the road surface element data and the block surface element data, and after sequentially loading the other required visual elements and the overlooking objects of the background building environment, project them vertically onto the terrain raster graphics of the geographical environment through the AlignShapes to Terrain function so as to be close to the terrain, thereby constructing the background environment of the overlooking objects.

[0082] S4. Perform color coding on the overlooking object and the background environment of the overlooking system respectively to obtain a three-dimensional model with color visual information:

[0083] Color coding is a type of coding, which is a visual information coding using colors as codes. In order to obtain information from the images generated from the sampling viewpoints, it is necessary to code the attribute information of the overlooking objects into specific colors according to certain rules. The corresponding color coding parsing process is to reverse-decode the color information into attribute information.

[0084] The specific implementation process of the color coding in the present invention is as follows: During the process of generating the three-dimensional information model of the landscape elements, the information attribute values (or category code values) are read and calculated and converted into a uniquely corresponding RGB color value, and then the color is used to dye the specified model area through the built-in function color of the CGA rule, so that the model obtains the color visual information. This color coding adopts the HSV color model to uniquely represent specific information values with H (hue), V (saturation), and S (brightness). The main operation process is as follows:

[0085] S41. Write a color coding function through the CGA rule and generate a three-dimensional model of the landscape elements, and the landscape elements are the overlooking objects.

[0086] S411. Set attribute parameters. Set the attribute parameters of the current element in the CGA rule file. Taking the object ID number as an example, the expression is like attr id = 0, where the attribute variable name of the ID number is the same as the attribute name of the landscape element data. In the CityEngine software, the default value of the specific value of this variable is 0. Once this function is applied, it automatically matches the attribute field with the same attribute name and assigns the ID value of this data to id; another variable is the maximum ID value, and the expression is like attr idMax = 100, and its value is the maximum id value among all landscape elements.

[0087] S412. Convert the ID value to an HVS color value.

[0088] In the HSV color model, the value range of H (hue) in the CGA rule of the CityEngine software is 0 - 360. By evenly dividing 0 - 360 into idMax + 1 parts and taking the middle value of the corresponding id interval, the ID variable value is converted into an interval serial number from 0 to 360, thus establishing a corresponding relationship with the H value. The other V (saturation) and S (brightness) are fixed at 1. The calculation formula of the H value is as follows: 180 / (idMax + 1) + (id - 1) * 360 / (idMax + 1). This calculation method can obtain the corresponding color code of the current element ID, which is not likely to conflict with the background color value (white or gray, where gray is the deviation of white caused by shadows) and the identification color (black) while ensuring the maximum discrimination.

[0089] S413. Color the object to be overlooked according to the HVS color value.

[0090] Take the HVS color value in S412 as the parameter H input value of the built-in function colorHSVToHex in the CGA rule, and input other V and S as 1. The output result is the hexadecimal color value of this color value. Use the three functions colorHexToR, colorHexToG, and colorHexToB to obtain the Red, Green, and Blue channel values of this hexadecimal color in the RGB color model respectively. Thus, the color function can be called to color the model area, realizing the function of converting the information attribute variable into a single-color visual information. The example of this function is mkColor as Figure 7 shown;

[0091] S414. Construct an information model generation function. Landscape elements generate a 3D model by establishing two layers of models, namely the main body layer and the identification layer. The main body layer is the main part of the model of the landscape element. After being extruded to the building height by the extrusion function extrude, it is a 3D model body with color-coded information assigned to the corresponding color according to the ID attribute of the element. The identification layer is based on the main body layer and is extruded by 2 unit heights (for example, set the variable name as unitHeight). This unit height is set based on normal display in the graph and can be used as a conversion unit between pixels and actual dimensions.

[0092] For the two layers of the identification layer, black (identification color) and white (background color) are respectively assigned. The setting of the upper white layer is to further prevent the adhesion of similar colors and separate overlapping objects; the lower black layer serves to quickly mark the positions of objects with a unified color. At the same time, since the actual simulated height of this layer is known, the actual height of the main body layer can be deduced from the pixel height of this image, playing the role of a scale.

[0093] Assuming that the maximum ID is 10, the schematic diagram of the information model generation is as Figure 8 shown;

[0094] Combined with the color-coding function of the figure, the 3D information model function mkExtrusion of the above landscape elements is expressed as Figure 9 shown;

[0095] S42. Set the background color for the background model to simplify the scene. The 3D models of landscape elements are colored according to the coding mode, and the 3D models of other building outlines are colored to (1, 1, 1) which is white using the color function, so as to obtain a scene that highlights visual objects and weakens background information. Due to the occlusion of scene shadows, some areas covered by shadows appear gray, so they are also set as the background model color.

[0096] S5. Automatically simulate and collect street view data in the urban simulation environment according to the sampling viewpoints to obtain scene photos. The urban simulation environment includes the viewing object and the background environment of the viewing object:

[0097] Import the sampling viewpoints into the scene created in step S4, and through writing a Python automation script, the following operations are realized:

[0098] S51. Pick up the sampling points in sequential order and move the camera lens to that point;

[0099] S52. Set the parameters of the camera lens according to the general observation characteristics of the average human height, with an elevation angle of 30°, a horizontal field of view of 120°, and a height of 1.6 m. Other viewing angle roles can also be set according to needs, such as the child's perspective.

[0100] S53. At the sampling point, intercept the scene photos with the lens rotated 0°, 90°, 180°, and 270° (i.e., -90°) along the y-axis respectively, and name them in the form of unified letter identification, viewpoint ID, and perspective content connected by a unified "_", such as IMG_01_90.png, and save them to the specified folder.

[0101] S6. Perform image processing on the scene photos, and reverse interpret the information represented by the colors in the pictures according to the rules of the color coding:

[0102] Perform image processing on the scene pictures generated in step S5, and use the OpenCV module to write a Python script to implement the interpretation of the image color coding information.

[0103] S61. Identify the identification layer in the image.

[0104] 1) Take the color of the identification layer (black) as the ROI (region of interest), specify the corresponding interval of black in the HSV color mode, and select all the identification layers through the OpenCV module function cv2.inRange. 2) Obtain the image position information of all ROIs: After binarizing the image using the cv2.threshold function, detect the contour information of all ROIs through cv2.findContours, and store the detection information in contours; traverse contours and obtain the coordinate values of the bounding rectangles of the contours one by one by calling cv2.boundingRect, including x, y, w, h, where x and y are the upper left coordinates, and w and h are the width and height of the matrix, so as to determine the position (x, y) of the overlooking object in the image and the height h of the corresponding image of the model.

[0105] S62. Obtain a binary background image. Take the colors of the background building and the environment (white, gray) as the ROI (region of interest), specify the corresponding intervals of white and gray in the HSV color mode (the white and gray color spaces are basically continuous intervals), select the ROI area through the OpenCV module function cv2.inRange, and perform binarization processing through the cv2.threshold function. For the processed result, the area with a pixel value of 1 in the image belongs to the original background, and the area with a pixel value of 0 belongs to the imaging area of the landscape elements in the image.

[0106] S63. Calculate the image area of the main body layer of the calculation element. Construct a convolution kernel of h*h with the circumscribed rectangle of the identification layer calculated in 1). Take the upper left coordinate of the element identification and round up the deviation to the right by w / 2, that is, the center of the first row of the identification layer ROI (x, y + int(w / 2)) as the starting point and scan the binary background image downward by h. Stop scanning only when the pixel values within the convolution kernel are all 1. Record the number of scans n. Calculate the position information of the ROI area of the main body layer. The width is w, the height is n*h, the upper left coordinate is the position where the identification layer is shifted down by one layer height, that is, (x+h,y). The area of the region is n*w*h. The above image information can be used to obtain the actual visible range according to the actual size unitHeight corresponding to h.

[0107] S64. Calculate the color of the main body layer of the element and parse the ID value.

[0108] To ensure the accuracy of color parsing, take the central position area of the ROI of the main body layer in the original image as the color value for parsing the ID value of the element. The center point coordinates are (x +h + int(h*n / 2)), y+int(w / 2) ), where the int function represents rounding. Obtain the value in the HSV color mode at this point. In the Python programming environment, the value range of H is (0,180). Therefore, according to the function logic during encoding in the CGA rule, the entire interval is divided into idMax+1 equal parts. This color value is the median of the id-th interval, where idMax is the maximum ID value of all elements set when the model applies the CGA rule for encoding, and id is the ID encoding of the model. Therefore, by dividing the H value by the equal division interval and taking the floor value and adding 1, the index number of the interval where it is located, that is, the element id value, can be obtained, and then the id value of the element can be parsed. The Python script function is as Figure 10 shown, where colorhsv is the hsv color value of the central position of the ROI of the main body layer;

[0109] According to the above operations, the following information content can be obtained: 1) Image parsing information: the ID number of the landscape elements presented in the image, the position, length, width and visible area of the visible area of the image; 2) Viewpoint information: By extracting the numbered information named in the photo and the Python script viewpoint parameters of the CityEngine software, the viewpoint ID, observation direction, elevation angle, horizontal field of view, view height and other perspective simulation information for generating the picture can be obtained, and the actual viewpoint position, elevation and other information can be associated by the ID; 3) Actual information: According to the conversion relationship between the image pixels and the actual size, the actual visible height, area, visible true height of the landscape elements at the viewpoint and all actual attribute information associated with the element ID can be measured.

[0110] S65. Uniformly input the parsed content of the above pictures into text for analysis and statistics.

[0111] S7. Statistically analyze the interpreted information and apply it to planning and design.

[0112] (1) Evaluation of the visibility of regional landscapes. Extract the viewpoint ID and the visible area of landscape element images from the text information to evaluate the visibility of regional landscapes. Summarize the visible area of element images by viewpoint ID, associate the viewpoint with the statistical results under this viewpoint through an attribute connection tool, and achieve a visual expression of the results through gridification based on ArcGIS, as Figure 11 shown, where the color depth represents the visibility of the current landscape element, providing a basis for the selection of viewing point locations in planning and design.

[0113] (2) Evaluation of the visibility integrity of regional landscapes. Extract the landscape element ID, the visible height, and the actual height of the landscape from the text information to evaluate the visibility integrity of regional landscapes. The visibility integrity refers to the ratio of the calculated visible area of the landscape elements that can be seen, that is, the visible height / the actual height. Aggregate the selected data by landscape element ID, and the aggregation method is to take the average value of the ratio of the visible area of the elements. Associate the viewpoint position by attribute based on the ArcGIS software, and achieve a visual expression of the results through gridification based on ArcGIS. The evaluation results provide a reference basis for selecting important landscape elements and comparing the landscape effects under different height controls.

[0114] (3) Analysis of the orientation of landscape elements. Extract the viewpoint ID and the landscape element ID from the text information, establish an OD analysis through ArcGIS, so as to express the degree of tightness of the visual association between landscape elements and regional viewpoints. The evaluation results can provide a reference for setting the position of landscape visual corridors guided by landscape resources.

[0115] In summary, through the above digital evaluation and analysis method of an urban landscape viewing system based on color coding, the following beneficial effects are achieved:

[0116] (1) The present invention focuses the research perspective of the landscape system on people, makes up for the technical defect of traditional GIS visual analysis centered on "objects", uses GIS technology to build a simplified urban white model environment of the real space, introduces a color coding method, and uses the CGA rule modeling method to assign different color visual information to landscape objects according to a unique ID value, making up for the problems of excessive visual interference in traditional street view images and insufficient information breadth and accuracy in recognition effects. Finally, rely on the viewpoint to automatically collect in the simulation environment, use image processing technology to accurately identify the colors with coding information to transmit visual information, provide a basis for the quantitative evaluation of the urban landscape system, and assist in planning and design;

[0117] (2) The present invention can independently collect and generate data for analysis, expand the spatial freedom, reduce the limitations of the position and quality of data collection on analysis, and improve the accuracy of evaluation; at the same time, unnecessary visual elements are simplified through the model, and the accuracy of image processing is increased.

[0118] (3) The present invention avoids the process of understanding complex mathematical principles, has a simple principle and conforms to the human visual cognitive process. At the same time, it can increase the characteristics of the overlooking object according to the evaluation requirements in the scene, and enhance the visual information volume.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the embodiments of the present invention are described in conjunction with the drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A digital evaluation and analysis method for an urban landscape view system based on color coding, characterized in that, Including: S1. Collect map data of the research area and produce a basic base map; S2. Determine the form of the research scope and the urban landscape view system, where the urban landscape view system includes sampling viewpoints and view objects; S3. Construct the background environment of the view object by constructing the geographical environment, background building environment and other required visual elements; the other required visual elements are model elements that actually exist in the scene and will affect the evaluation results when simulating the viewing behavior of people; S4. Conduct color coding on the view object and the background environment of the view object respectively to obtain a three-dimensional model with color visual information; S5. Automatically simulate and collect street view data in the urban simulation environment according to the sampling viewpoints to obtain scene photos, where the urban simulation environment includes the view object and the background environment of the view object; S6. Perform image processing on the scene photos, and reverse interpret the information represented by the colors in the pictures according to the rules of the color coding; S7. Conduct data statistical analysis on the interpreted information and apply it to the planning and design.

2. The digital evaluation and analysis method of the color-coded urban landscape viewing system according to claim 1, wherein The map data includes open-source data of Internet map images, building outlines, road alignments, DEM terrain, POIs, AOIs, street view photos, and landmark building photos collected independently.

3. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 1, characterized in that The step S2 includes: S21. Determine the form of the research scope, where the form of the research scope includes a planar scope, a linear scope or a point scope; S22. Determine the sampling viewpoints of the urban landscape view system according to the form of the research scope; S23. Extract the view objects of the urban landscape view system, where the view objects include natural landscape elements and artificial landscape elements.

4. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 3, characterized in that Step S22 includes: The viewpoints of the planar scope are generated by using the Create Fishing Net tool in ArcGIS. By deleting the grid points covered by buildings and then supplementing the points as needed to form sampling viewpoints; The viewpoints of the linear scope are obtained by performing operations on the linear elements in the ArcGIS software: successively perform Feature to Point and split the line at the Feature to Point locations to achieve an average division of the original line segment by length; then repeat the above operations on the divided line segments, and determine the repetition times N according to the density of the required sampling viewpoints to obtain 2N equal divisions with equal intervals of sampling viewpoints; For the point scope, directly draw and add the determined sampling viewpoints as point elements.

5. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 3, characterized in that Step S23 includes: S231. Extract natural landscape elements, where the natural landscape elements include mountain landscape; including: S2311. Convert the terrain raster data into vector line data with elevation information, and then generate closed contour surface element data through the Feature to Polygon tool; S2312. Delimit the natural landscape element scope based on the landscape AOI range information; S2313. Clip the contours within the natural landscape element scope surface as natural landscape element data; S2314. Cut the natural landscape elements from the terrain and fuse and update the terrain raster data; S232. Extract artificial landscape elements, where the artificial landscape elements include landmark buildings; including: S2321. Screen the building outlines according to the geometric properties of the buildings; S2322. Obtain the building function using reverse geocoding and screen out the building outlines for commercial office and public buildings. S2323. Through POI, AOI, and the information collected and sorted, supplemented by a tourism software APP, screen out the outlines of landmark buildings of historical and cultural categories. S2324. After verifying the popularity of the screened building outlines, use them as landmark building elements.

6. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 3, characterized in that The above S3 includes: S31. Construct the geographical environment, including terrain raster data, road element data, and block data. Load the terrain raster data of the base map. Load the road element data: Through the buffer tool, buffer the road line elements according to the width value determined by the road level to generate road surface element data. Load the block data: Use the feature to polygon tool to generate block surfaces from the enclosed areas of the closed road line elements, establish a block ID attribute field to uniquely identify each block surface; through the spatial join of ArcGIS software, assign the attribute information of the block ID where the building outline data is located according to the spatial position. S32. Generate the background building environment: Erase the landmark building outlines from the original building outline data to obtain the outline data of the background buildings, import the outline data of the background buildings into the scene, and automatically stretch them according to the attribute values of the height attributes in the building outline vector data to form building blocks, generating the background building environment. S33. Import the viewing objects: Uniformly encode the data of the natural landscape elements and artificial landscape elements with attribute information. S34. Create other required visual elements. S35. Load the terrain raster data according to the terrain layer, load the road surface element data and block surface element data, and after sequentially loading the background building environment, other required visual elements, and viewing objects, vertically project them onto the terrain raster graphics of the geographical environment to make them close to the terrain, thereby constructing the background environment of the viewing objects.

7. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 1, characterized in that The above step S4 includes: S41. Write a color coding function through CGA rules and generate a three-dimensional information model of the landscape elements. S411. Set the attribute parameters of the current element in the CGA rule file. S412. Convert the ID value consistent with the attribute name of the landscape element data into an HVS color value. S413. Color the model according to the HVS color value. S414. Construct an information model generation function: The landscape elements generate a three-dimensional model of the landscape elements by establishing two layers of models, namely the main body layer and the identification layer. The main body layer is the main part of the model of the landscape elements. After being stretched to the building height by the stretch function extrude, a three-dimensional model body with corresponding color coding information assigned according to the corresponding ID attribute is obtained; the identification layer is based on the main body layer and is stretched 2 unit heights. The two layers of the identification layer are respectively assigned black as the identification color and white as the background color. S42. Set the background color of the background model: Color the three-dimensional models of other building outlines except the three-dimensional models of the landscape elements white, and the areas covered by shadows appear gray.

8. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 1, characterized in that The above step S5 includes: S51. Pick sampling points in sequential order. S52. Set the elevation angle, horizontal field of view, and height parameters of the camera lens according to the general observation characteristics of the average human height. S53. At the sampling points, intercept the scene photos when the lens rotates 0°, 90°, 180°, and 270° along the y-axis respectively, and name them in the form of unified letter identification, viewpoint ID, and perspective content connected by a unified "_", and save them to the specified folder.

9. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 1, characterized in that Step S6 includes: S61. Use the identification layer color as the ROI to identify the identification layer in the image. S62. Use the background building and environmental colors as the ROI to obtain a binary background image for differentiation. S63. Calculate the image area of the main element layer. S64. Calculate the color of the main element layer and parse the ID value.

10. The digital evaluation and analysis method of the color-coded urban landscape view system according to claim 1, characterized in that, Step S7 includes the evaluation of the visibility degree of the regional landscape, the evaluation of the integrity degree of the visible regional landscape, and the analysis of the orientation of the landscape elements.

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