A data processing method, device, electronic device, and computer-readable medium

Abstract

The application discloses a data processing method and device, electronic equipment and computer readable medium. The method comprises the following steps: after obtaining image data for describing at least one building, first determining edge point sets corresponding to the buildings from the image data, so that the edge point sets are used for recording pixel points located at edge positions in all pixel points occupied by the corresponding buildings on the image data, so that the edge point sets can describe the positions of the corresponding buildings in the image data with fewer pixel point coordinates; then, for any building, the position information of the building is analyzed by using the edge point set corresponding to the building, so that the position information can represent the position of the building, so that the purpose of automatically analyzing the position of the building from the image data can be achieved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, apparatus, electronic device, and computer-readable medium. Background Technology

[0002] In some application scenarios (such as 3D building model reconstruction and building lighting analysis), it may be necessary to use some building location information to complete a certain processing task (such as 3D building model reconstruction).

[0003] However, in some cases, it may be impossible to directly obtain the location information of these buildings, thus making it impossible to complete the above processing tasks. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a data processing method, apparatus, electronic device, and computer-readable medium.

[0005] To achieve the above objectives, the technical solution provided in this application is as follows:

[0006] This application provides a data processing method, the method comprising:

[0007] Acquire image data, wherein the image data carries at least one pixel corresponding to a building;

[0008] Determine the set of edge points corresponding to each building from the image data;

[0009] For any of the aforementioned buildings, the location information of the building is analyzed using the set of edge points corresponding to the building.

[0010] In one possible implementation, analyzing the location information of the building using the set of edge points corresponding to the building includes:

[0011] The set of edge points corresponding to the building is simplified.

[0012] The location information of the building is determined based on the simplified set of edge points.

[0013] In one possible implementation, the simplification process includes:

[0014] If a first type of point exists in the set of edge points corresponding to the building, then the first type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes a first neighbor point corresponding to the first type of point and a second neighbor point corresponding to the first type of point. The first neighbor point and the second neighbor point are located on the same straight line, and the first type of point is not located on the straight line.

[0015] In one possible implementation, the first type of point is located at the tip of a serrated shape formed by the first type of point, the first neighboring point, and the second neighboring point.

[0016] In one possible implementation, the simplification process includes:

[0017] If a second type of point exists in the set of edge points corresponding to the building, then the second type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes several candidate points that are sequentially adjacent in position. The candidate points are located on the same straight line. The second type of point includes all points on the straight line except for the two endpoints.

[0018] In one possible implementation, the simplification process includes:

[0019] If a third type of point exists in the set of edge points corresponding to the building, then the third type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes the third neighbor point and the fourth neighbor point corresponding to the third type of point. The distance from the third type of point to the straight line formed by the third neighbor point and the fourth neighbor point is less than a first threshold.

[0020] In one possible implementation, the simplification process includes:

[0021] If a fourth type of point exists in the edge point set corresponding to the building, then the fourth type of point is deleted from the edge point set corresponding to the building. The edge point set corresponding to the building includes the fifth neighbor point corresponding to the fourth type of point, the sixth neighbor point corresponding to the fourth type of point, and the seventh neighbor point corresponding to the sixth neighbor point. The distance between the fifth neighbor point and the fourth type of point is greater than a second threshold, the distance between the fourth type of point and the sixth neighbor point is not greater than the second threshold, the distance between the sixth neighbor point and the seventh neighbor point is greater than the second threshold, the distance from the fourth type of point to the straight line formed by the fifth neighbor point and the seventh neighbor point is not greater than a third threshold, and the distance from the sixth neighbor point to the straight line formed by the fifth neighbor point and the seventh neighbor point is not greater than the third threshold.

[0022] In one possible implementation, the simplification process includes:

[0023] If a fifth type of point exists in the edge point set corresponding to the building, then the fifth type of point is deleted from the edge point set corresponding to the building. The edge point set corresponding to the building includes the eighth neighbor point corresponding to the fifth type of point, the ninth neighbor point corresponding to the fifth type of point, and the tenth neighbor point corresponding to the eighth neighbor point. The distance between the tenth neighbor point and the eighth neighbor point is greater than a second threshold, the distance between the eighth neighbor point and the fifth type of point is not greater than the second threshold, the distance between the fifth type of point and the ninth neighbor point is greater than the second threshold, the distance from the eighth neighbor point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than a third threshold, the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than the third threshold, and the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is less than a first threshold.

[0024] In one possible implementation, the process of determining the simplified edge point set includes:

[0025] If there are first-type points in the set of edge points corresponding to the building that satisfy the preset sawtooth simplification conditions, then delete the first-type points from the set of edge points corresponding to the building to obtain the sawtooth simplified set.

[0026] If there is a second type of point in the simplified zigzag set that satisfies the preset intermediate point simplification condition, then the second type of point is deleted from the simplified zigzag set to obtain the simplified intermediate point set.

[0027] If there is a third type of point in the simplified set of intermediate points that satisfies the preset three-point distance simplification condition, then the third type of point is deleted from the simplified set of intermediate points to obtain the simplified set of three-point distance.

[0028] If there is a fourth type of point in the simplified three-point distance set that satisfies the preset four-point angle simplification conditions, then the fourth type of point is deleted from the simplified three-point distance set to obtain the simplified four-point angle set.

[0029] If there is a fifth type of point in the simplified set of four-point angles that satisfies the preset intermediate point simplification conditions, then the fifth type of point is deleted from the simplified set of four-point angles to obtain the simplified edge point set.

[0030] In one possible implementation, the method further includes:

[0031] For any of the buildings, a three-dimensional model of the building is constructed using the building's location information;

[0032] Display a three-dimensional model of at least one of the buildings.

[0033] In one possible implementation, the process of acquiring the image data includes:

[0034] When the map is in display mode, the map display mode is adjusted according to the map display configuration parameters corresponding to the target building group, so that the adjusted map is used to display the target building group. The map display configuration parameters include center point parameters and zoom parameters. The center point parameters are determined based on the center position information of the target building group, and the zoom parameters are determined based on the area occupied by the target building group on the map.

[0035] The adjusted map is screenshotted to obtain the image data.

[0036] In one possible implementation, determining the set of edge points corresponding to each building from the image data includes:

[0037] The pixels in the image data other than those corresponding to at least one building are hollowed out to obtain a building description image, which includes the pixels corresponding to the at least one building.

[0038] If there are sixth type points and adjacent points corresponding to the sixth type points in the building description image, and the number of adjacent points corresponding to the sixth type points reaches the fourth threshold, then the sixth type points in the building description image are hollowed out to obtain an edge point description image, which includes the edge points corresponding to the at least one building.

[0039] The pixels in the edge point description image are grouped to obtain the edge point set corresponding to each building.

[0040] This application provides a data processing apparatus, including:

[0041] An acquisition unit is used to acquire image data, wherein the image data carries at least one pixel corresponding to a building;

[0042] A determining unit is used to determine the set of edge points corresponding to each of the buildings from the image data;

[0043] The analysis unit is used to analyze the location information of any of the buildings by using the set of edge points corresponding to the building.

[0044] This application provides an electronic device, the device comprising: a processor and a memory;

[0045] The memory is used to store instructions or computer programs;

[0046] The processor is configured to execute the instructions or computer program in the memory, so that the electronic device performs the data processing method provided in this application.

[0047] This application provides a computer-readable medium storing instructions or computer programs that, when executed on a device, cause the device to perform the data processing method provided in this application.

[0048] This application provides a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the data processing method provided in this application.

[0049] Compared with the prior art, this application has at least the following advantages:

[0050] In the technical solution provided in this application, after obtaining image data (e.g., map screenshots) for describing at least one building, the set of edge points corresponding to each building is first determined from the image data. This set of edge points is used to record the pixels located at the edge positions among all the pixels occupied by the corresponding building in the image data, so that the set of edge points can describe the position of the corresponding building in the image data with fewer pixel coordinates. Then, for any building, the location information of the building is analyzed using the set of edge points corresponding to the building, so that the location information can represent the location of the building. This achieves the purpose of automatically analyzing the location of the building from the image data.

[0051] In addition, since the edge point set corresponding to a building is used to record the pixels located at the edge position among all the pixels occupied by the building in the image data, the edge point set can describe the location of the building in the image data with as few pixels as possible. This makes the resources (such as time resources, computing resources, etc.) required to analyze the location information of the building based on the edge point set relatively small, which helps to reduce the resource consumption of the location analysis process. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a data processing method provided in an embodiment of this application;

[0054] Figure 2 A schematic diagram of image data provided in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of a simplification process for sawtooth patterns provided in an embodiment of this application;

[0056] Figure 4 A schematic diagram illustrating a sawtooth pattern provided in an embodiment of this application;

[0057] Figure 5 A schematic diagram illustrating a simplified intermediate point scenario provided for an embodiment of this application;

[0058] Figure 6 A simplified diagram illustrating the distance between three points provided in an embodiment of this application;

[0059] Figure 7 A schematic diagram illustrating a simplified four-point angle configuration provided for an embodiment of this application;

[0060] Figure 8 A simplified schematic diagram of an edge point set provided for an embodiment of this application;

[0061] Figure 9 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0062] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0064] To better understand the technical solution provided in this application, the data processing method provided in this application will be explained below with reference to some accompanying drawings. For example... Figure 1 As shown, the data processing method provided in this application includes S1-S3 as described below. Wherein, the... Figure 1 This is a flowchart of a data processing method provided in an embodiment of this application.

[0065] S1: Acquire image data, which carries at least one pixel corresponding to a building.

[0066] Image data refers to the reference data needed when analyzing the location of buildings (e.g., Figure 2 The image data shown includes at least one pixel corresponding to a building, so that the image data can be used to describe these buildings. It should be noted that, for any building, the pixel corresponding to the building is used to describe the area occupied by the building in the image data, so that the location of the building can be analyzed subsequently using some or all of the pixels corresponding to the building; moreover, this application does not limit the pixel corresponding to the building; for example, the pixel corresponding to the building may include pixels appearing within the area occupied by the building in the image data.

[0067] In addition, this application does not limit itself to buildings; for example, it may refer to apartment buildings or other types of buildings.

[0068] Furthermore, this application does not limit the above phrase "at least one building," for example, it could refer to all the buildings in a certain community.

[0069] Furthermore, this application does not limit the method of acquiring the image data mentioned above. For example, it can be implemented using any existing or future method of acquiring image data that can describe the location of some buildings.

[0070] In fact, since maps can depict the location of buildings, they can be used to determine the image data mentioned above. Based on this, this application also provides a method for obtaining the image data mentioned above, which may specifically include steps 11-12 below.

[0071] Step 11: When the map is in display mode, adjust the map display mode according to the map display configuration parameters corresponding to the target building group so that the adjusted map can be used to display the target building group. The map display configuration parameters include center point parameters and zoom parameters. The center point parameters are determined based on the center location information of the target building group, and the zoom parameters are determined based on the area occupied by the target building group on the map.

[0072] The map is used to describe the location of some buildings; and this application is not limited to the map, which can be implemented using any existing or future digital map.

[0073] Furthermore, this application does not limit the way the map above is displayed. For example, it can be displayed using certain applications or web pages.

[0074] The target building complex refers to a group of buildings for which the location of buildings needs to be determined; and the target building complex includes at least one building. It should be noted that this application does not limit the target building complex; for example, it could be a residential community or... Figure 2 The building complex shown.

[0075] The map display configuration parameters corresponding to the target building complex refer to the map display configuration parameters required when performing positioning processing on the target building complex; moreover, the map display configuration parameters are determined based on the relevant information of the target building complex (such as the coordinates of the center point, the size of the area it occupies, etc.).

[0076] In addition, the map display configuration parameters for the target building complex mentioned above can include at least a center point parameter and a zoom parameter. These two parameters are described below.

[0077] The center point parameter describes the display center location required for map display. This center point parameter can be determined based on the center location information of the target building complex, ensuring that the center point of the map displayed according to this parameter is located at the center of the target building complex. The center location information of the target building complex describes its center; however, this application does not limit the center location information, for example, it can use the center coordinates of the target building complex. It should be noted that this application does not limit the method of obtaining the center location information of the target building complex; for example, it can be implemented by manual annotation. Furthermore, this application does not limit the process of determining the center point parameter; for example, it can specifically involve directly using the center location information of the target building complex to automatically adjust the center point parameter of the map, so that the position described by the adjusted center point parameter is the position described by the center location information of the target building complex, thereby ensuring that the center point of the map displayed according to the adjusted center point parameter is located at the center of the target building complex.

[0078] The scaling parameter describes the scaling ratio required for map display. This scaling parameter can be determined based on the area occupied by the target building complex on the map, ensuring that the map displayed according to the scaling parameter accurately shows the target building complex in its entirety. It should be noted that this application does not limit the process of determining the scaling parameter. For example, when the map is displayed using a preset window, the specific process of determining the scaling parameter can be: calculating the scaling parameter based on the size of the preset window and the size of the area occupied by the target building complex on the map, so that the map displayed according to the scaling parameter accurately shows the target building complex in its entirety within the preset window. This achieves improved clarity of the target building complex while ensuring its completeness.

[0079] Based on the relevant content in step 11 above, for a map currently being displayed, if the goal is to determine the location of a target building complex, the map display configuration parameters corresponding to the target building complex can be determined first based on its relevant information (such as center point coordinates, size of the area it occupies, etc.). These parameters should represent the necessary configuration parameters for clearly and completely displaying the target building complex on the map. Then, the map display state can be adjusted according to these parameters to obtain an adjusted map. The center point of this adjusted map should be the center of the target building complex, ensuring a clear and complete display. This allows image data obtained through screenshots of the adjusted map to more clearly describe the target building complex while maintaining its integrity, thus improving the location determination effect.

[0080] Step 12: Take a screenshot of the adjusted map to obtain image data.

[0081] In this application, after adjusting the map display status according to the map display configuration parameters corresponding to the target building group, a screenshot can be taken of the adjusted map to obtain the image data corresponding to the target building group, so that the center pixel of the image data can be used to describe the center position of the target building group, and the image data can clearly and completely display the target building group.

[0082] Based on the relevant content of steps 11 to 12 above, in some application scenarios, when the map is in the display state, if it is necessary to determine the location of a target building group, the map display state can be adjusted first using the map display configuration parameters corresponding to the target building group to obtain an adjusted map. This adjusted map can more clearly describe the target building group while ensuring its integrity. Then, a screenshot is taken of the adjusted map to obtain the image data corresponding to the target building group. This image data can clearly and completely display the target building group, so that the location of each building in the target building group can be determined by the subsequent processing of the image data (for example, the processing process shown in S2-S3 below).

[0083] Based on the relevant content of S1 above, if it is desired to perform location determination processing on a target building group, image data corresponding to the target building group is obtained so that the image data carries the pixel points corresponding to each building in the target building group, thereby enabling the image data to represent the location of each building in the target building group.

[0084] S2: Determine the set of edge points corresponding to each building from the image data.

[0085] For any given building, the set of edge points corresponding to that building is used to record the pixels located at the edge positions among all the pixels occupied by that building in the image data above, so that the set of edge points corresponding to that building can describe the outline of that building in the image data.

[0086] Furthermore, this application does not limit the implementation of S2 above. For example, S2 can be implemented using any existing or future method that can extract edge information of some objects from image data (e.g., any edge extraction method).

[0087] In fact, in order to better improve the determination effect of the edge point set, this application also provides a possible implementation of S2 above. In this implementation, when the image data above includes at least one pixel corresponding to at least one building, S2 may specifically include steps 21-23 below.

[0088] Step 21: Hollow out all pixels in the image data above except for the pixel corresponding to at least one building to obtain a building description image, which includes the pixel corresponding to at least one building in the above image.

[0089] The building description image is used to record only the pixels occupied by each building described in the preceding image data, so that the building description image can represent all the buildings described in the preceding image data. Therefore, the building description image only includes the pixels corresponding to at least one building mentioned above.

[0090] Furthermore, this application does not limit the implementation method of the cutout process. For example, it can be that the pixel value of the pixel is simply deleted while the position of the pixel is retained. As an example, when the image data above is implemented using F1 as shown in formula (1) below, if it is necessary to perform cutout processing on the pixel in the second row and second column of F1, then the image data F2 shown in formula (2) below can be obtained.

[0091]

[0092]

[0093] Where F1 represents the image data before the hollowing-out process; F2 represents the image data after the hollowing-out process is applied to the second row and second column of the pixel in F1.

[0094] Based on the relevant content of step 21 above, after obtaining image data including the pixels corresponding to at least one building, a building description image can be constructed using all the pixels corresponding to buildings recorded in the image data. This ensures that the building description image only includes the pixels corresponding to these buildings, so that the building description image is only used to describe the state of these buildings in the image data. This effectively avoids interference from other pixels in the image data (such as pixels for the open space between two buildings) on the determination of building location, thereby improving the location determination effect.

[0095] Step 22: If there are sixth type points and adjacent points corresponding to the sixth type points in the above building description image, and the number of adjacent points corresponding to the sixth type points reaches the fourth threshold, then the sixth type points in the building description image are hollowed out to obtain an edge point description image, which includes the edge points corresponding to the at least one building.

[0096] The sixth type of point represents pixels in the building description image that are located within the area occupied by a particular building, so that this sixth type of point can represent interior points (i.e., non-edge points). An interior point refers to a pixel in the building description image that is located within the area occupied by a particular building. Therefore, in one possible implementation, the sixth type of point can include interior points corresponding to all buildings.

[0097] Furthermore, regarding the sixth type of point mentioned above, because this sixth type of point is an internal point, it has many adjacent points in the building description image (e.g., adjacent points appearing in the front, back, left, right, and four diagonal directions). This results in the following characteristics for the sixth type of point: the building description image contains the sixth type of point and its corresponding adjacent points, and the number of adjacent points reaches a fourth threshold. This fourth threshold can be preset, for example, it can be 8. The adjacent points corresponding to the sixth type of point refer to valid pixels in the building description image that are adjacent to the sixth type of point in position (e.g., adjacent in the front, back, left, right, and four diagonal directions). A valid pixel refers to a pixel in the building description image that has a pixel value (i.e., a pixel used to describe a building).

[0098] The edge point description image is used to describe the outlines of each building presented in the image data above; and the edge point description image includes only the edge points corresponding to at least one building above. Specifically, for any building, the edge points corresponding to that building are used to describe the outline of that building presented in the image data.

[0099] Based on the relevant content of step 22 above, after obtaining the building description image used to describe the state of at least one of the buildings mentioned above, the internal points corresponding to each building can be hollowed out from the building description image so that only the edge points corresponding to each building are retained in the hollowed-out image data, so that the hollowed-out building description image can represent the location of each building with fewer pixels.

[0100] Step 23: Group the pixels in the image described above to obtain the set of edge points corresponding to each building.

[0101] It should be noted that this application does not limit the implementation of step 23. For example, it can be implemented using any existing or future scheme that can group the pixels in the image described above as edge points.

[0102] For example, step 23 above can be implemented using the nearest neighbor propagation method, specifically as follows: First, randomly select a pixel from the edge point description image and initialize an edge point set corresponding to a building using this pixel, so that the edge point set includes this pixel; second, find the pixel closest to this pixel and add this "pixel closest to this pixel" to the edge point set; then, find the pixel closest to this "pixel closest to this pixel" and add this found pixel to the edge point set; this process is repeated multiple times to form the edge point set corresponding to the building; and the process of determining the edge point set corresponding to each building can be implemented using the aforementioned process. It should be noted that the stopping condition of the iteration process involved in this paragraph is: during the search process, the distance between the found pixel and the pixel used as the search keyword is greater than a preset distance threshold, which can be set according to the actual application scenario.

[0103] Based on the relevant content of steps 21 to 23 above, after obtaining the image data used to describe at least one building, we can first obtain the pixels corresponding to all buildings from the image data; then, we can hollow out the internal points corresponding to each building; finally, we can group the remaining points to obtain the set of edge points corresponding to each building, so that we can analyze the location of each building based on the set of edge points corresponding to each building.

[0104] Based on the relevant content of S2 above, after obtaining the image data used to describe at least one of the buildings mentioned above, the edge point set corresponding to each building can be extracted from the image data so that the edge point set corresponding to each building can describe the outline of each building in the image data, so that the location of each building can be analyzed based on the edge point set corresponding to each building.

[0105] S3: For any building, use the set of edge points corresponding to the building to analyze the location information of the building.

[0106] In this application, the location information of any building is used to describe the geographical location of the building; and the application does not limit the way the location information of the building is represented. For example, it can be implemented by any geographical location representation method (such as latitude and longitude).

[0107] Furthermore, this application does not limit the implementation of S3 above. For example, when "at least one building" includes N buildings, S3 can specifically be: first, convert the image coordinates of each pixel in the edge point set corresponding to the nth building into geographic coordinates to obtain the geographic coordinates corresponding to each pixel in the edge point set; then, collect the geographic coordinates corresponding to all pixels in the edge point set to obtain the location information of the nth building, so that the location information includes the geographic coordinates corresponding to all pixels in the edge point set. The image coordinates of the pixel are used to describe the coordinates of the pixel in the image coordinate system of the image data above; and this application does not limit the image coordinate system. For example, the image coordinate system refers to a coordinate system established with the upper left corner of the image data as the origin. It should be noted that this application does not limit the implementation of the coordinate transformation. For example, it can be implemented using any existing or future method that can convert image coordinates in a map screenshot into actual geographic coordinates (e.g., coordinate transformation according to a pre-set transformation rule). Where n is a positive integer, n≤N, and N is a positive integer, representing the number of buildings described in the image data above. It should also be noted that in some application scenarios, the coordinate transformation process involved in this application may need to use the geographic coordinate information inherent in the image data itself (e.g., the geographic coordinate range of the area where the building complex described by the image data is located).

[0108] In fact, for the edge point set mentioned above, there are still a relatively large number of pixels in the edge point set. Therefore, in order to better save resources (such as storage resources, computing resources, time resources, etc.), this application also provides a possible implementation of the above S3. In this implementation, when the above "at least one building" includes N buildings, the S3 may specifically include steps 31-32 below.

[0109] Step 31: Simplify the edge point set corresponding to the nth building to obtain a simplified edge point set corresponding to the nth building, such that the number of pixels in the simplified edge point set corresponding to the nth building is less than the number of pixels in the edge point set corresponding to the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0110] The simplification process is used to remove some pixels with low utility from the set of edge points corresponding to a building.

[0111] Furthermore, this application does not limit the implementation method of the simplification process. For example, it can be: simplifying the set of edge points corresponding to the nth building according to the simplification rules set in advance based on the application scenario, to obtain the simplified set of edge points corresponding to the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0112] In addition, to better understand the simplification process, some examples are provided below.

[0113] Scenario 1: In some application scenarios, for a diagonal line formed by multiple pixels from the aforementioned edge point set, the line may exhibit jagged edges due to vector pixelation (e.g., from...). Figure 3 or Figure 4 The jagged edges shown in the "slashes before processing" section are rendered so that the positional information provided by the pixels as sharp teeth in this jagged edge case is of little use.

[0114] Based on the preceding paragraph, to improve the simplification effect, jagged edges can be simplified on the set of edge points mentioned above. Therefore, in one possible implementation, the simplification process may include at least step 311 below.

[0115] Step 310: If a first-type point exists in the edge point set corresponding to the nth building, then delete the first-type point from the edge point set corresponding to the nth building; the edge point set corresponding to the nth building includes the first neighbor and the second neighbor of the first-type point; the first neighbor and the second neighbor are located on the same straight line, and the first-type point is not located on the same straight line. Where n is a positive integer, n≤N, and N is a positive integer.

[0116] Here, the first type of point refers to the pixel point that meets the preset aliasing simplification conditions, so that the first type of point can represent the pixel point that acts as a sharp tooth (e.g., Figure 4 P shown n This pixel).

[0117] The preset aliasing simplification condition is used to filter the pixels to be deleted in the case of aliasing (that is, the first type of points mentioned above); and this application does not limit the preset aliasing simplification condition. For example, when the set of edge points corresponding to the nth building mentioned above includes the first type of points (for example, Figure 4 P shown n This pixel), and the first neighboring point of the first type of point (for example, Figure 4 P shown n-1 This pixel), and the second neighboring point corresponding to the first type of point (e.g., Figure 4 P shown n+1 When considering this pixel, the preset aliasing simplification condition can be specifically defined as follows: the first neighboring point and the second neighboring point are located on the same straight line, and the first type of point is not located on this straight line. Here, the first neighboring point refers to a point in a certain direction (e.g., ...). Figure 4 The first neighbor (as shown by the diagonal line) refers to a pixel that is positioned before the first type of pixel and is adjacent to it. The second neighbor refers to a pixel that is positioned after the first type of pixel and is adjacent to it in the same direction. Therefore, the first neighbor, the first type of pixel, and the second neighbor form a group of pixels that are sequentially adjacent to each other.

[0118] For example, in some application scenarios, the pre-set aliasing simplification conditions mentioned above can be used to filter pixels that act as sharp teeth in aliasing cases (e.g., Figure 4 P shown n This pixel). Based on this, it can be understood that in one possible implementation, when the set of edge points corresponding to the nth building mentioned above includes this first type of point (for example, Figure 4 P shown n This pixel), and the first neighboring point of the first type of point (for example, Figure 4 P shown n-1 This pixel), and the second neighboring point corresponding to the first type of point (e.g., Figure 4 P shown n+1 When this pixel is located, the preset aliasing simplification condition can specifically be: the first type of point is located in the aliasing shape formed by the first type of point, the first neighboring point, and the second neighboring point (e.g., Figure 4 The position of the sharp teeth in the shape defined by the dashed box on the left (for example, Figure 4 China P n This pixel is Figure 4 (The position within the shape defined by the dashed box on the left). It can be seen that this first type of point can be located at an angle between its adjacent points, such as left-middle-top-right.

[0119] Based on the relevant content of step 310 above, in some application scenarios, for any building, after obtaining the set of edge points corresponding to the building, it is possible to detect whether there are first-type points in the set of edge points that meet the preset aliasing simplification conditions. If they do exist, in order to better improve the simplification effect, the first-type points can be directly deleted from the set of edge points, so that there are no first-type points that meet the preset aliasing simplification conditions in the set of edge points after deletion. This ensures that there are no pixels with low usability due to aliasing in the set of edge points after deletion, and thus the set of edge points after deletion can represent the location of the building with fewer pixels.

[0120] Scenario 2: In some application scenarios, for the set of edge points mentioned above, multiple adjacent pixels in the set can form a straight line, making the positional information provided by the pixel in the middle of the line less valuable.

[0121] Based on the preceding paragraph, to improve the simplification effect, center point simplification processing can be performed on the set of edge points mentioned above. Therefore, in one possible implementation, the simplification processing may include at least step 311 below.

[0122] Step 311: If a second type of point exists in the edge point set corresponding to the nth building, then delete the second type of point from the edge point set corresponding to the nth building. The edge point set corresponding to the nth building includes several candidate points that are sequentially adjacent in position, and these candidate points are located on the same straight line. The second type of point includes all points on the straight line except for the two endpoints. Where n is a positive integer, n≤N, and N is a positive integer.

[0123] The second type of point refers to pixels that meet the preset intermediate point simplification conditions (e.g., Figure 5 P shown n+1 This pixel, P n+2 This pixel, ..., P n+N-1 This pixel), so that the second type of point can represent a pixel in the middle position on a straight line composed of multiple pixels.

[0124] The preset intermediate point simplification condition is used to filter the pixels to be deleted under the intermediate point simplification condition (that is, the second type of points mentioned above); and this application does not limit the preset intermediate point simplification condition. For example, when the set of edge points corresponding to the nth building mentioned above includes several candidate points with sequentially adjacent positions (for example, these candidate points may include...) Figure 5 P shown n This pixel, P n+1 This pixel, ..., and P n+N When the candidate points are located on the same straight line, the simplified condition for the preset intermediate point can be: all points on the straight line except for the two endpoints. The candidate points represent multiple pixels that are sequentially adjacent and located on the same straight line. An endpoint refers to a pixel located at the edge of the straight line among the candidate points; moreover, this application does not limit the two endpoints. For example, the two endpoints may include a first endpoint and a last endpoint. The first endpoint refers to a pixel among the candidate points used as the starting endpoint of the straight line (e.g., ...). Figure 5 P shown n This pixel point). The tail point refers to the pixel point that exists among the candidate points and is used as the termination endpoint of the line (e.g., Figure 5 P shown n+N This pixel).

[0125] Based on the relevant content of step 311 above, in some application scenarios, for any building, after obtaining the set of edge points corresponding to the building (or the set of edge points processed by step 310 above), it is possible to detect whether there are second-type points in the set of edge points that meet the preset intermediate point simplification conditions. If there are, in order to better improve the simplification effect, the second-type points can be directly deleted from the set of edge points, so that there are no second-type points in the set of edge points after deletion that meet the preset intermediate point simplification conditions. Thus, there are no pixels with low usage value in the straight line formed by multiple sequentially adjacent pixels in the set of edge points after deletion, so that the set of edge points after deletion can represent the location of the building with fewer pixels.

[0126] Scenario 3: In some application scenarios, for the set of edge points mentioned above, there may be some internal points close to the edge, and these internal points have little practical value.

[0127] Based on the preceding paragraph, to improve the simplification effect, a three-point distance simplification process can be performed on the set of edge points mentioned above. Therefore, in one possible implementation, the simplification process may include at least step 312 below.

[0128] Step 312: If a third type of point exists in the edge point set corresponding to the nth building, then delete the third type of point from the edge point set corresponding to the nth building. The edge point set corresponding to the nth building includes the third neighbor and the fourth neighbor of the third type of point, and the distance from the third type of point to the straight line formed by the third neighbor and the fourth neighbor is less than a first threshold. Here, n is a positive integer, n≤N, and N is a positive integer.

[0129] The third type of point refers to a pixel that meets the preset three-point distance simplification condition, so that the third type of point can represent an interior point near the edge (e.g., Figure 6 P shown n This pixel).

[0130] The preset three-point distance simplification condition is used to filter the pixels to be deleted under the three-point distance simplification condition (that is, the third type of points mentioned above); and this application does not limit the preset three-point distance simplification condition, for example, when the set of edge points corresponding to the nth building mentioned above includes the third type of points (for example, Figure 6 P shown n This pixel), and the third neighboring point of the third type of point (for example, Figure 6 P shown n-1 This pixel), and the fourth neighboring pixel corresponding to the third type of point (e.g., Figure 6 P shown n+1 When considering a pixel, the specific simplified condition for the three-point distance can be: the distance from the third-type point to the straight line formed by the third neighbor and the fourth neighbor is less than a first threshold. Here, the third neighbor refers to a point in a certain direction (e.g., ...). Figure 6 The fourth neighbor refers to a pixel that is positioned in the horizontal direction (as shown) before the position of the third type of pixel and is adjacent to the position of the third type of pixel.

[0131] Based on the relevant content of step 312 above, in some application scenarios, for any building, after obtaining the set of edge points corresponding to the building (or, using the set of edge points processed by steps 310-311 above in sequence), it is possible to detect whether there are third-type points in the set of edge points that meet the preset three-point distance simplification conditions. If there are, in order to better improve the simplification effect, the third-type points can be directly deleted from the set of edge points, so that there are no third-type points in the set of edge points after deletion that meet the preset three-point distance simplification conditions. This results in no internal points close to the edge position of the area where the building is located in the set of edge points after deletion, and thus the set of edge points after deletion can represent the location of the building with fewer pixels.

[0132] Scenario 4: In some application scenarios, for the set of edge points mentioned above, there may be some points similar to... Figure 7 China P n+1 This pixel represents an internal point, and this internal point has little practical value.

[0133] Based on the preceding paragraph, to better improve the simplification effect, a four-point angle simplification process can be performed on the set of edge points mentioned above. Therefore, in one possible implementation, the simplification process may include at least step 313 below.

[0134] Step 313: If a fourth type of point exists in the edge point set corresponding to the nth building, then delete the fourth type of point from the edge point set corresponding to the nth building. The edge point set corresponding to the nth building includes the fifth neighbor, the sixth neighbor, and the seventh neighbor of the fourth type of point. The distance between the fifth neighbor and the fourth type of point is greater than a second threshold, the distance between the fourth type of point and the sixth neighbor is not greater than a second threshold, the distance between the sixth neighbor and the seventh neighbor is greater than a second threshold, the distance from the fourth type of point to the line formed by the fifth and seventh neighbors is not greater than a third threshold, and the distance from the sixth neighbor to the line formed by the fifth and seventh neighbors is not greater than a third threshold. Where n is a positive integer, n≤N, and N is a positive integer.

[0135] The fourth type of point refers to a pixel that meets the preset four-point angle simplification conditions, so that the fourth type of point can represent something similar to... Figure 7 China P n+1 This pixel represents an internal point.

[0136] The preset four-point angle simplification condition is used to filter the pixels to be deleted under the four-point angle simplification condition (that is, the fourth type of points mentioned above); and this application does not limit the preset four-point angle simplification condition, for example, when the set of edge points corresponding to the nth building mentioned above includes the fourth type of points (for example, Figure 7 China P n+1 This pixel), and the fifth neighboring pixel of the fourth type of point (for example, Figure 7 China P n This pixel), and the sixth neighbor of the fourth type of point (for example, Figure 7 China P n+2 This pixel), and the seventh neighbor corresponding to the sixth neighbor (for example, Figure 7 China P n+3 When considering this pixel, the specific simplified condition for the four-point angle can be: the distance between the fifth neighboring point and the fourth type of point (for example, by...). Figure 7 The distance described by line segment D1 shown is greater than the second threshold, and the distance between the fourth type point and the sixth neighbor point (e.g., by...) is greater than the second threshold. Figure 7 The distance described by line segment D2 shown is less than or equal to the second threshold, and the distance between the sixth neighbor and the seventh neighbor (e.g., by...) Figure 7 The distance described by line segment D3 shown is greater than the second threshold, and the distance from the fourth type point to the straight line formed by the fifth neighbor and the seventh neighbor (for example, by...) is greater than the second threshold. Figure 7 The distance described by line segment D4 shown is less than or equal to the third threshold, and the distance from the sixth neighbor point to the straight line formed by the fifth neighbor point and the seventh neighbor point (e.g., by...) Figure 7 The distance described by line segment D5 shown is less than or equal to the third threshold. The second threshold can be preset, for example, equal to the first threshold mentioned above; the third threshold can also be preset, for example, equal to twice the second threshold. The fifth neighbor refers to a point in a certain direction (e.g., ...). Figure 7 The sixth neighbor is a pixel that is positioned before the fourth neighbor in the horizontal direction (as shown). The seventh neighbor is a pixel that is positioned after the sixth neighbor in the same direction.

[0137] Based on the relevant content of step 313 above, in some application scenarios, for any building, after obtaining the set of edge points corresponding to the building (or, the set of edge points processed by steps 310-312 above), it is possible to detect whether there is a fourth type of point in the set of edge points that satisfies the preset four-point angle simplification condition. If so, in order to better improve the simplification effect, the fourth type of point can be directly deleted from the set of edge points, so that the set of edge points after deletion does not contain any fourth type of point that satisfies the preset four-point angle simplification condition, thereby ensuring that the set of edge points after deletion does not contain any points similar to... Figure 7 China P n+1 This pixel represents an interior point, which in turn allows the set of edge points after deletion to represent the location of the building with fewer pixels.

[0138] Scenario 5: In some application scenarios, for the set of edge points mentioned above, there may be some points similar to... Figure 7 China P n+2 This pixel indicates an internal point near the edge, and this internal point has little practical value.

[0139] Based on the preceding paragraph, to improve the simplification effect, a correction and simplification process can be performed on the set of edge points mentioned above. Therefore, in one possible implementation, the simplification process may include at least step 314 below.

[0140] Step 314: If a fifth-type point exists in the edge point set corresponding to the nth building, then delete the fifth-type point from the edge point set corresponding to the nth building. The edge point set corresponding to the nth building includes the eighth neighbor, the ninth neighbor, and the tenth neighbor of the fifth-type point. The distance between the tenth neighbor and the eighth neighbor is greater than a second threshold, the distance between the eighth neighbor and the fifth-type point is not greater than the second threshold, the distance between the fifth-type point and the ninth neighbor is greater than the second threshold, the distance from the eighth neighbor to the line formed by the tenth and ninth neighbors is not greater than a third threshold, the distance from the fifth-type point to the line formed by the tenth and ninth neighbors is not greater than the third threshold, and the distance from the fifth-type point to the line formed by the tenth and ninth neighbors is less than a first threshold. Where n is a positive integer, n≤N, and N is a positive integer.

[0141] The fifth type of point refers to pixels that meet preset correction conditions, so that the fifth type of point can represent similar to Figure 7 China P n+2 This pixel shows an internal point near the edge.

[0142] The preset correction conditions are used to filter the pixels to be deleted during the correction simplification process (i.e., the fifth type of points mentioned above); and this application does not limit the preset correction conditions, for example, when the set of edge points corresponding to the nth building mentioned above includes the fifth type of points (e.g., Figure 7 China P n+2 This pixel), the eighth neighbor of the fifth type of point (for example, Figure 7 China P n+1 This pixel), the ninth neighbor of the fifth type of point (for example, Figure 7 China P n+3 This pixel), and the tenth neighbor corresponding to the eighth neighbor (e.g., Figure 7 China P n When this pixel is mentioned, the preset correction condition can specifically be: the distance between the tenth neighbor and the eighth neighbor (for example, by...). Figure 7 The distance described by line segment D1 shown is greater than the second threshold, and the distance between the eighth neighbor and the fifth type of point (e.g., by...) is greater than the second threshold. Figure 7 The distance described by line segment D2 shown is less than or equal to the second threshold, and the distance between the fifth type point and the ninth neighbor point (e.g., by...) Figure 7 The distance described by line segment D3 shown is greater than the second threshold, and the distance from the eighth neighbor point to the straight line formed by the tenth neighbor point and the ninth neighbor point (for example, the distance from the eighth neighbor point to the straight line formed by the tenth neighbor point and the ninth neighbor point) is greater than the second threshold. Figure 7 The distance described by line segment D4 shown is less than or equal to the third threshold, and the distance from the fifth type point to the straight line formed by the tenth neighbor and the ninth neighbor (e.g., by...) Figure 7 The distance described by line segment D5 shown is less than or equal to the third threshold, and the distance from the fifth type point to the straight line formed by the tenth neighbor and the ninth neighbor is less than the first threshold. Here, the eighth neighbor refers to a point in a certain direction (e.g., ...). Figure 7 The third threshold (as shown in the horizontal direction, etc.) refers to pixels that are positioned before the fifth type of pixel and are adjacent to it. The ninth neighbor refers to pixels that are positioned after the fifth type of pixel and are adjacent to it in the same direction. The tenth neighbor refers to pixels that are positioned before the eighth neighbor and are adjacent to it in the same direction. It should be noted that this application does not limit the correlation between the third threshold and the first threshold; for example, the third threshold may be twice the first threshold.

[0143] Based on the relevant content of step 314 above, in some application scenarios, for any building, after obtaining the edge point set corresponding to the building (or, the edge point set processed by steps 310-312 above), it is possible to detect whether there are any fifth-type points in the edge point set that meet the preset correction conditions. If so, in order to better improve the simplification effect, the fifth-type points can be directly deleted from the edge point set, so that there are no fifth-type points that meet the preset correction conditions in the edge point set after deletion, thereby ensuring that there are no points similar to the fifth-type points in the edge point set after deletion. Figure 7 China P n+2 This pixel represents an interior point near the edge, thus enabling the deleted set of edge points to represent the location of the building with fewer pixels.

[0144] Case 6: In some application scenarios, to better improve the simplification effect and efficiency, the various simplification processes described above can be applied sequentially to the set of edge points corresponding to a certain building. Based on this, this application also provides a possible implementation of step 31 above, which may specifically include steps 315-319 below.

[0145] Step 315: If there exists a first-class point in the edge point set corresponding to the nth building that satisfies the preset zigzag simplification condition, then delete the first-class point from the edge point set corresponding to the nth building to obtain the zigzag-simplified set corresponding to the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0146] In this application, after extracting the set of edge points corresponding to the nth building from the image data above, it is possible to detect whether there are first-type points (e.g., pixels acting as sharp teeth or similar) in the set of edge points that satisfy the preset jagged edge simplification conditions. Figure 4 P shown n If this pixel exists, it can be directly deleted from the set of edge points to obtain the simplified zigzag set corresponding to the nth building. This ensures that the simplified zigzag set does not contain any first-class points that satisfy the preset zigzag simplification conditions, thus achieving zigzag simplification. Here, n is a positive integer, n≤N, and N is a positive integer.

[0147] Step 316: If there exists a second type of point in the simplified zigzag set corresponding to the nth building above that satisfies the preset intermediate point simplification condition, then delete the second type of point from the simplified zigzag set corresponding to the nth building to obtain the simplified intermediate point set corresponding to the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0148] In this application, after obtaining the simplified set of the nth building through sawtooth simplification, it is possible to detect whether there are any second-type points in the simplified set that satisfy the preset intermediate point simplification conditions (e.g., ...). Figure 5 P shown n+1 This pixel, P n+2 This pixel, ..., P n+N-1 If a pixel exists, it is directly deleted from the simplified jagged edge set, resulting in a simplified set of intermediate points corresponding to the nth building. This ensures that the simplified set of intermediate points does not contain any second-type points that satisfy the preset intermediate point simplification conditions, thus achieving intermediate point simplification. Here, n is a positive integer, n≤N, and N is a positive integer. It should be noted that this application does not limit the implementation method of the detection described in this paragraph; for example, it can be implemented recursively.

[0149] Step 317: If there is a third type of point in the simplified set of intermediate points corresponding to the nth building above that satisfies the preset three-point distance simplification condition, then delete the third type of point from the simplified set of intermediate points corresponding to the nth building to obtain the simplified set of three-point distances corresponding to the nth building; where n is a positive integer, n≤N, and N is a positive integer.

[0150] In this application, after obtaining the simplified set of intermediate points corresponding to the nth building through intermediate point simplification, it is possible to detect whether there are any third-type points in the simplified set of intermediate points that satisfy the preset three-point distance simplification conditions (e.g., ...). Figure 6 P shown n If a pixel exists, then the third type of point is deleted from the simplified set of intermediate points, resulting in a simplified set of three-point distances corresponding to the nth building. This ensures that the simplified set of three-point distances does not contain any third type of point that satisfies the preset three-point distance simplification conditions, thus achieving three-point distance simplification. Here, n is a positive integer, n≤N, and N is a positive integer. It should be noted that this application does not limit the implementation method of the detection described in this paragraph; for example, it can be implemented recursively.

[0151] Step 318: If there is a fourth type of point in the simplified set of three-point distances corresponding to the nth building above that satisfies the preset four-point angle simplification conditions, then delete the fourth type of point from the simplified set of three-point distances to obtain the simplified set of four-point angles corresponding to the nth building; where n is a positive integer, n≤N, and N is a positive integer.

[0152] In this application, after obtaining the simplified set of three-point distances corresponding to the nth building through three-point distance simplification, it is possible to detect whether there are fourth-type points in the simplified set of three-point distances that satisfy the preset four-point angle simplification conditions (e.g., Figure 7 China Pn+1 If a pixel exists, then the fourth type of point is deleted from the simplified set of three-point distances, resulting in the simplified set of four-point angles corresponding to the nth building. This ensures that the simplified set of four-point angles does not contain any fourth type of point satisfying the preset four-point angle simplification conditions, thus achieving four-point angle simplification. Here, n is a positive integer, n≤N, and N is a positive integer. It should be noted that this application does not limit the implementation method of the detection described in this paragraph; for example, it can be implemented recursively.

[0153] Step 319: If there exists a fifth type of point in the simplified set of four-point angles corresponding to the nth building above that satisfies the preset intermediate point simplification condition, then delete the fifth type of point from the simplified set of four-point angles corresponding to the nth building to obtain the simplified edge point set corresponding to the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0154] In this application, after obtaining the simplified set of four-point angles corresponding to the nth building through four-point angle simplification, it is possible to check again whether there are fifth-type points in the simplified set that satisfy the preset intermediate point simplification conditions. If so, the fifth-type points are deleted from the simplified set to obtain the simplified edge point set corresponding to the nth building, so that the simplified edge point set does not contain points similar to... Figure 7 China P n+2 This pixel represents an internal point near the edge, thus enabling simplified correction processing. Here, n is a positive integer, n ≤ N, and N is a positive integer. It should be noted that this application does not limit the implementation method of the detection described in this paragraph; for example, it can be implemented recursively.

[0155] Based on the relevant content of steps 315 to 319 above, after extracting the set of edge points corresponding to a building from the image data above, the edge point set can be processed sequentially with jagged edges, midpoints, first three-point distance, four-point angle, and second three-point distance to obtain the simplified edge point set corresponding to the building, so that the edge point set can represent the location of the building with fewer pixels.

[0156] Based on the relevant content of step 31 above, after extracting the edge point sets corresponding to each building from the image data above, the edge point sets corresponding to each building can be simplified to obtain the simplified edge point sets corresponding to each building (e.g., Figure 8 The simplified edge point set corresponding to each building is used to make the number of pixels in the simplified edge point set corresponding to each building less than the number of pixels in the original edge point set.

[0157] Step 32: Based on the simplified edge point set corresponding to the nth building mentioned above, determine the location information of the nth building. Here, n is a positive integer, n≤N, and N is a positive integer.

[0158] It should be noted that this application does not limit the implementation of step 32. For example, it can specifically be: first, converting the image coordinates of each pixel in the simplified edge point set corresponding to the nth building into geographic coordinates to obtain the geographic coordinates corresponding to each pixel in the simplified edge point set; then, aggregating the geographic coordinates corresponding to all pixels in the simplified edge point set to obtain the location information of the nth building, so that the location information includes the geographic coordinates corresponding to all pixels in the simplified edge point set. Here, n is a positive integer, n≤N, and N is a positive integer. It should be noted that the relevant content regarding coordinate transformation involved in this application can be found above.

[0159] Based on the relevant content of steps 31 and 32 above, after extracting the edge point sets corresponding to each building from the image data above, the edge point sets corresponding to each building are first simplified to obtain simplified edge point sets corresponding to each building; then, the location information of each building is analyzed using the simplified edge point sets corresponding to each building. Since the simplified edge point sets can describe the location of the corresponding building with the fewest pixels, the location analysis process based on these simplified edge point sets can effectively overcome the defects caused by location analysis based on a large number of pixel coordinates (such as excessive resource consumption, excessive data volume of the 3D model constructed based on a large number of pixel coordinates, and poor display performance of the 3D model constructed based on a large number of pixel coordinates), thus improving the location determination effect.

[0160] Based on the relevant content of S1 to S3 above, it can be seen that for the data processing method provided in this application embodiment, after obtaining image data (e.g., map screenshot) for describing at least one building, the set of edge points corresponding to each building is first determined from the image data, so that the set of edge points is used to record the pixels located at the edge position among all the pixels occupied by the corresponding building in the image data, so that the set of edge points can describe the position of the corresponding building in the image data with fewer pixel coordinates; then, for any building, the location information of the building is analyzed using the set of edge points corresponding to the building, so that the location information can represent the location of the building, thus achieving the purpose of automatically analyzing the location of the building from the image data.

[0161] In addition, since the edge point set corresponding to a building is used to record the pixels located at the edge position among all the pixels occupied by the building in the image data, the edge point set can describe the location of the building in the image data with as few pixels as possible. This makes the resources (such as time resources, computing resources, etc.) required to analyze the location information of the building based on the edge point set relatively small, which helps to reduce the resource consumption of the location analysis process.

[0162] Furthermore, this application does not limit the executing entity of the data processing method. For example, any implementation of the data processing method provided in the embodiments of this application can be applied to devices with data processing functions, such as terminal devices or servers. As another example, any implementation of the data processing method provided in the embodiments of this application can also be implemented through data communication between different devices (e.g., a terminal device and a server, two terminal devices, or two servers). The terminal device can be a smartphone, computer, personal digital assistant (PDA), or tablet computer, etc. The server can be a standalone server, a cluster server, or a cloud server.

[0163] Furthermore, in some application scenarios, after obtaining the location information of some buildings, three-dimensional models of these buildings can be constructed based on this location information. Based on this, this application also provides a possible implementation of the data processing method described above. In this implementation, the data processing method includes not only S1-S3 above, but may also include steps 41-42 below. The execution time of step 41 is later than the execution time of S3 above.

[0164] Step 41: For any building, construct a 3D model of the building using its location information.

[0165] For any given building, a three-dimensional model of the building is used to describe the building in three-dimensional space; and this application does not limit the three-dimensional model, for example, the three-dimensional model may refer to a three-dimensional model used to simulate lighting conditions.

[0166] Furthermore, this application does not limit the implementation of step 41 above. For example, it can be implemented using any existing or future method that can construct a three-dimensional model of a building based on its location information.

[0167] Step 42: Display a 3D model of at least one of the buildings mentioned above.

[0168] Based on steps 41 and 42 above, in some application scenarios, after acquiring image data (e.g., map screenshots) to describe at least one building, the edge point set corresponding to each building is first determined from the image data. This edge point set records the pixels located at the edge positions among all the pixels occupied by the corresponding building in the image data, allowing the edge point set to describe the location of the corresponding building in the image data with fewer pixel coordinates. Then, for any building, the location information of the building is analyzed using the corresponding edge point set, so that the location information can represent the location of the building. Finally, for any building, a 3D model of the building is constructed and displayed using the building's location information, so that the 3D model can be displayed to the user according to the building's location information. Since the building's location information is determined based on a small number of pixel coordinates, the amount of data for the building's location information is small, resulting in a smaller amount of data for the 3D model constructed based on the building's location information. This effectively reduces the amount of data in the 3D model, thereby improving the display performance of the 3D model.

[0169] Based on the data processing method provided in the embodiments of this application, the embodiments of this application also provide a data processing apparatus, which will be described below in conjunction with... Figure 9 Explanation and clarification will be provided. Among them, Figure 9 This is a schematic diagram of a data processing apparatus provided in an embodiment of this application. It should be noted that for technical details of the data processing apparatus provided in this application embodiment, please refer to the relevant content of the data processing method above.

[0170] like Figure 9 As shown, the data processing apparatus 900 provided in this application embodiment includes:

[0171] The acquisition unit 901 is used to acquire image data, wherein the image data carries at least one pixel corresponding to a building.

[0172] The determining unit 902 is used to determine the set of edge points corresponding to each of the buildings from the image data;

[0173] The analysis unit 903 is used to analyze the location information of any of the buildings by using the set of edge points corresponding to the building.

[0174] In one possible implementation, the analysis unit 903 includes:

[0175] The simplified processing subunit is used to simplify the set of edge points corresponding to the building;

[0176] The location determination sub-unit is used to determine the location information of the building based on the simplified set of edge points.

[0177] In one possible implementation, the simplified processing subunit includes:

[0178] The first deletion subunit is used to delete the first type of point from the edge point set corresponding to the building if the first type of point exists in the edge point set corresponding to the building; the edge point set corresponding to the building includes the first neighbor point corresponding to the first type of point and the second neighbor point corresponding to the first type of point; the first neighbor point and the second neighbor point are located on the same straight line, and the first type of point is not located on the straight line.

[0179] In one possible implementation, the first type of point is located at the tip of a serrated shape formed by the first type of point, the first neighboring point, and the second neighboring point.

[0180] In one possible implementation, the simplified processing subunit includes:

[0181] The second deletion subunit is used to delete the second type of point from the edge point set corresponding to the building if there is a second type of point in the edge point set corresponding to the building; the edge point set corresponding to the building includes a number of candidate points that are sequentially adjacent in position, the number of candidate points are located on the same straight line, and the second type of point includes other points on the straight line except for the two endpoints.

[0182] In one possible implementation, the simplified processing subunit includes:

[0183] The third deletion subunit is used to delete the third type of point from the edge point set corresponding to the building if there is a third type of point in the edge point set corresponding to the building; the edge point set corresponding to the building includes the third neighbor point corresponding to the third type of point and the fourth neighbor point corresponding to the third type of point, and the distance from the third type of point to the straight line formed by the third neighbor point and the fourth neighbor point is less than a first threshold.

[0184] In one possible implementation, the simplified processing subunit includes:

[0185] The fourth deletion subunit is used to delete the fourth type of point from the edge point set corresponding to the building if a fourth type of point exists in the edge point set corresponding to the building. The edge point set corresponding to the building includes the fifth neighbor point corresponding to the fourth type of point, the sixth neighbor point corresponding to the fourth type of point, and the seventh neighbor point corresponding to the sixth neighbor point. The distance between the fifth neighbor point and the fourth type of point is greater than a second threshold, the distance between the fourth type of point and the sixth neighbor point is not greater than the second threshold, the distance between the sixth neighbor point and the seventh neighbor point is greater than the second threshold, the distance from the fourth type of point to the straight line formed by the fifth neighbor point and the seventh neighbor point is not greater than a third threshold, and the distance from the sixth neighbor point to the straight line formed by the fifth neighbor point and the seventh neighbor point is not greater than the third threshold.

[0186] In one possible implementation, the simplified processing subunit includes:

[0187] The fifth deletion subunit is used to delete the fifth type of point from the edge point set corresponding to the building if the fifth type of point exists in the edge point set corresponding to the building. The edge point set corresponding to the building includes the eighth neighbor point corresponding to the fifth type of point, the ninth neighbor point corresponding to the fifth type of point, and the tenth neighbor point corresponding to the eighth neighbor point. The distance between the tenth neighbor point and the eighth neighbor point is greater than a second threshold, the distance between the eighth neighbor point and the fifth type of point is not greater than the second threshold, the distance between the fifth type of point and the ninth neighbor point is greater than the second threshold, the distance from the eighth neighbor point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than a third threshold, the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than the third threshold, and the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is less than a first threshold.

[0188] In one possible implementation, the simplification processing subunit is specifically used for: if there are first-type points in the set of edge points corresponding to the building that satisfy a preset zigzag simplification condition, then delete the first-type points from the set of edge points corresponding to the building to obtain a zigzag-simplified set; if there are second-type points in the zigzag-simplified set that satisfy a preset intermediate point simplification condition, then delete the second-type points from the zigzag-simplified set to obtain an intermediate point simplified set; if there are third-type points in the intermediate point simplified set that satisfy a preset three-point distance simplification condition, then delete the third-type points from the intermediate point simplified set to obtain a three-point distance simplified set; if there are fourth-type points in the three-point distance simplified set that satisfy a preset four-point angle simplification condition, then delete the fourth-type points from the three-point distance simplified set to obtain a four-point angle simplified set; if there are fifth-type points in the four-point angle simplified set that satisfy a preset intermediate point simplification condition, then delete the fifth-type points from the four-point angle simplified set to obtain a simplified edge point set.

[0189] In one possible implementation, the data processing apparatus 900 further includes:

[0190] A construction unit is used to construct a three-dimensional model of any of the aforementioned buildings using the building's location information.

[0191] The display unit is used to display a three-dimensional model of the at least one building.

[0192] In one possible implementation, the acquisition unit 901 is specifically used to: when the map is in display mode, adjust the display mode of the map according to the map display configuration parameters corresponding to the target building group, so that the adjusted map is used to display the target building group, wherein the map display configuration parameters include center point parameters and scaling parameters, wherein the center point parameters are determined based on the center position information of the target building group, and the scaling parameters are determined based on the area occupied by the target building group on the map; and perform screenshot processing on the adjusted map to obtain the image data.

[0193] In one possible implementation, the determining unit 902 is specifically configured to: hollow out all pixels in the image data except for those corresponding to at least one building to obtain a building description image, the building description image including the pixels corresponding to the at least one building; if there are sixth-type points and adjacent points corresponding to the sixth-type points in the building description image, and the number of adjacent points corresponding to the sixth-type points reaches a fourth threshold, then hollow out the sixth-type points in the building description image to obtain an edge point description image, the edge point description image including the edge points corresponding to the at least one building; and group the pixels in the edge point description image to obtain a set of edge points corresponding to each building.

[0194] Based on the aforementioned content of the data processing device 900, it is understood that, for the data processing device 900 provided in this application embodiment, after acquiring image data (e.g., map screenshots) used to describe at least one building, it first determines the set of edge points corresponding to each building from the image data, so that the set of edge points is used to record the pixels located at the edge position among all the pixels occupied by the corresponding building in the image data, thereby enabling the set of edge points to describe the position of the corresponding building in the image data with fewer pixel coordinates; then, for any building, the location information of the building is analyzed using the set of edge points corresponding to the building, so that the location information can represent the location of the building, thus achieving the purpose of automatically analyzing the location of the building from the image data.

[0195] In addition, since the edge point set corresponding to a building is used to record the pixels located at the edge position among all the pixels occupied by the building in the image data, the edge point set can describe the location of the building in the image data with as few pixels as possible. This makes the resources (such as time resources, computing resources, etc.) required to analyze the location information of the building based on the edge point set relatively small, which helps to reduce the resource consumption of the location analysis process.

[0196] Furthermore, this application also provides an electronic device, which includes a processor and a memory: the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory so that the electronic device performs any implementation of the data processing method provided in this application.

[0197] See Figure 10The diagram illustrates a structural schematic of an electronic device 1000 suitable for implementing embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0198] like Figure 10 As shown, the electronic device 1000 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the operation of the electronic device 1000. The processing unit 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0199] Typically, the following devices can be connected to the I / O interface 1005: input devices 1006 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1008 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic device 1000 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 10 An electronic device 1000 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0200] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1009, or installed from storage device 1008, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of embodiments of this disclosure.

[0201] The electronic device provided in this embodiment belongs to the same inventive concept as the method provided in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0202] This application also provides a computer-readable medium storing instructions or a computer program that, when executed on a device, causes the device to perform any implementation of the data processing method provided in this application.

[0203] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0204] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0205] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0206] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the aforementioned methods.

[0207] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0209] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units / modules do not necessarily limit the specific unit itself.

[0210] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0211] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0212] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0213] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0214] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0215] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0216] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that, The method includes: Acquire image data, wherein the image data carries at least one pixel corresponding to a building; Determine the set of edge points corresponding to each building from the image data; For any of the aforementioned buildings, in response to the existence of a fourth type of point in the edge point set corresponding to the building that satisfies the preset four-point angle simplification condition, the fourth type of point is deleted from the edge point set corresponding to the building, and the location information of the building is analyzed using the edge point set corresponding to the building. The preset four-point angle simplification condition indicates that the edge point set corresponding to the building includes the fifth neighbor point corresponding to the fourth type of point, the sixth neighbor point corresponding to the fourth type of point, and the seventh neighbor point corresponding to the sixth neighbor point. The distance between the fifth neighbor point and the fourth type of point is greater than a second threshold, the distance between the fourth type of point and the sixth neighbor point is not greater than the second threshold, the distance between the sixth neighbor point and the seventh neighbor point is greater than the second threshold, the distance from the fourth type of point to the line formed by the fifth neighbor point and the seventh neighbor point is not greater than a third threshold, the distance from the sixth neighbor point to the line formed by the fifth neighbor point and the seventh neighbor point is not greater than the third threshold, and the third threshold is equal to twice the second threshold.

2. The method according to claim 1, characterized in that, The step of analyzing the location information of a building using the set of edge points corresponding to the building includes: The set of edge points corresponding to the building is simplified. The location information of the building is determined based on the simplified set of edge points.

3. The method according to claim 2, characterized in that, The simplification process includes: If a first type of point exists in the set of edge points corresponding to the building, then the first type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes a first neighbor point corresponding to the first type of point and a second neighbor point corresponding to the first type of point. The first neighbor point and the second neighbor point are located on the same straight line, and the first type of point is not located on the straight line.

4. The method according to claim 3, characterized in that, The first type of point is located at the tip of the sawtooth shape formed by the first type of point, the first neighboring point, and the second neighboring point.

5. The method according to claim 2, characterized in that, The simplification process includes: If a second type of point exists in the set of edge points corresponding to the building, then the second type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes several candidate points that are sequentially adjacent in position. The candidate points are located on the same straight line. The second type of point includes all points on the straight line except for the two endpoints.

6. The method according to claim 2, characterized in that, The simplification process includes: If a third type of point exists in the set of edge points corresponding to the building, then the third type of point is deleted from the set of edge points corresponding to the building. The set of edge points corresponding to the building includes the third neighbor point and the fourth neighbor point corresponding to the third type of point. The distance from the third type of point to the straight line formed by the third neighbor point and the fourth neighbor point is less than a first threshold.

7. The method according to claim 2, characterized in that, The simplification process includes: If a fifth type of point exists in the edge point set corresponding to the building, then the fifth type of point is deleted from the edge point set corresponding to the building. The edge point set corresponding to the building includes the eighth neighbor point corresponding to the fifth type of point, the ninth neighbor point corresponding to the fifth type of point, and the tenth neighbor point corresponding to the eighth neighbor point. The distance between the tenth neighbor point and the eighth neighbor point is greater than a second threshold, the distance between the eighth neighbor point and the fifth type of point is not greater than the second threshold, the distance between the fifth type of point and the ninth neighbor point is greater than the second threshold, the distance from the eighth neighbor point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than a third threshold, the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is not greater than the third threshold, and the distance from the fifth type of point to the line formed by the tenth neighbor point and the ninth neighbor point is less than a first threshold.

8. The method according to claim 2, characterized in that, The process of determining the simplified edge point set includes: If there are first-type points in the set of edge points corresponding to the building that satisfy the preset sawtooth simplification conditions, then delete the first-type points from the set of edge points corresponding to the building to obtain the sawtooth simplified set. If there is a second type of point in the simplified zigzag set that satisfies the preset intermediate point simplification condition, then the second type of point is deleted from the simplified zigzag set to obtain the simplified intermediate point set. If there is a third type of point in the simplified set of intermediate points that satisfies the preset three-point distance simplification condition, then the third type of point is deleted from the simplified set of intermediate points to obtain the simplified set of three-point distance. If there is a fourth type of point in the simplified three-point distance set that satisfies the preset four-point angle simplification condition, then delete the fourth type of point from the simplified three-point distance set to obtain the simplified four-point angle set. If there is a fifth type of point in the simplified set of four-point angles that satisfies the preset intermediate point simplification conditions, then the fifth type of point is deleted from the simplified set of four-point angles to obtain the simplified edge point set.

9. The method according to claim 1, characterized in that, The method further includes: For any of the buildings, a three-dimensional model of the building is constructed using the building's location information; Display a three-dimensional model of at least one of the buildings.

10. The method according to claim 1, characterized in that, The process of acquiring the image data includes: When the map is in display mode, the map display mode is adjusted according to the map display configuration parameters corresponding to the target building group, so that the adjusted map is used to display the target building group. The map display configuration parameters include center point parameters and zoom parameters. The center point parameters are determined based on the center position information of the target building group, and the zoom parameters are determined based on the area occupied by the target building group on the map. The adjusted map is screenshotted to obtain the image data.

11. The method according to claim 1, characterized in that, Determining the set of edge points corresponding to each building from the image data includes: The pixels in the image data other than those corresponding to at least one building are hollowed out to obtain a building description image, which includes the pixels corresponding to the at least one building. If there are sixth type points and adjacent points corresponding to the sixth type points in the building description image, and the number of adjacent points corresponding to the sixth type points reaches the fourth threshold, then the sixth type points in the building description image are hollowed out to obtain an edge point description image, which includes the edge points corresponding to the at least one building. The pixels in the edge point description image are grouped to obtain the edge point set corresponding to each building.

12. A data processing apparatus, characterized in that, include: An acquisition unit is used to acquire image data, wherein the image data carries at least one pixel corresponding to a building; A determining unit is used to determine the set of edge points corresponding to each of the buildings from the image data; The analysis unit is configured to, for any of the aforementioned buildings, in response to the existence of a fourth type of point in the edge point set corresponding to the building that satisfies a preset four-point angle simplification condition, delete the fourth type of point from the edge point set corresponding to the building, and analyze the location information of the building using the edge point set corresponding to the building. The preset four-point angle simplification condition indicates that the edge point set corresponding to the building includes a fifth neighbor point corresponding to the fourth type of point, a sixth neighbor point corresponding to the fourth type of point, and a seventh neighbor point corresponding to the sixth neighbor point; the distance between the fifth neighbor point and the fourth type of point is greater than a second threshold, the distance between the fourth type of point and the sixth neighbor point is not greater than the second threshold, the distance between the sixth neighbor point and the seventh neighbor point is greater than the second threshold, the distance from the fourth type of point to the line formed by the fifth neighbor point and the seventh neighbor point is not greater than a third threshold, the distance from the sixth neighbor point to the line formed by the fifth neighbor point and the seventh neighbor point is not greater than the third threshold, and the third threshold is equal to twice the second threshold.

13. An electronic device, characterized in that, The device includes: a processor and a memory; The memory is used to store instructions or computer programs; The processor is configured to execute the instructions or computer program in the memory to cause the electronic device to perform the method according to any one of claims 1-11.

14. A computer-readable medium, characterized in that, The computer-readable medium stores instructions or computer programs that, when executed on the device, cause the device to perform the method according to any one of claims 1-11.

Images