Building density extraction method, model and readable storage medium

Abstract

The invention discloses a building density extraction method and model and a readable storage medium, and the method comprises the following steps: obtaining the road vector data of a target region from the field survey data in the first national natural disaster comprehensive risk census data, and carrying out the merging processing of the road vector data and the boundary data of the target region, converting the data into grating data; carrying out reclassification processing by utilizing a grid binarization method to obtain binarization data; carrying out aggregation processing on the binarized data to form a complete road architecture, smoothing the boundary of the data of the complete road architecture by adopting an expansion and contraction mode, then carrying out skeleton extraction on the obtained data of the road with the smooth boundary, and obtaining community sub-region data formed by surrounding of the center line of the road; and finally, processing the community sub-region data to obtain high-precision data, calculating each community sub-region, and taking the ratio of the building floor area of the community sub-region to the land area of the community sub-region as the building density.

Description

Technical Field

[0001] The invention belongs to the technical field of geographic information data processing, and in particular relates to a building density extraction method, a model and a readable storage medium. Background Art

[0002] Building density refers to the ratio (%) of the total base area of ​​buildings to the occupied land area within a certain range. It refers to the coverage rate of buildings, specifically the ratio (%) of the total base area of ​​all buildings within the project land area to the planned construction land area. It can reflect the vacant land rate and building density within a certain land area. The building density is different from the common building volume ratio. Relative to the same building plot, the building density considers the building area occupancy rate, while the building volume ratio considers the building's usable space.

[0003] The building density index is used in many fields. In the planning of urban roads and emergency response plans related to urban activities and disasters, it is also necessary to understand the building density index of existing urban areas. In this index, according to the principle of adjacent similarity of buildings, the building group is divided into community sub-areas by roads. The building density is the ratio of the total floor area of ​​buildings in a community sub-area to the land area of ​​the community sub-area. The building density can be used as a reference for the assessment of the risk index of the area.

[0004] In the prior art, there is a method of using GIS software to obtain the corresponding building density. The vector data obtained in the urban area can intuitively show the specific block density, but the existing building density calculation method is to directly grid the block where the building is located. This method can easily lead to a small amount of actual building density calculation, resulting in errors in area calculation. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a building density extraction method, model and readable storage medium, which aims to obtain the vector data of the target area from the risk survey data for calculation, so as to obtain the building density index of the plane, thereby providing guidance for the corresponding emergency plans for accidents or disasters and urban road planning.

[0006] The technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides a building density extraction method, which calculates and extracts the building density of a target area, and the specific steps are as follows:

[0008] Step 100. First, determine the boundary of the target area, then obtain the road vector data of the target area from the field survey data in the first national comprehensive natural disaster risk census data, merge the road vector data and the boundary data of the target area, and then convert them into grid data;

[0009] Step 200: Reclassify the processed grid data using a grid binarization method to obtain binary data in a way that highlights the urban road network;

[0010] Step 300. Then, the binary data is aggregated to form a complete road structure, and then the data of the complete road structure is smoothed by expanding and contracting, and then the skeleton is extracted from the obtained road data with smooth boundaries to obtain the community sub-area data formed by the center line of the road;

[0011] Step 400. After the community sub-area data is finally processed to obtain high-precision data, calculation is performed on each community sub-area, and the building density is taken as the ratio of the building area of ​​the community sub-area to the land area of ​​the community sub-area.

[0012] In combination with the first aspect, the present invention provides a first implementation of the first aspect, in the step 100, the road vector data includes boundary vector data of the target area and urban road vector data, and the two data are merged for processing before processing.

[0013] In combination with the first aspect, the present invention provides a second implementation of the first aspect. In the step 100, the road vector data is preprocessed, and through the topological relationship calculation method, the road section with only a single node degree is used as the determination condition of the dead end road, and all the dead ends in the road vector data are obtained and removed.

[0014] In combination with the first aspect, the present invention provides a third implementation of the first aspect. In step 200, when the grid data is processed by grid binarization method, the range of the administrative area is set to 0 value and the road is set to 1 value for processing.

[0015] In combination with the first aspect, the present invention provides a fourth implementation of the first aspect, in the step 300, a width threshold of the gaps between roads is set, and all roads with gaps not greater than the width threshold are pixel-aggregated to form a single road.

[0016] In combination with the first aspect, the present invention provides a fifth implementation of the first aspect. In step 300, when performing skeleton extraction on data of a road with smooth boundaries, a corrosion method is used to shrink the boundary of the image to eliminate small areas smaller than a set area threshold, and a dilation method is used to merge background points contacting the target area into the corresponding target object, so that the target boundary expands outward to eliminate holes and small particle noise smaller than the set area threshold.

[0017] In combination with the first aspect, the present invention provides a sixth implementation of the first aspect. When processing the community sub-area data in step 400, the minimum area threshold is set using GIS software, and objects smaller than the area threshold are eliminated according to their boundaries, and then the fragmented surfaces are merged into nearby surface elements.

[0018] In a second aspect, the present invention provides a model, which is an extraction model established by the building density extraction method described above.

[0019] In combination with the second aspect, the present invention provides a first implementation of the second aspect, in which the model is programmed using the script programming function of the GIS software to form an automated script which is applied in the GIS software in a secondary menu window to calculate the building density of the target area.

[0020] In a third aspect, the present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods described above.

[0021] The beneficial effects of the present invention are:

[0022] The data of the present invention is the building data of the corresponding target area extracted from the field survey data of the risk census data. The data source is more accurate and different from the data obtained by the GIS system or satellite remote sensing images, which can more realistically reflect the building density; by extracting the road centerline through the road network, urban planning, management and sustainable development can be comprehensively considered; through morphological expansion and corrosion operations, the road boundary can be smoothed and the road skeleton can be extracted, which helps to accurately extract the centerline of the road, and then determine the boundary of the community sub-area, thereby improving the accuracy and precision of the building density. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a method in an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of image changes in which vector data is converted into grid data in an embodiment of the present invention;

[0025] Figure 3 is a grid data image before grid reclassification in an embodiment of the present invention;

[0026] Figure 4 is a grid data image after the grid is binarized and reclassified in an embodiment of the present invention;

[0027] Figure 5 is a partial schematic diagram of a road before grid aggregation of the road in an embodiment of the present invention;

[0028] Figure 6 is a partial schematic diagram of a road after grid aggregation of the road in an embodiment of the present invention;

[0029] Figure 7 It is a schematic diagram of a local image interface after the center line of a road is extracted using a plug-in in a GIS software in an embodiment of the present invention;

[0030] Figure 8 In the embodiment of the present invention, the extracted road centerline schematic diagram is processed for a certain area. DETAILED DESCRIPTION

[0031] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0033] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0034] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0035] In the description of this application, it should be noted that if the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the product of the application is usually placed when in use. It is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on this application. In addition, if the terms "first", "second", etc. appear in the description of this application, they are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0036] Embodiment 1:

[0037] This embodiment provides a method for extracting building density, which aims to accurately obtain and calculate the building density in a corresponding area by setting corresponding parameter indicators through actual surveying and mapping plane vector diagrams.

[0038] It should be noted that the vector map obtained in this embodiment refers to the road vector data based on field surveys obtained from the national risk census data, but is not limited to this, and road vector data obtained from other software and systems may also be used.

[0039] Specifically, in this embodiment, for the determined target area, the coordinate range is used as the boundary of the target area, so that all image points included in the coordinate range are processed as road vector data of the target area. The processing method is to first convert it into grid data, and then binarize the grid data to obtain clearer road data, and then clean the road data to obtain a smoother and uniformly wide road. The final road data is obtained by extracting the center line of the road, and several areas delineated by the road data are used as community sub-areas. The building area and land area of ​​each community sub-area are compared to obtain a ratio representing the building density.

[0040] In this processing method, the road is used as the dividing line to determine the land area of ​​a community sub-area, and then the building density in each community sub-area is obtained by taking the ratio of the determined building area. This calculation method can clearly obtain the boundary of the area, and at the same time, it can also divide the area occupied by the road into each community sub-area, so as to obtain a more accurate building density as much as possible.

[0041] At the same time, different from the existing technology, using roads as a division method can obtain the building density of several plots with the same functional attributes or the same geographical relationship in the target area. Because roads are the separation boundaries of urban buildings, they are better than using the center line of buildings as the boundary demarcation range or the customized area range, which is more accurate. The so-called same geographical relationship refers to areas that may not belong to the same building or building complex, but are not divided by streets, and have a closer relationship of personnel mobility, that is, door-to-door communities, courtyards, or wall-to-wall communities, although they belong to different communities or courtyards, they are close to each other and the population distribution is relatively even, so they can be regarded as the same building complex.

[0042] And because such buildings with the same geographical relationship have the same regional attributes, they have similar characteristic points for judging population density or judging the disaster situation of people after an accident in emergency plans, and are more relevant for planning escape routes or temporary shelters.

[0043] Furthermore, in this embodiment, the road vector data of the target area obtained through the national risk census data is processed by external software.

[0044] First, determine the boundary of the target area, and then obtain the road vector data of the target area from the basic data of the national risk census data obtained from the administrative agency corresponding to the target area. The road vector data includes the boundary vector data of the target area and the urban road vector data. The two data are merged before processing.

[0045] Then, the road vector data is preprocessed. By calculating the topological relationship, the road sections with only a single node degree are used as the conditions for determining the dead-end roads. All the dead-end roads in the road vector data are obtained and removed.

[0046] The preprocessed road vector data is converted into grid data, and the processed grid data is reclassified using the grid binarization method. When the grid data is processed by the grid binarization method, the range of the administrative area is set to 0 and the road is set to 1 for processing, so as to obtain binary data in a way that highlights the urban road network.

[0047] The binary data is then aggregated to form a complete road structure. The data of the complete road structure is then expanded and contracted to smooth the boundaries. The skeleton of the road with smooth boundaries is then extracted to obtain the community sub-area data surrounded by the center line of the road.

[0048] Finally, after processing the community sub-area data to obtain high-precision data, calculations are performed on each community sub-area, and the building density is taken as the ratio of the building area of ​​the community sub-area to the land area of ​​the community sub-area.

[0049] As another implementation method, in this embodiment, GIS software is directly used to process the road vector data of the target area to calculate the building density. Figure 1 , Figure 1 This is a step diagram of this implementation method.

[0050] Step 1: Since the road network presents a tree structure, there are many dead-end roads at the end of the road. The topological relationship between the graphics is used to eliminate the hanging points at the end of the road. The administrative district boundary vector data and the urban road network vector data are merged and converted into raster data. Figure 2 , recorded as "Step 1".

[0051] Step 2: Use the raster binarization method to reclassify the data in "Step 1", refer to Figure 3 and Figure 4 , set the administrative area to 0 value and the urban road network to 1 value for storage, generating "step 2";

[0052] Step 3: Aggregate the selected hole pixels in "Step 2", refer to Figure 5 and Figure 6 , thus forming a complete road structure "Step 3";

[0053] Step 4: Expand and shrink the 1-value pixels to smooth the boundaries. The skeleton of the binary image is extracted by morphological expansion and erosion. Figure 7 , we can see in the figure that we get the center line of the 1-value pixel "Step 4", and the area surrounded by the center line is the community sub-area;

[0054] Step 5, by eliminating broken surfaces, surface features with an area smaller than a certain value are eliminated according to their attributes, and areas with too small areas are extracted and merged into the surrounding community sub-areas to obtain the final community sub-area formed by the processed "Road E".

[0055] Step 6, calculate the building density by taking the ratio of the total floor area of ​​buildings in a community sub-area to the land area of ​​the community sub-area.

[0056] Wherein, step 4 comprises the following steps:

[0057] The purpose of erosion and dilation operations is to remove unnecessary image elements while keeping the original shape of the region of interest.

[0058] Erosion essentially shrinks the border of an image and can be used to eliminate small and meaningless parts:

[0059]

[0060] Among them, x represents the displacement of the set, and Θ is the operator of the corrosion operation.

[0061] Dilation is to merge the background points that touch the target area into the target object, so that the target boundary expands outward. It can be used to fill some holes in the target area and eliminate small particle noise contained in the target area:

[0062]

[0063] Among them, x represents the displacement of the set, The operator for the dilation operation.

[0064] Furthermore, in this embodiment, the urban administrative division of a certain region is provided as an actual example for explanation, as follows:

[0065] Furthermore, each community sub-region obtained in this embodiment is used as an object to guide the emergency response plan for determining the temporary disaster refuge area.

[0066] After obtaining the building density, it has a reference significance for emergency management. This data can be used to more accurately assess the potential risks of communities or regions in disasters, and can also guide the formulation of more scientific and reasonable emergency plans. High-density buildings mean higher disaster risks, such as faster spread of fires and more serious damage caused by earthquakes. In response to this, evacuation routes can be planned in advance to ensure that people can evacuate quickly in an emergency; at the same time, rescue forces can be deployed more effectively to improve response speed and rescue efficiency. In addition, building density also directly affects the demand and configuration of emergency supplies, ensuring that there are sufficient supplies in high-density areas to deal with possible disaster events.

[0067] In addition, this embodiment also establishes an extraction model established by a building density extraction method through the above-mentioned extraction method, and the model is programmed into an automated script using the script programming function of the GIS software and applied in the GIS software to calculate the building density of the target area in a secondary menu window.

[0068] Additionally, this embodiment also provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method described in any one of the above is implemented.

[0069] In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0070] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0071] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0072] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0073] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0074] The present invention is not limited to the above optional implementations, and anyone can derive other various forms of products under the enlightenment of the present invention. The above specific implementations should not be understood as limiting the scope of protection of the present invention. The scope of protection of the present invention should be based on the definition in the claims, and the description can be used to interpret the claims.

Claims

1. A building density extraction method, which calculates and extracts the building density of a target area, characterized by: The specific steps are as follows: Step 100. First, determine the boundary of the target area, then obtain the road vector data of the target area from the field survey data in the first national comprehensive natural disaster risk census data, merge the road vector data and the boundary data of the target area, and then convert them into grid data; Step 200: Reclassify the processed grid data using a grid binarization method to obtain binary data in a way that highlights the urban road network; Step 300. Then, the binary data is aggregated to form a complete road structure, and then the data of the complete road structure is smoothed by expanding and contracting, and then the skeleton is extracted from the obtained road data with smooth boundaries to obtain the community sub-area data formed by the center line of the road; Step 400. After the community sub-area data is finally processed to obtain high-precision data, calculation is performed on each community sub-area, and the building density is taken as the ratio of the building area of ​​the community sub-area to the land area of ​​the community sub-area.

2. A building density extraction method according to claim 1, characterized in that: In step 100, the road vector data includes boundary vector data of the target area and urban road vector data, and the two data are combined before processing.

3. A building density extraction method according to claim 1, characterized in that: In step 100, the road vector data is preprocessed, and by calculating the topological relationship, a road section with only a single node degree is used as a condition for determining a dead end road, and all the dead ends in the road vector data are obtained and removed.

4. A building density extraction method according to claim 1, characterized in that: In step 200, when the grid data is processed by the grid binarization method, the range of the administrative area is set to 0 and the road is set to 1 for processing.

5. A building density extraction method according to claim 1, characterized in that: In step 300, a width threshold of the gaps between roads is set, and all roads with gaps not greater than the width threshold are pixel-aggregated to form a single road.

6. A building density extraction method according to claim 1, characterized in that: In step 300, when skeleton extraction is performed on the data of a road with smooth boundaries, the boundary of the image is shrunk by using the corrosion method to eliminate small areas smaller than the set area threshold, and the background points contacting the target area are merged into the corresponding target object by using the expansion method, so that the target boundary expands outward to eliminate holes and small particle noise smaller than the set area threshold.

7. A building density extraction method according to claim 1, characterized in that: When processing the community sub-region data in step 400, the GIS software is used to set a minimum area threshold, objects smaller than the area threshold are eliminated according to the boundaries, and then the fragmented surfaces are merged into nearby surface elements.

8. A model, characterized in that: An extraction model established by a building density extraction method as described in any one of claims 1-7.

9. A model according to claim 8, characterized in that: The model is programmed into an automated script using the script programming function of GIS software and applied in the GIS software in the form of a secondary menu window to calculate the building density of the target area.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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