WO2020233152A1 - 基于城市建筑空间数据的建成区边界识别方法及设备 - Google Patents
基于城市建筑空间数据的建成区边界识别方法及设备 Download PDFInfo
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- the invention relates to a method and equipment for recognizing the boundary of an urban built-up area, in particular to a method and equipment for recognizing the boundary of a built-up area based on urban building space data.
- the boundary of the urban built-up area refers to the boundary of the urban built-up area composed of relatively concentrated buildings, urban public facilities and urban roads within the urban administrative area. Its scope indicates the construction land of the city at different stages of development Usage. With the rapid development of social economy, the speed of urbanization in China is astonishing. The delineation of the boundary of urban construction land plays an important role in reflecting the scale and speed of urban development, and judging the benefits and growth trends of land use. On the one hand, changes in the scope and boundaries of built-up areas reflect the direction and scale of urban development, and their precise and reasonable delineation plays a key role in the study of urban expansion changes and the analysis of urban driving forces. On the other hand, the numerical information of the built-up area is the statistical basis for a series of indicators such as population density, level of sanitation facilities, output value per unit area and expansion coefficient, which will play a very important role in the next round of urban development strategy planning.
- the commonly used urban built-up area boundary identification methods are combined with current topographic maps, combined with satellite images, and manually drawn in CAD or geographic information systems.
- Such identification methods have long drawing time, large investment in human resources, and boundary identification.
- the human brain judges arbitrariness and other problems.
- One is to combine the geographic information system software platform to use visual deciphering images or computer monitoring methods to classify the boundaries of different land use types for high-resolution remote sensing images.
- recognition methods have high requirements for algorithms and image quality, and their There are differences in the maximum likelihood distinction between buildings and hard floors.
- the technical problem to be solved by the present invention is to provide a built-up area boundary recognition method and equipment based on urban architectural spatial data, which solves the time-consuming, labor-intensive, and arbitrary boundary recognition of the existing recognition method.
- high-precision recognition of the boundaries of urban built-up areas is achieved through data clustering analysis and spatial aggregation, which efficiently and accurately meets the various index data and vector analysis required in the field of urban development and construction assessment and planning demand.
- the method for identifying the boundary of built-up areas based on urban building space data of the present invention is characterized by including the following steps:
- the content of the urban building space vector data includes buildings and blocks.
- the method for determining the critical value in step (2) is: calculating the geometric center point of each block in the urban building space vector data, and clustering the nearest distances of the geometric center points of adjacent blocks to generate adjacent blocks The average value of the minimum distance between the centers is used as the critical value to distinguish whether the building is continuous or not.
- n is the number of vertices in each block
- i is the vertex number of the block
- x i is the longitude of the vertex numbered i
- y i is the latitude of the vertex numbered i
- x i+1 is the vertex numbered i+1
- the longitude of, y i+1 is the latitude of the vertex numbered i+1
- C x is the longitude of the geometric center point of the block
- Cy is the latitude of the geometric center point of the block.
- clustering operation is Average Nearest Neighbor clustering algorithm, specifically:
- i is the block number
- n is the number of blocks
- D i is a number between Linjie region nearest the geometric center of the block from the geometric center of i, It is the average value of the minimum distance between the centers of adjacent blocks.
- the method for generating the area of the building continuous area in step (3) is: calculating the distance between all the buildings, spatially aggregate the buildings whose distance is less than or equal to the critical value, and combine the above-mentioned spatially aggregated buildings with the buildings. The external space between them is connected to obtain the area of the continuous building area.
- the method of spatial aggregation is: converting the architectural space vector data into a fixed-size raster, connecting all the vertices of the buildings whose distance is less than or equal to the critical value, and selecting the polygon with the largest area. , Merge the grids covered by all the polygons selected above into a complete area.
- the side length of the grid is 0.5 meters.
- the screening method in step (4) is the natural break point classification method.
- the specific steps of the natural discontinuity grading method are as follows: according to the size of the geometric area, all the building continuous areas are divided into several groups by the natural discontinuity grading method, and the numerical interval with the highest range upper limit is selected as the screening interval , Select the area of the continuous building area in this interval.
- the method of deriving the boundary contour line in step (4) is: filling the internal holes of the screened building continuous area area and removing the boundary of the hole to obtain the building continuous area area without holes, and its outer contour line That is the boundary contour line.
- the device of the present invention includes a computer memory and a processor.
- the memory stores computer readable instructions.
- the processor executes the above method.
- the present invention has the following advantages:
- the continuous architectural data is closed by the high-precision rasterization spatial aggregation method to maximize the accuracy of the identified boundary;
- the automatically extracted boundary of the built-up area can quickly and efficiently meet the needs of various index data and vector analysis required in the field of urban development and construction status assessment and planning, so as to avoid traditional methods that are time-consuming, labor-intensive, and rely on human brain for boundary recognition Judging problems with strong arbitrariness; achieving efficient, accurate, and automated built-up area boundaries that are universally applicable to all types of cities, quickly and efficiently meeting the needs of various index data and vector analysis required in the field of urban development and construction assessment and planning.
- Figure 1 is a flow chart of the overall method of an embodiment of the present invention.
- FIG. 2 is a calculation diagram of the nearest distance between geometric center points of adjacent blocks in an embodiment
- Fig. 3 is a schematic diagram of the continuous construction of differentiating buildings according to the embodiment.
- Figure 4 is a schematic diagram of the spatial aggregation of the building of the embodiment
- Figure 5 is an area map of the continuous building area after spatial aggregation of the embodiment
- Fig. 6 is an area map of the continuous building area screened according to the clustering results of the embodiment
- Fig. 7 is a boundary diagram of an urban built-up area with holes removed according to an embodiment.
- an embodiment of the present invention discloses a method for identifying the boundary of a built-up area based on urban building space data, which includes the following steps
- the space vector data can be obtained through relevant government functional departments such as the Planning Bureau.
- the building space vector data includes polygonal block faces (also can be generated by road red lines), and the block faces contain more than one polygonal building faces.
- the above data can be in DWG format or SHP format.
- the geographic information processing platform is used for vector data processing, including: ArcGIS and CAD;
- Step 2) Determine the critical value of building continuity: For all polygonal block faces in the range, the geometric center point of each block is obtained through geometric calculation, and the nearest distance between the geometric center points of adjacent blocks is clustered through unsupervised clustering algorithm Calculation to generate the average value of the minimum distance between the centers of adjacent blocks as the critical value to distinguish whether the building is continuous or not;
- the geometric center point of each block is obtained by geometric calculation for all polygonal block faces in the range, and the purpose is to cluster the geometric center distance of the next step by obtaining the geometric center (angle bisector intersection) of each block Prepare for operation.
- This step includes two methods. The first method is to use the feature to point command in ArcGIS to convert the polygonal block surface into the center point of each surface, and the center point contains the coordinate data; the second method is to use the software Code programming (using Python programming tools), by obtaining the coordinate data of each vertex of each polygonal street block surface, calculate the coordinate data of its geometric center point, the implementation method is based on the following formula to get the vector data of each geometric center:
- n is the number of vertices in each block
- i is the vertex number of the block
- x i is the longitude of the vertex numbered i
- y i is the latitude of the vertex numbered i
- x i+1 is the vertex numbered i+1
- the longitude of, y i+1 is the latitude of the vertex numbered i+1
- C x is the longitude of the geometric center point of the block
- Cy is the latitude of the geometric center point of the block.
- the next step is to use an unsupervised clustering algorithm to obtain the average of the nearest distances of the geometric center points of all adjacent blocks, as the critical value to distinguish whether the building is continuous or not.
- the calculation of the closest distance to the center point is shown in Figure 2.
- the unsupervised algorithm includes K-means clustering algorithm based on center point, hierarchical clustering algorithm based on connection distance, DBSCAN clustering based on point density, and t-SNE clustering algorithm based on nonlinear dimensionality reduction
- the embodiment needs to calculate the average value of the minimum distance between adjacent blocks, so the Average Nearest Neighbor clustering algorithm processing in hierarchical clustering is preferred, specifically:
- i is the block number
- n is the number of blocks
- D i is a number between the center position of the region nearest Linjie block from the geometric center of i, It is the average value between the geometric center of the block and the center of the nearest neighboring block (that is, the critical value of continuous building).
- the average value of the minimum distance between the centers of adjacent blocks is the sum of the average city block side length and the city's average road width.
- the distance between the two buildings is greater than the sum of the width of a block and the width of a road, they are not connected to each other. Therefore, as shown in Figure 3, when the closest distance between two adjacent buildings is less than the predicted average distance of the geometric center of the generated block, the building is determined to be continuous, otherwise, the building is not continuous.
- Step 3) Building continuous area area generation: Calculate the distance between all buildings and divide it into two data sets based on the critical value of building continuous, and divide the buildings included in the data set within the critical value of building continuous Carry out spatial aggregation to obtain the area of the building continuous area;
- the distance between all buildings in pairs is divided into two data sets, and the distance between all buildings in the city (the distance between the center points of the buildings) is calculated. Buildings and their distances form a set of data elements, and N buildings form a total Group of data elements. Then each group of data elements is judged, and the data elements whose building distance is within the building continuous critical value are classified into data set A, and the other data elements are classified into data set B.
- the judgment method is as follows:
- D i is the distance between the center points of the two buildings in the data element numbered i.
- the method for spatial aggregation of the buildings included in the data set within the critical value of the building continuousness is shown in Figure 4.
- the building space vector data is converted into a grid with a width of 0.5 meters, and all grids are assigned a value of 0 ;
- the grid is assigned a value of 1.
- all grids with a value of 1 are merged into a complete area, and the resulting complete area is the area of the building continuous area.
- the generated building continuous area area is shown in FIG. 5, and the building continuous area area is an area constructed by connecting buildings within the average shortest distance between them and the external space between them.
- the centralized classification method of geographic information system data can adopt a variety of classification methods such as equidistant classification, quantile classification, equal area classification, standard difference classification, and natural discontinuity classification (Jenks).
- the natural break point classification method (Jenks) is preferably used.
- the clustering principle of the natural break point classification method is to divide the data into several groups to ensure that the numerical variance between groups is the largest and the variance within the group is the smallest.
- the number of groups is It depends on the size of different cities, and the number of groups is not less than 3 groups.
- the urban built-up area is a general term for the non-agricultural production and construction areas that have been actually developed and have a certain construction scale to maintain the basic and complete construction of municipal public facilities, it is necessary to filter out smaller non-urbanized areas and unsatisfactory For small-scale areas that require the construction of municipal public facilities, this office needs to group all the geometric areas of the contiguous area of buildings according to their numerical values and ensure that the numerical difference between groups is the largest.
- the natural discontinuity classification method Jenks
- Jenks can simulate the built-up characteristics of the city, and is the closest to the judgment basis of artificially identifying the boundary of the built-up area.
- the similar categories are most appropriately grouped, and the data Set the boundary at the position where the value difference is relatively large, that is, divide the data into several groups to ensure that the numerical variance between groups is the largest and the variance within the group is the smallest.
- the geometric area of all the architectural contiguous areas is clustered according to their numerical distribution and divided into multiple numerical intervals, and the numerical interval with the highest range upper limit is selected as the screening interval, and the corresponding architectural contiguous area in this interval
- the region exports its boundary contour lines.
- the specific method of deriving its boundary contour line is to fill in the internal holes of each selected architectural continuous area area and remove the boundary of the holes to obtain the architectural continuous area area without holes, and its external contour line is the city built District boundary.
- the method for high-precision recognition of urban built-up area boundaries using the architectural space big data of the embodiment of the present invention can perform scientific and rapid automatic drawing and recognition of different urban built-up areas, and the automatically extracted built-up area boundaries can meet the assessment and planning of urban development and construction.
- Various index data and vector analysis requirements required by the field, and avoid traditional methods that are time-consuming, labor-intensive, and boundary recognition relies on human brain judgments to be highly arbitrary; it achieves high efficiency, accuracy, and automation that is universally applicable to various cities Identify the boundary of the built-up area, efficiently and accurately meet the needs of various index data and vector analysis required in the field of urban development and construction status assessment and planning.
- D i is the distance between the center points of the two buildings in the data element numbered i;
- step (4.1) The area of the building continuous area generated in step (3.3) is classified into 5 categories according to the area size according to the natural discontinuity point classification method to perform numerical distribution clustering, and a summary table of the building continuous area sorted by area size is generated:
- Natural break point category Building area (m2) Number of continuous areas 1 14339889.1-494656450.3 1 2 5936354.3-14339889.1 4 3 2245590.5-5936354.3 17 4 72488.5-2245590.5 38 5 48.89-72488.5 370
- An embodiment of the present invention also provides a device, which includes a memory and at least one processor, a computer program stored in the memory and executable on the at least one processor, and at least one communication bus.
- a device which includes a memory and at least one processor, a computer program stored in the memory and executable on the at least one processor, and at least one communication bus.
- the at least one processor executes the computer program, the above-mentioned built-up area boundary recognition method based on urban building space data is implemented.
- the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
- a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
- These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
- the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
- These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
- the instructions provide steps for implementing functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in the block diagram.
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Abstract
Description
天津建筑连绵的临界值 | 147.6624米 |
P值 | 0.0047 |
数据集 | 判定条件 | 数据元组数 |
数据集A | D i≤147.6624米 | 79万组 |
数据集B | D i>147.6624米 | 131万组 |
自然裂点类别 | 建筑面积区间(㎡) | 连绵面域个数 |
1 | 14339889.1-494656450.3 | 1 |
2 | 5936354.3-14339889.1 | 4 |
3 | 2245590.5-5936354.3 | 17 |
4 | 72488.5-2245590.5 | 38 |
5 | 48.89-72488.5 | 370 |
Claims (10)
- 一种基于城市建筑空间数据的建成区边界识别方法,其特征在于包括以下步骤:(1)将城市建筑空间矢量数据输入地理信息处理平台;(2)确定区分建筑连绵与否的临界值;(3)生成建筑连绵区面域;(4)筛选建筑面积值最高的一组建筑连绵区面域,导出其边界轮廓线。
- 根据权利要求1所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于:所述城市建筑空间矢量数据的内容包括建筑和街区。
- 根据权利要求1所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于步骤(2)中确定临界值的方法为:计算得到城市建筑空间矢量数据中每个街区的几何中心点,对相邻街区的几何中心点最近距离进行聚类运算,生成相邻街区中心之间最小距离的平均值,作为区分建筑连绵与否的临界值。
- 根据权利要求1所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于,步骤(3)中生成建筑连绵区面域的方法为:计算所有建筑之间的距离,将距离小于等于所述临界值的建筑进行空间聚合,将上述空间聚合的建筑与所述建筑之间的外部空间相连结,得到所述的建筑连绵区面域。
- 根据权利要求6所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于,所述空间聚合的方法为:将所述建筑空间矢量数据转换为固定大小的栅格,将距离小于等于所述临界值的建筑的所有顶点进行连线,选取其中面积最大的一个多边形,将上述选取的所有多边形覆盖的栅格合并成一个完整面域。
- 根据权利要求1所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于,步骤(4)中的筛选方法为自然间断点分级法,具体步骤为:按照几何面积的大小将所有建筑连绵区面域采取自然间断点分级法分为若干个组,选取其中范围上限最高的数值区间作为筛选区间,选择这一区间内的建筑连绵区面域。
- 根据权利要求1所述的基于城市建筑空间数据的建成区边界识别方法,其特征在于,步骤(4)中导出边界轮廓线的方法为:将筛选出的建筑连绵区面域填充其内部的孔洞并去除孔洞边界,得到不含孔洞的建筑连绵区面域,其外部轮廓线即为所述的边界轮廓线。
- 一种设备,包括计算机存储器和处理器,所述的存储器中存储有计算机可读指令,所述计算机可读指令被所述处理器执行时,使得处理器执行如权利要求1至9任一项所述的方法。
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