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 PDF

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WO2020233152A1
WO2020233152A1 PCT/CN2020/071905 CN2020071905W WO2020233152A1 WO 2020233152 A1 WO2020233152 A1 WO 2020233152A1 CN 2020071905 W CN2020071905 W CN 2020071905W WO 2020233152 A1 WO2020233152 A1 WO 2020233152A1
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building
urban
built
continuous
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杨俊宴
邵典
孙瑞琪
史北祥
曹俊
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东南大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

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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

一种基于城市建筑空间数据的建成区边界识别方法及设备，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。解决了现有识别方法耗时长、人力投入大、边界识别依赖人脑判断随意性大和对图像质量要求高等不足，通过数据的聚类分析和空间聚合实现对城市建成区边界进行高精度识别，高效、精准地满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求。

Description

基于城市建筑空间数据的建成区边界识别方法及设备 技术领域
本发明涉及一种城市建成区边界识别方法及设备，特别是涉及一种基于城市建筑空间数据的建成区边界识别方法及设备。
背景技术
城市建成区边界是指城市行政区范围内,由建设分布相对集中的建筑物、市内公共设施及城市道路等所构成的城市建成区的范围边界线，其范围表明了城市在不同发展阶段建设用地的使用情况。随着社会经济的快速发展，中国城镇化速度惊人。城市建设用地边界的划定对反应城市发展的规模与速度，判断土地利用效益与增长趋势具有重要作用。一方面，建成区范围与边界的变化反应了城市发展的方向与规模，其精确合理划定对研究城市扩张变化和城市驱动力分析起了关键性的作用。另一方面，建成区的范围数值信息是人口密度，卫生设施水平，单位面积产值和扩张系数等一系列指标的统计依据，对城市下一轮发展战略规划起到十分重要的作用。
目前常用的城市建成区边界识别方法，一种是结合现状地形图，结合卫星影像，在CAD或地理信息系统中进行人工绘制，这样的识别方法存在制图时间长，投入人力资源大，边界识别依赖人脑判断随意性大等问题。一种是结合地理信息系统软件平台，对高分辨遥感图像，采用目视破译图像或计算机监测方法实现不同土地利用类型边界的分类，这样的识别方法对算法与图像质量要求较高，且其对建筑与硬质地面的最大似然区分存在差异。
发明内容
发明目的：本发明要解决的技术问题是提供一种基于城市建筑空间数据的建成区边界识别方法及设备，解决了现有识别方法耗时长、人力投入大、边界识别依赖人脑判断随意性大和对图像质量要求高等不足，通过数据的聚类分析和空间聚合实现对城市建成区边界进行高精度识别，高效、精准地满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求。
技术方案：本发明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。
进一步的，所述城市建筑空间矢量数据的内容包括建筑和街区。
进一步的，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。
进一步的，所述计算得到所述几何中心点的公式为：
Figure PCTCN2020071905-appb-000001
Figure PCTCN2020071905-appb-000002
Figure PCTCN2020071905-appb-000003
其中n为每个街区的顶点数量，i为街区顶点编号，x i为编号为i的顶点的经度，y i为编号为i的顶点的纬度，x i+1为编号为i+1的顶点的经度，y i+1为编号为i+1的顶点的纬度，C x为该街区几何中心点的经度，C y为该街区几何中心点的纬度。
进一步的，所述的聚类运算为Average Nearest Neighbor聚类算法，具体为：
Figure PCTCN2020071905-appb-000004
其中，i为街区编号,n为街区的数量，d i为编号为i的街区的几何中心与最近邻街区几何中心之间的距离，
Figure PCTCN2020071905-appb-000005
为相邻街区中心之间最小距离的平均值。
进一步的，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。
进一步的，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。
进一步的，所述栅格的边长为0.5米。
进一步的，步骤(4)中的筛选方法为自然间断点分级法。
进一步的，所述的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。
进一步的，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。
本发明所述的设备，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，使得处理器执行上述的方法。
有益效果：本发明具备以下优点：
1、基于建筑空间矢量数据，通过高精度栅格化空间聚合方法将连绵的建筑数据进行闭合，最大程度提高所识别边界的准确性；
2、通过无监督聚类生成街区几何中心的平均最近距离，以此作为建筑连绵与否的临界值，确保所识别边界的科学性及学理性，最大程度逼近人工识别城市建成区边界的常用方法；同时该方法普遍适用于各类城市，确保了本专利方法的普适性；
3、所有步骤和方法阶基于矢量数据和矢量运算方法，所识别出的城市建成区边界矢量数据结果满足可编辑、可操作、可计算的实际需求，保证了该识别方法的实用性和可操作性；
4、所自动提取的建成区边界能够快速高效地满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求，以避免传统方法耗时长、人力投入大、边界识别依赖人脑判断随意性强的问题；实现了普遍适用于各类城市的高效、精准、自动化的建成区边界，快速高效地满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求。
附图说明
图1为本发明实施例的整体方法流程图；
图2为实施例的相邻街区的几何中心点最近距离计算图；
图3为实施例的区分建筑连绵示意图；
图4为实施例的建筑进行空间聚合原理图；
图5为实施例的空间聚合后建筑连绵区面域图；
图6为实施例的根据聚类结果筛选的建筑连绵区面域图；
图7为实施例的去除孔洞的城市建成区边界图。
具体实施方式
如图1所示，本发明实施例公开了一种基于城市建筑空间数据的建成区边界识别方法，包括如下步骤
步骤1)：数据获取及输入：获取并储存给定范围内的城市建筑空间矢量数据，输入地理信息处理平台；其中，所述给定范围不得小于城市中心城区范围，城市中心城区范围即为对应城市的最新版城市总体规划中划定的中心城区范围；所述建筑空间矢量数据包含多边形街区面及多边形建筑面。
所述空间矢量数据可以通过规划局等相关政府职能部门获得。其中，建筑空间矢量 数据包含多边形街区面(也可以通过道路红线围合成面生成)、街区面内包含一个以上的多边形建筑面，以上数据可以为DWG格式或SHP格式等。所述地理信息处理平台用于矢量数据处理，包括：ArcGIS、CAD；
步骤2)：确定建筑连绵临界值：针对范围内的所有多边形街区面，通过几何计算得到每个街区的几何中心点，通过无监督聚类算法对相邻街区的几何中心点最近距离进行聚类运算，从而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；
所述针对范围内的所有多边形街区面，通过几何计算得到每个街区的几何中心点，其目的在于通过获取每个街区的几何中心(角平分线交点)来为下一步的几何中心距离聚类运算做准备。该步骤包含两种方法，方法一为在ArcGIS中运用要素转点(Feature to point)指令，将多边形街区面转换为每个面的中心点，所述中心点包含坐标数据；方法二即通过软件代码编程(运用Python编程工具)，通过获取每个多边形街区面每个顶点的坐标数据，计算其几何中心点的坐标数据，其实现方式按照如下公式得到每个几何中心的矢量数据：
Figure PCTCN2020071905-appb-000006
Figure PCTCN2020071905-appb-000007
Figure PCTCN2020071905-appb-000008
其中n为每个街区的顶点数量，i为街区顶点编号，x i为编号为i的顶点的经度，y i为编号为i的顶点的纬度，x i+1为编号为i+1的顶点的经度，y i+1为编号为i+1的顶点的纬度，C x为该街区几何中心点的经度，C y为该街区几何中心点的纬度。
在得到所有多边形街区面几何中心及其坐标数据之后，下一步需要通过无监督聚类算法，获取所有相邻街区的几何中心点最近距离的平均值，作为区分建筑连绵与否的临界值，几何中心点最近距离计算如图2所示。具体地，所述无监督算法包含基于中心点的K-means聚类算法、基于连接距离的分层聚类算法、基于点密度的DBSCAN聚类以及基于非线性降维的t-SNE聚类算法，实施例需计算相邻街区之间最小距离的平均值，故优选分层聚类中的Average Nearest Neighbor聚类算法处理，具体为：
Figure PCTCN2020071905-appb-000009
其中，i为街区编号,n为街区的数量，d i为编号为i的街区的几何中心与最近邻街区中心位置之间的距离，
Figure PCTCN2020071905-appb-000010
为街区的几何中心与最近邻街区中心位置之间的平均值(即建筑连绵的临界值)。
将相邻街区中心之间最小距离的平均值
Figure PCTCN2020071905-appb-000011
作为区分建筑连绵与否的临界值，其原理为：相邻街区中心之间最小距离的平均值，即为城市平均街区边长与城市平均道路宽度之和。当两个建筑在同一街区，则彼此连绵；当两个建筑在相邻街区，则彼此连绵；当两个建筑之间的距离大于一个街区宽度与一条道路宽度的总和，则彼此不连绵。因此，如图3所示，当两个相邻建筑之间的最近距离小于生成街区几何中心的预测平均距离，则判定建筑连绵，反之则建筑不连绵。
步骤3)：建筑连绵区面域生成：计算所有建筑两两之间的距离并以建筑连绵临界值为界将其分为两个数据集，将其中建筑连绵临界值以内的数据集所包含建筑进行空间聚合，得到建筑连绵区面域；
所述计算所有建筑两两之间的距离并根据建筑连绵临界值具体为，将其分为两个数据集，计算城市内所有建筑两两之间的距离(建筑中心点距离)，每两个建筑及其距离形成一组数据元，N个建筑共形成
Figure PCTCN2020071905-appb-000012
组数据元。然后对每组数据元进行判定，将建筑距离在建筑连绵临界值以内的数据元归入数据集A、以外的归入数据集B，判定方法如下：
数据集
Figure PCTCN2020071905-appb-000013
数据集
Figure PCTCN2020071905-appb-000014
其中
Figure PCTCN2020071905-appb-000015
为建筑连绵临界值(即街区的几何中心与最近邻街区中心位置之间的平均值)，D i为编号为i的数据元中两个建筑中心点距离。
所述将其中建筑连绵临界值以内的数据集所包含建筑进行空间聚合，其方法如图4所示，将建筑空间矢量数据转换为宽度为0.5米的栅格，并对所有栅格赋值为0；将数据集A中每一组数据元都进行如下操作：将数据元中两个建筑的所有顶点进行依次连线，生成其中面积最大的一个不规则多边形，并将该不规则多边形所覆盖的栅格赋值为1；最后将所有值为1的栅格合并成一个完整的面域，所生成完整面域即建筑连绵区面域。所生成的建筑连绵区面域如图5所示，所述建筑连绵区面域为将间距平均最近距离以内的建筑及其之间的外部空间相连结所构造的面域。
步骤4)：建成区边界提取：计算所有建筑连绵区面域的几何面积，利用地理信息系统数据集中分类方法筛选出面积值最高的一组建筑连绵区面域，导出其边界轮廓线，得 到城市建成区边界。
地理信息系统数据集中分类方法可以采用等距离分级、分位数分级、等面积分级、标准差分级、自然间断点分级法(Jenks)等多种分类方法。优选的采用自然间断点分级法(Jenks)，自然间断点分级法的聚类原理为将数据划分为数个组，保证组与组之间的数值方差最大、组内方差最小，其中分组的个数视不同城市规模情况而定，且组数不小于3组。因为城市建成区是对实际建设发展起来的非农业生产建设地段的统称，且具有一定的建设规模以保持基本完善的市政公用设施建设，因此需要筛选掉规模较小的非城市化区域以及无法满足市政公用设施建设需求的较小规模区域，本处需将所有建筑连绵区面域的几何面积根据其数值进行分组并保证组与组之间的数值差异最大。自然间断点分级法(Jenks)能够模拟城市的建成特征，最逼近人工识别建成区边界的判定依据，基于所有建筑连绵区面域中固有的自然分组，对相似类进行最恰当分组，并在数据值的差异相对较大的位置处设置边界，即将数据划分为数个组，保证组与组之间的数值方差最大、组内方差最小。具体地，将所有建筑连绵区面域的几何面积按照其数值分布进行聚类并分为多个数值区间，选取其中范围上限最高的数值区间作为筛选区间，将这一区间内对应的建筑连绵区面域导出其边界轮廓线。
导出其边界轮廓线的具体方法为，对每个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，其外部轮廓线即为城市建成区边界。
利用本发明实施例的建筑空间大数据的城市建成区边界高精度识别方法，能够对不同城市建成区进行科学快速的自动绘制识别，所自动提取的建成区边界能够满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求，并避免传统方法耗时长、人力投入大、边界识别依赖人脑判断随意性强的问题；实现了普遍适用于各类城市的高效、精准、自动化识别建成区边界，高效精准地满足城市发展建设现状评估和规划领域所需的各项指标数据和矢量分析需求。
以下将以天津市城市建成区边界高精度识别为例对本发明的技术方案进行详细说明。
(1)以天津作为目标城市，获取城市建成区域内的空间矢量数据，其范围不得小于城市最新版总体规划划定的城市中心城区范围，并将空间数据录入空间矢量平台，具体包括：
(1.1)通过天津市规划局或自然资源局获得天津的空间矢量数据，包含天津市域范围内的城市街区数据及建筑数据，以上数据均为CAD文件或SHP文件；
(1.2)空间矢量数据中的现状闭合街区CAD文件、现状闭合建筑CAD文件或SHP文件导入ArcGIS软件或其他空间矢量平台，并导出闭合面(Polygon)的SHP格式；
(2)通过对范围内所有的多边形街区面，计算其几何中心点，并通过无监督类算法对相邻街区的几何中心点距离进行聚类运算，确定建筑连绵的临界值；
(2.1)通过ArcGIS识别空间数据的地理坐标系统，来获取每个多边形街区每个顶点的坐标数据，以此来计算所有多边形街区面几何中心点的坐标，其实现方式按照上述的公式得到每个街区几何中心的矢量数据。
(2.2)利用所得到的所有多边形街区面几何中心及其坐标数据，测量每个街区几何中心与其最邻近街区中心位置之间的距离。通过使用上述的Average Nearest Neighbor聚类算法，计算得出相邻街区中心之间最小距离的平均值(如图2)，即建筑连绵的临界值。
具体计算结果如表1所示：
表1平均预测距离结果表
天津建筑连绵的临界值 147.6624米
P值 0.0047
(3)计算天津所有建筑两两之间的距离并以建筑连绵临界值为界将其分为两个数据集，将其中建筑连绵临界值以内的数据集所包含建筑进行空间聚合，得到建筑连绵区面域；
(3.1)计算所有建筑两两之间的距离(中心点距离)并根据建筑连绵临界值将其分为两个数据集，其计算中心点方法与(2.1)一致；天津共包含21万个建筑，因此形成210万组数据元，每组数据元中包含两两匹配的建筑及其中心点距离数据；然后对每组数据元进行判定，将建筑中心点距离在建筑连绵临界值147.6624米以内的数据元归入数据集A、以外的归入数据集B，如表2：
表2数据集统计表
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
其中D i为编号为i的数据元中两个建筑中心点距离；
(3.2)将天津建筑空间数据栅格化，将建筑空间矢量数据以及外部空间转换为宽度为0.5米的栅格，并对所有栅格赋值为0；
(3.3)将数据集A中每一组数据元都进行如下操作：将数据元中两个建筑的所有顶点进行依次连线，生成其中面积最大的一个不规则多边形，并将该不规则多边形所覆盖的栅格赋值为1；最后将所有值为1的栅格合并成一个完整的面域，所生成完整面域即建筑连绵区面域，如图5所示；
(4)计算所有建筑连绵区面域的几何面积，利用自然间断点分级法(Jenks)筛选 出面积值最高的一组建筑连绵区面域，导出其边界轮廓线，得到城市建成区边界；
(4.1)对步骤(3.3)中生成的建筑连绵区面域按用自然间断点分级法进行根据面积大小分为5类进行数值分布聚类，生成根据面积大小排序的建筑连绵区汇总表：
表3建筑连绵区汇总表
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
(4.2)从上表中筛选出自然裂点类别中范围上限最高的一组建筑连绵区面域如图6所示，将筛选出的面域填补其内部孔洞，只保留其外部轮廓边界及内部完整的不含孔洞的闭合面；
(4.3)如图7所示，将不含孔洞的建筑连绵区面域导出SHP或CAD格式文件，其外部轮廓线即为识别出的城市建成区边界。
本发明的实施例还提供了一种设备，设备包括存储器和至少一个处理器、存储在所述存储器中并可在所述至少一个处理器上运行的计算机程序、至少一条通讯总线。所述至少一个处理器执行所述计算机程序时实现上述基于城市建筑空间数据的建成区边界识别方法。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指 令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。

Claims (10)

  1. 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。
  2. 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。
  3. 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。
  4. 根据权利要求3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，所述计算得到所述几何中心点的公式为：
    Figure PCTCN2020071905-appb-100001
    Figure PCTCN2020071905-appb-100002
    Figure PCTCN2020071905-appb-100003
    其中n为每个街区的顶点数量，i为街区顶点编号，x i为编号为i的顶点的经度，y i为编号为i的顶点的纬度，x i+1为编号为i+1的顶点的经度，y i+1为编号为i+1的顶点的纬度，C x为该街区几何中心点的经度，C y为该街区几何中心点的纬度。
  5. 根据权利要求3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，所述的聚类运算为Average Nearest Neighbor聚类算法，具体为：
    Figure PCTCN2020071905-appb-100004
    其中，i为街区编号,n为街区的数量，d i为编号为i的街区的几何中心与最近邻街区几何中心之间的距离，
    Figure PCTCN2020071905-appb-100005
    为相邻街区中心之间最小距离的平均值。
  6. 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。
  7. 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。
  8. 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。
  9. 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。
  10. 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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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112579718A (zh) * 2020-12-14 2021-03-30 深圳市城市交通规划设计研究中心股份有限公司 一种城市用地功能的识别方法、装置及终端设备
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150221128A1 (en) * 2014-01-31 2015-08-06 Hover Inc. Scale error correction in a geo-referenced multi-dimensional model
CN105095888A (zh) * 2015-09-18 2015-11-25 中国科学院遥感与数字地球研究所 一种基于不透水面聚集密度的城市主要建成区遥感提取方法
CN108009201A (zh) * 2017-10-31 2018-05-08 衡阳师范学院 一种基于空间聚类的城市建成区边界提取方法
CN108520142A (zh) * 2018-04-04 2018-09-11 兰州交通大学 一种城市群边界识别方法、装置、设备及存储介质
CN110135351A (zh) * 2019-05-17 2019-08-16 东南大学 基于城市建筑空间数据的建成区边界识别方法及设备

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103824309B (zh) * 2014-03-12 2017-01-18 武汉大学 一种城市建成区边界自动提取方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150221128A1 (en) * 2014-01-31 2015-08-06 Hover Inc. Scale error correction in a geo-referenced multi-dimensional model
CN105095888A (zh) * 2015-09-18 2015-11-25 中国科学院遥感与数字地球研究所 一种基于不透水面聚集密度的城市主要建成区遥感提取方法
CN108009201A (zh) * 2017-10-31 2018-05-08 衡阳师范学院 一种基于空间聚类的城市建成区边界提取方法
CN108520142A (zh) * 2018-04-04 2018-09-11 兰州交通大学 一种城市群边界识别方法、装置、设备及存储介质
CN110135351A (zh) * 2019-05-17 2019-08-16 东南大学 基于城市建筑空间数据的建成区边界识别方法及设备

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
胡斌斌 等 (HU, BINBIN ET AL.): "地名注记的街区式居民地边界提取方法 (the Method of Street-Block Settlement Boundary Extraction Based on Geographic Name Annotation)", 测绘科学 (SCIENCE OF SURVEYING AND MAPPING), vol. 43, no. 8, 31 August 2018 (2018-08-31), XP55756558 *

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