CN107679229A - The synthetical collection and analysis method of city three-dimensional building high-precision spatial big data - Google Patents

Abstract

The invention discloses a method for comprehensive collection and analysis of high-precision spatial big data of urban three-dimensional buildings. The method includes the following steps: (1) establishing a basic topography database; (2) establishing an urban traffic database; (3) establishing an urban building ‑Land use database; (4) Construct elevation‑contour database; (5) Establish a 3D model of the city to form high-precision spatial big data of 3D buildings in the city; (6) Conduct data mining analysis and visual display of 3D modeling data. The present invention can cope with the processing of massive spatial form data, perform fast and efficient acquisition of high-precision spatial big data of urban three-dimensional buildings and measurement of spatial structural elements, and realize comprehensive collection and information synthesis of urban spatial analysis basic data based on artificial intelligence system, Contribute to the comprehensive, standardized and efficient operation of urban planning and design.

Description

Comprehensive acquisition and analysis method of high-precision spatial big data of urban 3D buildings

technical field

The invention relates to a mining, collection and analysis of urban spatial big data, in particular to a comprehensive collection and analysis method of urban three-dimensional building high-precision spatial big data.

Background technique

Urban high-precision spatial big data with three-dimensional buildings as the smallest unit has always been the core foundation of spatial planning and even urban planning, but this database has the characteristics of difficulty in obtaining, long cycle, and small samples. In response to this problem, it is of great significance to obtain a large database of urban space by using a multi-source approach of high-tech informatization and intelligentization for the study of the evolution of urban spatial form and the internal development mechanism of the city. Compared with the role of traditional remote sensing satellite maps in distinguishing various elements of the city through infrared bands, the high-precision spatial large database of urban 3D buildings based on imaging technology and data mining has the characteristics of full samples, positioning, visualization, and real-time monitoring. More importantly, this large database will carry out comprehensive and comprehensive information expression and analysis of various spatial systems and spatial units of the city from the surface to the depth, from the real to the virtual.

In the research of image technology and data mining, it is an important part to distinguish various data elements through different types of data sources. The establishment of a high-precision spatial large database of urban 3D buildings is related to professional information identification, data extraction, dynamic change prediction and Comprehensive map making and other important aspects. This database will meet the needs of urban planning and design, auxiliary decision-making and urban management of the increasingly large urban space big data, which is one of the problems faced by urban planning.

At present, domestic and foreign studies on urban 3D building high-precision spatial large databases mainly focus on the collection, analysis, and visualization of single data, less attention is paid to the mutual influence relationship between various data systems in urban space, and there is even a lack of comprehensive urban space at the level of 3D buildings. Production and dynamic display technology of overlaid three-dimensional building high-precision spatial large database. In the collection of high-precision spatial large databases of urban 3D buildings, there is a lack of urban geographic spatial coordinates, data processing in a single dimension, and lagging behind in the field of urban spatial big data collection, integration and display of large-scale composite data.

Contents of the invention

Purpose of the invention: Aiming at the deficiencies of the above-mentioned prior art, the present invention provides a comprehensive acquisition and analysis method for high-precision spatial big data of urban three-dimensional buildings. Seamless collection of high-precision spatial big data and simulated dynamic display of spatial features.

Technical solution: comprehensive acquisition and analysis method of urban three-dimensional building high-precision spatial big data, including the following steps:

(1) Collect high-definition remote sensing image data in the area to be measured, and establish a database of urban topography and geomorphology;

(2) Extract the information of roads and various traffic stations in the area through open source urban big data on the Internet, and establish an urban traffic database;

(3) Identify the outer contour points of buildings and land units within the area from the Internet city map code big data, and establish a building-land database;

(4) Use SRTM DEM for data acquisition of elevation points and contour line data within the area, and establish an elevation-contour line database;

(5) Transform the above database into a unified wgs84 coordinate system after translation, scaling, and rotation space processing, and organize, manage and synthesize data in a unified shp data format, and establish high-precision spatial big data of urban 3D buildings;

(6) Carry out data mining analysis on the 3D modeling data, and distribute and visualize it through the geographic information system.

Wherein, the module of establishing topography database described in step (1) includes adopting infrared remote sensing sub-band technology to distinguish the basic elements of internal mountains, water bodies and green vegetation, and simultaneously extracting them for vectorization processing.

Further, the urban topography database module of setting up described in step (1) includes the following specific steps:

(1.1) Collect high-definition remote sensing image technology data in a certain area, and absorb the colors of mountains, water bodies and green vegetation elements in the high-definition remote sensing image images in the area based on the straw tool in the infrared band distinction platform, and then set Get the color standard threshold value of mountains, water bodies and green vegetation elements, and collect the image parts within the threshold of mountains, water bodies and green vegetation elements within the scope of the area, and set it as a separate file format;

(1.2) Import the element data of mountains, water bodies and green vegetation into VPstudio vectorization software respectively, and carry out the A contour line vectorization command operation, and at the same time adjust the general straightening command intensity to "strong" level, and "layer" this Adjust the first threshold level to the strongest level at the beginning and end, and finally use the V vectorization command to perform vectorization processing on the four types of elements respectively to form a basic base map for vectorization;

(1.3) Import the vectorized data of mountains, water bodies and green vegetation elements into the geographic information system, and store them as shp format files, in which the elements of mountains and water bodies are converted into multi-section faces, and the three types of data are converted into topography and landforms Database module 3D modeling basic data.

Further, the urban traffic database module described in step (2) specifically includes the following steps:

(2.1) Use the URL encoding method to access the backstage API port of openstreet map, and select the area in the same range as step A, and use the "Overpass API" command to download the data;

(2.2) Import the data obtained in step (2.1) into JSOM software, and use the keyword search method to search and find the spatial elements of roads and traffic stations, and export them separately into independent OSM format data for the extraction of spatial element data and screening;

(2.3) Import the OSM format data into the geographic information system and convert it into an independent SHP format file.

Further, the establishment of urban building-land database module described in step (3) specifically includes the following steps:

(3.1) Use the Gaode map coordinate picking tool to pick up the latitude and longitude coordinates of the boundary nodes within the same area, and check and record them;

(3.2) Use the building coding program in the IDLE (Python GUI) module in the Python software to capture the building outline points within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to capture the building outline points Convert it into the outline of the building and give it building height attribute information;

(3.3) Use the land use coding program in the IDLE (Python GUI) module of the Python software to capture the outer contour points of the land plot within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to convert the land use plot The outer contour points are converted into the outer contour line of the land plot, and endowed with land use property attribute information;

Further, the described step (4) establishes the elevation-contour database including developing methods and vectorization through API codes, using the valley geographic information system platform to grab elevation points and contour data in the region, and Select the required accuracy and coordinate system, and export it into a csv format file; then import the csv format file into the Arcgis platform, and convert it into a SHP format file.

Further, the step (5) of establishing high-precision spatial big data of urban three-dimensional buildings specifically includes the following steps:

(5.1) Carry out comprehensive modeling of a single type of big data, import them into the geographic information system database respectively, and align the spatial positions of the four types of data modules in accordance with a unified data format, and convert various database modules into a unified wgs84 coordinate system , so that the value of each type of data can be spatially coupled with the value of other types of data;

(5.2) Perform multi-level data format conversion, and convert data types in different formats into unified or mutually convertible data formats.

(5.3) Unify the six types of data after alignment into the block surrounded by urban roads as the basic statistical unit. Each block basic unit includes topography database module, traffic database module, building-land database module and elevation- The data information of the four modules of the contour database module.

Further, step (6) includes counting the building quantity data, building base area data, and building total area data within each land use unit, and calculating spatial indicators such as building density and floor area ratio in each block.

Beneficial effects: Compared with the prior art, the present invention has significant effects as follows: 1. Based on image analysis and big data mining, the present invention can deal with the processing of massive data, and carry out real-time and rapid processing of urban three-dimensional building high-precision spatial big data. Comprehensive acquisition; 2. By superimposing various types and multi-system urban spatial data under the same digital map system, the seamless collection of urban three-dimensional building high-precision spatial big data based on the urban coordinate system and the identification of spatial characteristics are realized. Simulate dynamic display; 3. Realize urban spatial big data based on the urban coordinate system by superimposing the terrain database module, traffic database module, building-land database module and elevation-contour database module under the same digital map system The collection and comprehensive analysis display; 4. Dividing multiple layers through different ways and types corresponds to the corresponding urban space big data elements, which is conducive to classification management and selection operations. Setting the selection function can quickly select the display range, thereby reducing manual duplication of labor. It is convenient for data input and output, rapid analysis and image export; 5. The present invention seamlessly integrates the data of various elements of urban spatial form with multiple interfaces, realizes rapid acquisition of massive data and intuitive query display, and provides services for government functional departments and architectural design, urban Provide data access in the field of planning; 6. Urban three-dimensional building high-precision spatial big data elements can be displayed through selection query, and the required data and images can be dynamically displayed on the computer to further provide decision-making for the improvement of urban system mechanisms and spatial form optimization plan.

Description of drawings

Fig. 1 is a diagram of the comprehensive collection method of urban three-dimensional building high-precision spatial big data based on image analysis and big data mining in the present invention;

Fig. 2 is the spatial scope figure of Hangzhou City of the present invention;

Fig. 3 is a module diagram of Hangzhou urban topography database of the present invention;

Fig. 4 is a block diagram of the urban traffic database in Hangzhou of the present invention;

Fig. 5 is a module diagram of Hangzhou city building-land database of the present invention;

Fig. 6 is a block diagram of the Hangzhou city elevation-contour database of the present invention.

detailed description

In order to describe the technical solution disclosed in the present invention in detail, further elaboration will be made below in conjunction with the accompanying drawings and specific embodiments.

The comprehensive collection and analysis method of the three-dimensional building high-precision space big data of the city in conjunction with Hangzhou, China (total area 16596 square kilometers, permanent population 9.188 million, urbanization rate 76.2%), the data collection process of the present invention is shown in Figure 1 As shown, the specific operation steps are as follows:

(1) Collect the high-definition remote sensing image data within the urban area of Hangzhou, and use infrared remote sensing sub-band technology to distinguish the basic elements of internal mountains, water bodies and green vegetation, and extract them and vectorize them to form The topography database module, as shown in Figure 3;

(1.1) Collect the technical data of high-definition remote sensing images within the urban area of Hangzhou, and absorb the colors of mountains, water bodies and green vegetation elements in the high-definition remote sensing image images within the urban area of Hangzhou based on the straw tool in the infrared band discrimination platform, Then set the color standard threshold value of the mountain, water body and green vegetation elements, and collect the image parts within the threshold of the mountain body, water body and green vegetation elements within the scope of Hangzhou City, and set it as a separate file format;

(1.2) Import the element data of mountains, water bodies and green vegetation into VPstudio vectorization software respectively, and carry out the A contour line vectorization command operation, and at the same time adjust the general straightening command intensity to "strong" level, and "layer" this Adjust the first threshold level to the strongest level at the beginning and end, and finally use the V vectorization command to perform vectorization processing on the four types of elements respectively to form a basic base map for vectorization;

(1.3) Import the vectorized data of mountain body, water body and green vegetation elements into the geographic information system respectively, and store them as shp format files respectively. It is converted into basic data for 3D modeling of the terrain and landform database module.

(2) Use the URL encoding method to extract the roads and various traffic stations within the Hangzhou city area from the Internet open source city big data capture method, and store them as separate files to form a traffic database module, as shown in Figure 4 Show;

(2.1) Use the URL encoding method to access the backstage API port of openstreet map, and select the area in the same range as step A, and use the "Overpass API" command to download the data;

(2.2) Import the data downloaded according to step (2.1) into JSOM software, and use the keyword search method to search and find the spatial elements of roads and traffic stations, and export them separately into independent OSM format data for spatial element data extraction and screening;

(2.3) Import the OSM format data into the geographic information system and convert it into an independent SHP format file.

(3) Use the Java language code method to identify the outer contour points of buildings and land use units within the urban area of Hangzhou from the Internet city map code big data method, and integrate them into multiple sections of buildings and land use to form a building-land use database module , as shown in Figure 5;

(3.1) Use the AutoNavi map coordinate picking tool to pick up the latitude and longitude coordinates of the boundary nodes within the same Hangzhou city area, and check and record them;

(3.2) Use the building coding program in the IDLE (Python GUI) module in the Python software to capture the building outline points within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to capture the building outline points Convert it into the outline of the building and give it building height attribute information;

#The latitude and longitude coordinates of the upper left corner (Mars coordinates) 117.250586,31.879621

zs_lon_lat='117.250586,31.879621'

zs_lon_deg=float(zs_lon_lat.split(',')[0])

zs_lat_deg=float(zs_lon_lat.split(',')[1])

#The latitude and longitude coordinates of the lower right corner (Mars coordinates) 117.312298,31.851338

yx_lon_lat='117.312298,31.851338'

yx_lon_deg=float(yx_lon_lat.split(',')[0])

yx_lat_deg=float(yx_lon_lat.split(',')[1])

# map zoom level

zoom=int(17)

li1=deg2num(zs_lat_deg,zs_lon_deg,zoom)

m=li1[0]

n=li1[1]

level=li1[2]

li2=deg2num(yx_lat_deg,yx_lon_deg,zoom)

m1=li2[0]

n1=li2[1]

X=m1-m

Y=n1–n

(3.3) Use the land use coding program in the IDLE (Python GUI) module of the Python software to capture the outer contour points of the land plot within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to convert the land use plot The outer contour points are converted into the outer contour line of the land plot, and endowed with land use property attribute information;

def deg2num(lat_deg, lon_deg, zoom):

lat_rad = math.radians(lat_deg)

n=2.0**zoom

tx=int((lon_deg+180.0)/360.0*n)

ty=int((1.0-math.log(math.tan(lat_rad)+(1/math.cos(lat_rad)))/math.pi)/2.0*n)

li=[]

li.append(tx)

li.append(ty)

li.append(zoom)

return li

def transformCell(tx,ty,zoom):

if tx>2**zoom-1:

tx=2**zoom-1

if ty>2**zoom-1:

ty=2**zoom-1

d=int(math.pow(2,int((zoom+1)/2)))

x=tx%d

y=ty%d

m=(tx-x)/d

n=(ty-y)/d

tile＝[tx-m*d+n*d,ty-n*d+m*d]

return tile

(4) Through the API code development method, SRTM DEM is used for data capture to obtain the elevation point and contour line data within the scope of Hangzhou City, and imported into CAD for vectorization to form an elevation-contour line database module, as shown in Figure 6 Show;

(4.1) Use the valley geographic information system platform to capture the elevation points and contour line data within the Hangzhou city area, and select the required accuracy and coordinate system, and export it into a csv format file;

(4.2) Import the csv format file into the Arcgis platform and convert it into a SHP format file;

(5) Import the above database modules into the geographic information system platform, and perform spatial processing such as translation, zooming, and rotation, and convert various database modules into a unified wgs84 coordinate system, and ensure complete spatial alignment; The data format is used to organize and manage all kinds of data, and the superimposed data is synthesized and processed. There is a unified block data processing unit between each data, and 3D modeling is performed based on the synthesized data to form a unified high-precision 3D architectural space in Hangzhou. data;

(5.1) Carry out comprehensive modeling of a single type of big data, import them into the geographic information system database respectively, and align the spatial positions of the four types of data modules in accordance with a unified data format, and convert various database modules into a unified wgs84 coordinate system , so that the value of each type of data can be spatially coupled with the value of other types of data;

(5.2) Perform multi-level data format conversion, and convert data types in different formats into unified or mutually convertible data formats.

(5.3) Unify the six types of data after alignment into the block surrounded by urban roads in Hangzhou as the basic statistical unit, that is, the basic unit of each block includes topography database module, traffic database module, building-land database module and The data information of the four modules of the elevation-contour database module.

(6) Carry out data mining analysis on the 3D modeling data, obtain comprehensive information on the spatial index characteristics of Hangzhou urban space big data, and analyze the 3D construction of Hangzhou city in real time, and then distribute and visualize it through the geographic information system.

(6.1) Statistics of building quantity data, building base area data, and total building area data within each land plot unit are calculated, and spatial indicators such as building density and volume ratio in each block are calculated.

(6.2) Visually display the database through the geographic information system, support the export of two-dimensional display image formats in DWG, JPEG, PDF, EPS, PNG, GIF, TIFF; support the export of three-dimensional display image formats in DWG, 3ds, skp, CityGML.

Claims (8)

1. the synthetical collection and analysis method of city three-dimensional building high-precision spatial big data, it is characterised in that：Including following step Suddenly：

(1) the high definition remote sensing image diagram data in region to be measured is acquired, establishes City Terrain relief data storehouse；

(2) road and all kinds of traffic site information in the region are extracted by internet city big data of increasing income, establishes city Traffic database；

(3) building and land used unit outline point in the regional extent are identified from dubai internet city Dubai map code big data, is built Vertical building-land used database；

(4) elevational point and contour line data in the data acquisition regional extent are carried out with SRTM DEM, establishes elevation-contour Line database；

(5) it was transformed into after above-mentioned database being entered into translation, scaling, revolution space processing in unified wgs84 coordinate systems, with system One shp data formats carry out data organization and management, synthesis, establish city three-dimensional building high-precision spatial big data；

(6) data mining analysis is carried out to three-dimensional modeling data, is distributed and is visualized by GIS-Geographic Information System.

2. collection and the analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its feature It is：Topography and geomorphology database module of establishing described in step (1) includes distinguishing inside using infrared remote sensing subrane technology Massif, water body and afforestation vegetation fundamental, while extracted carry out vectorized process.

3. the synthetical collection and analysis method of the city three-dimensional building high-precision spatial big data according to claim 1,2, It is characterized in that：The City Terrain relief data library module of foundation described in step (1) includes step in detail below：

(1.1) the high definition remote sensing image technical data in certain area is acquired, and platform is distinguished based on infrared ray wave band In the Eyedropper tool the massif in the high definition remote sensing image image in region, water body and afforestation vegetation key element color are inhaled Take, and then set out the color standard threshold value of massif, water body and afforestation vegetation key element, and meet mountain in the range of pickup area Image section in body, water body and afforestation vegetation key element threshold value, is set as single file format；

(1.2) massif, water body and afforestation vegetation factor data are directed respectively into VPstudio vector softwares, carry out A contour lines Vector quantization command operation, while the general order intensity that stretches is adjusted to " strong " grade, and to " layer " this threshold levels Head and the tail most strong grade is adjusted to, finally using V vector quantization orders, vectorized process is carried out to four class key elements respectively, forms conduct The gradient map of vector quantization；

(1.3) massif, water body and afforestation vegetation factor vector data are directed respectively into GIS-Geographic Information System, and distinguished Shp formatted files are stored as, wherein massif, water body key element are using the processing of multistage face is turned, by three class data conversions with turning into landform Looks database module three-dimensional modeling basic data.

4. the synthetical collection and analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its It is characterised by：Urban transportation database module of establishing described in step (2) specifically includes following steps：

(2.1) with URL coding methods access openstreet map backstages API ports, and frame is selected and step A same ranges Region, using " Overpass API " order carry out data download；

(2.2) data for obtaining step (2.1) import JSOM softwares, and using keyword search methodology to road and traffic station Space of points key element is scanned for searching, and it is individually exported and carries out Space Elements number as independent OSM formatted datas According to extraction and screening；

(2.3) OSM formatted datas are imported into GIS-Geographic Information System, and is translated into independent SHP formatted files.

5. the synthetical collection and analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its It is characterised by：Urban architecture-land used database module of establishing described in step (3) specifically includes following steps：

(3.1) latitude and longitude coordinates of the boundary node in the range of same area are picked up with high moral map reference pick tool Take, and checked and recorded；

(3.2) the building coded programs in IDLE (Python GUI) module in Python softwares are used to accurate longitude and latitude The building outline point in the range of boundary node under degree coordinate is captured, while uses txtToPolygon coded programs Building outline point is converted into building outer contour, and assigns its building height attribute information；

(3.3) the landuse coded programs in IDLE (Python GUI) module in Python softwares are used to accurate longitude and latitude The land used plot outline point in the range of boundary node under degree coordinate is captured, while with txtToPolygon coding journeys Land used plot outline point is converted into land used plot outer contour by sequence, and assigns its land character attribute information.

6. the synthetical collection and analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its It is characterised by：Described step (4), which establishes elevation-contour line data storehouse, to be included passing through API code development approach and vector quantization, is transported The elevational point in regional extent and contour line data are captured with valley floor GIS platform, and required for selection Precision and coordinate system, export to csv formatted files；Then csv formatted files are imported into Arcgis platforms, is converted into SHP forms File.

7. the synthetical collection and analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its It is characterised by：Step (5) establishes city three-dimensional building high-precision spatial big data and specifically includes following steps：

(5.1) comprehensive modeling is carried out to the big data of single type, gis database is directed respectively into, by four class data Module carries out the contraposition of locus according to unified data format, and Various types of data library module is converted into unified wgs84 coordinates System so that the numerical value per a kind of data can be with other categorical data Numerical Implementation Space Couplings；

(5.2) multi-level Data Format Transform is carried out, the data type of different-format is converted into unified or can mutually be turned The data format changed；

(5.3) it is six class data after contraposition are unified to using the block that urban road encloses as basic statistics unit, it is each Block elementary cell include shape relief data library module, traffic data library module, building-land used database module and elevation- The data message of contour line data library module four module.

8. the synthetical collection and analysis method of three-dimensional building high-precision spatial big data in city according to claim 1, its It is characterised by：Step (6) includes counting asd number data inside each land used Land unit, building area data, built Gross area data are built, and calculate the space index such as the site coverage in each block, plot ratio.