CN107679229B - Comprehensive acquisition and analysis method for high-precision spatial big data of urban three-dimensional building - Google Patents
Comprehensive acquisition and analysis method for high-precision spatial big data of urban three-dimensional building Download PDFInfo
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Abstract
The invention discloses a comprehensive acquisition and analysis method of high-precision spatial big data of an urban three-dimensional building, which comprises the following steps: (1) establishing a basic landform database; (2) establishing an urban traffic database; (3) establishing an urban building-land database; (4) constructing an elevation-contour database; (5) establishing a city three-dimensional model to form high-precision space big data of a city three-dimensional building; (6) and carrying out data mining analysis and visual display on the three-dimensional modeling data. The invention can process massive space form data, quickly and efficiently acquire high-precision space big data of the urban three-dimensional building and measure spatial structure elements, realizes comprehensive acquisition and information synthesis of urban space analysis basic data based on an artificial intelligence system, and is beneficial to comprehensive, normative and efficient operation of urban planning and design.
Description
Technical Field
The invention relates to urban space big data mining, collecting and analyzing, in particular to a comprehensive collecting and analyzing method for urban three-dimensional building high-precision space big data.
Background
The high-precision space big data of the city with the three-dimensional building as the minimum unit is always the core foundation of space planning and even city planning, but the database has the characteristics of difficult acquisition, long period, small samples and the like. Aiming at the problem, the method for acquiring the large urban space database by using the high-new informatization and intelligentization multisource approach has very important significance for researching urban space form evolution, urban internal development mechanisms and the like. Compared with the function of distinguishing various urban elements through infrared bands of a traditional remote sensing satellite map, the urban three-dimensional building high-precision space large database based on the image technology and data mining has the characteristics of full samples, positioning, visualization and real-time monitoring. More importantly, the large database can comprehensively and comprehensively express and analyze information of each space system and each space unit of the city from the surface layer to the deep layer and from the real layer to the virtual layer.
In the research of image technology and data mining, various data elements are judged by different types of data sources to be an important part, and the establishment of a high-precision large space database of an urban three-dimensional building is concerned with important aspects such as professional information identification, data extraction, dynamic change prediction, comprehensive map making and the like. The database can meet the requirements of increasingly huge urban space big data on urban planning and design, auxiliary decision making and urban management, and is one of the difficulties faced by urban planning.
At present, the research at home and abroad on the acquisition, analysis and visualization of single data of the urban three-dimensional building high-precision space big database mainly focuses on, the mutual influence relation among all data systems of the urban space is less focused on, and the manufacturing and dynamic display technology of the three-dimensional building high-precision space big database covering the urban space from the fine level to the three-dimensional building level is lacked. The method is lack of urban geospatial coordinates in the acquisition of urban three-dimensional building high-precision space big databases, stays in single-dimensional data processing, and falls behind in the field of urban space big data acquisition, integration and display of large-scale composite data.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides a comprehensive acquisition and analysis method for high-precision spatial big data of an urban three-dimensional building.
The technical scheme is as follows: the comprehensive acquisition and analysis method of the high-precision spatial big data of the urban three-dimensional building comprises the following steps:
(1) collecting high-definition remote sensing image data in a region to be measured, and establishing an urban landform database;
(2) extracting road and various traffic station information in the area through Internet open source city big data, and establishing an urban traffic database;
(3) identifying the outer contour points of the buildings and land units in the area range from the big data of the Internet city map codes, and establishing a building-land database;
(4) using an SRTM DEM to perform data acquisition on elevation points and contour line data in the area range, and establishing an elevation-contour line database;
(5) the database is subjected to translation, scaling and rotation space processing and then converted into a unified wgs84 coordinate system, data organization management and synthesis are carried out in a unified shp data format, and high-precision space big data of the urban three-dimensional building are established;
(6) and carrying out data mining analysis on the three-dimensional modeling data, and carrying out distribution and visual display through a geographic information system.
The step (1) of establishing the landform and landform database module comprises the steps of distinguishing the internal mountain, water and greening vegetation basic elements by adopting an infrared remote sensing waveband division technology, and simultaneously extracting the basic elements for vectorization treatment.
Further, the urban topography and landform database module established in the step (1) comprises the following specific steps:
(1.1) acquiring technical data of high-definition remote sensing images in a certain area, absorbing colors of mountain bodies, water bodies and greening vegetation elements in the high-definition remote sensing image in the area based on a straw tool in an infrared band distinguishing platform, further setting color standard thresholds of the mountain bodies, the water bodies and the greening vegetation elements, acquiring image parts in the area range, which meet the threshold values of the mountain bodies, the water bodies and the greening vegetation elements, and setting the image parts to be in an independent file format;
(1.2) respectively importing mountain, water and greening vegetation element data into VPstudio vectorization software, carrying out A contour line vectorization command operation, simultaneously adjusting the strength of universal straightening commands to be a 'strong' grade, adjusting a 'layer' threshold grade to be a head-tail strongest grade, and finally respectively carrying out vectorization treatment on four types of elements by adopting a V vectorization command to form a basic base map used as vectorization;
and (1.3) respectively importing the vectorized data of the mountain, the water body and the greening vegetation elements into a geographic information system, and respectively storing the vectorized data into shp format files, wherein the mountain and the water body elements are processed by converting multiple sections, and the three types of data are converted into three-dimensional modeling basic data of a landform and landform database module.
Further, the establishing of the urban traffic database module in the step (2) specifically comprises the following steps:
(2.1) accessing an Openstreet map background API port by using a URL coding method, framing out an area in the same range as that in the step A, and downloading data by using an 'Overpass API' command;
(2.2) importing the data obtained in the step (2.1) into JSM software, searching and searching spatial elements of roads and traffic stations by adopting a keyword search method, and respectively and independently exporting the spatial elements to independent OSM format data for extracting and screening the spatial element data;
and (2.3) importing the OSM format data into a geographic information system and converting the OSM format data into an independent SHP format file.
Further, the establishing of the urban building-land database module in the step (3) specifically comprises the following steps:
(3.1) picking up longitude and latitude coordinates of boundary nodes in the same area range by using a Gade map coordinate picking tool, and checking and recording;
(3.2) grabbing building outer contour points in a boundary node range under accurate longitude and latitude coordinates by using a building coding program in an IDLE (Python GUI) module in Python software, and simultaneously converting the building outer contour points into building outer contour lines by using a txttopoly coding program and giving building height attribute information to the building outer contour lines;
(3.3) using land use coding program in IDLE (Python GUI) module in Python software to capture land-used plot outline points in the boundary node range under accurate longitude and latitude coordinates, simultaneously using txtToPolygon coding program to convert the land-used plot outline points into land-used plot outline lines and endowing the land-used plot outline lines with land property attribute information;
further, the step (4) of establishing the elevation-contour database comprises capturing elevation points and contour line data in an area range by using a valley geographical information system platform through an API (application programming interface) code development method and vectorization, selecting required precision and a coordinate system, and exporting the data into a csv format file; and then importing the csv format file into an Arcgis platform, and converting the csv format file into an SHP format file.
Further, the step (5) of establishing the high-precision spatial big data of the urban three-dimensional building specifically comprises the following steps:
(5.1) carrying out comprehensive modeling on single type of big data, respectively introducing the big data into a geographic information system database, carrying out spatial position alignment on four types of data modules according to a uniform data format, and converting each type of database module into a uniform wgs84 coordinate system so that the numerical value of each type of data can be spatially coupled with the numerical values of other types of data;
and (5.2) carrying out multi-level data format conversion, and converting the data types in different formats into a uniform or interconvertible data format.
And (5.3) unifying the six types of data after alignment to a block formed by enclosing urban roads as a basic statistical unit, wherein each block basic unit comprises data information of four modules, namely a shape and landform database module, a traffic database module, a building-land database module and an elevation-contour line database module.
Further, the step (6) includes the steps of counting the number data of buildings, the data of the base floor area of the buildings and the data of the total area of the buildings in the land block units of each land, and calculating the space indexes such as the density and the volume rate of the buildings in each block.
Has the advantages that: compared with the prior art, the invention has the remarkable effects that: 1. the method is based on image analysis and big data mining, can deal with the processing of mass data, and can rapidly and comprehensively collect the high-precision spatial big data of the urban three-dimensional building in real time; 2. the seamless connection collection of high-precision spatial big data of the urban three-dimensional building based on an urban coordinate system and the simulated dynamic display of spatial features are realized by superposing urban spatial data of various types and systems under the same digital map system; 3. the method comprises the steps that a landform database module, a traffic database module, a building-land database module and an elevation-contour database module are overlapped under the same digital map system, so that the urban space big data collection and comprehensive analysis display based on an urban coordinate system are realized; 4. the multiple image layers are divided into corresponding urban space big data elements through different ways and types, classification management and selection operation are facilitated, the display range can be quickly selected by setting the region selection function, and therefore manual repeated labor is reduced, data input and output are facilitated, and images are quickly analyzed and exported; 5. the invention carries out multi-interface seamless combination on each element data of the urban space form, realizes the rapid acquisition and visual query display of mass data, and provides data access for government functional departments, the fields of building design and urban planning; 6. the high-precision space big data elements of the urban three-dimensional building can be displayed by selecting query, required data and images are dynamically displayed on a computer, and a decision scheme is further provided for the improvement of each system mechanism and the optimization of the space form of the city.
Drawings
FIG. 1 is a diagram of a comprehensive acquisition method of high-precision spatial big data of an urban three-dimensional building based on image analysis and big data mining;
FIG. 2 is a diagram of the urban region space range of Hangzhou city according to the present invention;
FIG. 3 is a block diagram of a Hangzhou city landform database according to the present invention;
FIG. 4 is a block diagram of a Hangzhou city traffic database according to the present invention;
FIG. 5 is a block diagram of a Hangzhou city building-land database according to the present invention;
FIG. 6 is a block diagram of a Hangzhou city elevation-contour database according to the present invention.
Detailed Description
For the purpose of illustrating the technical solutions disclosed in the present invention in detail, the following description is further provided with reference to the accompanying drawings and specific embodiments.
The data acquisition process of the invention is shown in figure 1 by combining a comprehensive acquisition and analysis method of urban three-dimensional building high-precision space big data in China Hangzhou city domain range (total area 16596 square kilometers, constantly living population 918.8 ten thousands of people, urbanization rate 76.2%), and comprises the following specific operation steps:
(1) collecting high-definition remote sensing image data in Hangzhou city region range, adopting infrared remote sensing sub-band technology to distinguish mountain, water and greening vegetation basic elements, simultaneously extracting them and carrying out vectorization treatment to form a landform database module, as shown in figure 3;
(1.1) acquiring high-definition remote sensing image technical data in the Hangzhou city domain range, absorbing colors of mountain bodies, water bodies and greening vegetation element colors in a high-definition remote sensing image in the Hangzhou city domain range based on a suction pipe tool in an infrared band distinguishing platform, further setting color standard thresholds of the mountain bodies, the water bodies and the greening vegetation element colors, acquiring image parts in the Hangzhou city domain range, wherein the image parts meet the mountain body, water bodies and greening vegetation element thresholds, and setting the image parts to be in an independent file format;
(1.2) respectively importing mountain, water and greening vegetation element data into VPstudio vectorization software, carrying out A contour line vectorization command operation, simultaneously adjusting the strength of universal straightening commands to be a 'strong' grade, adjusting a 'layer' threshold grade to be a head-tail strongest grade, and finally respectively carrying out vectorization treatment on four types of elements by adopting a V vectorization command to form a basic base map used as vectorization;
and (1.3) respectively importing the vectorized data of the mountain, the water body and the greening vegetation elements into a geographic information system, and respectively storing the vectorized data into shp format files, wherein the mountain and the water body elements are subjected to a multi-section-surface conversion technical processing method, so that the three types of data can be converted into three-dimensional modeling basic data of a landform database module.
(2) Extracting roads and various traffic stations in the Hangzhou city domain range by using a URL (uniform resource locator) coding method and an Internet open source city big data capturing method, and storing the roads and various traffic stations as independent files to form a traffic database module, as shown in figure 4;
(2.1) accessing an Openstreet map background API port by using a URL coding method, framing out an area in the same range as that in the step A, and downloading data by using an 'Overpass API' command;
(2.2) importing the data downloaded in the step (2.1) into JSON (Java Server object model) software, searching and searching spatial elements of roads and traffic stations by adopting a keyword search method, and respectively and independently exporting the spatial elements to independent OSM (open service management) format data to extract and screen the spatial element data;
and (2.3) importing the OSM format data into a geographic information system and converting the OSM format data into an independent SHP format file.
(3) Identifying the outer contour points of the buildings and the land units in the Hangzhou city domain range by using a Java language code method and an Internet city map code big data method, and spatially integrating the outer contour points into a multi-segment surface of the buildings and the land to form a building-land database module, as shown in figure 5;
(3.1) picking up longitude and latitude coordinates of boundary nodes in the same Hangzhou city domain range by using a Gade map coordinate picking tool, and checking and recording;
(3.2) grabbing building outer contour points in a boundary node range under accurate longitude and latitude coordinates by using a building coding program in an IDLE (Python GUI) module in Python software, and simultaneously converting the building outer contour points into building outer contour lines by using a txttopoly coding program and giving building height attribute information to the building outer contour lines;
# upper left longitude and latitude coordinates (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])
Bottom right corner longitude and latitude coordinates (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) using land use coding program in IDLE (Python GUI) module in Python software to capture land-used plot outline points in the boundary node range under accurate longitude and latitude coordinates, simultaneously using txtToPolygon coding program to convert the land-used plot outline points into land-used plot outline lines and endowing the land-used plot outline lines with land 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 an API code development method, data capture is carried out by using an SRTM DEM to obtain elevation points and contour line data in the Hangzhou city domain range, and the data are led into a CAD for vectorization to form an elevation-contour line database module, as shown in FIG. 6;
(4.1) capturing elevation points and contour line data in the Hangzhou urban area range by using a valley geographic information system platform, selecting required precision and a coordinate system, and exporting to a csv format file;
(4.2) importing the csv format file into an Arcgis platform, and converting the csv format file into an SHP format file;
(5) importing the database module into a geographic information system platform, carrying out spatial processing such as translation, scaling and rotation, converting various database modules into a uniform wgs84 coordinate system, and ensuring complete spatial alignment; meanwhile, various data organization management is carried out in a unified data format, the superposed data are subjected to synthesis processing, a unified block data processing unit is arranged among the data, and three-dimensional modeling is carried out according to the synthesized data to form unified Hangzhou city three-dimensional building high-precision space big data;
(5.1) carrying out comprehensive modeling on single type of big data, respectively introducing the big data into a geographic information system database, carrying out spatial position alignment on four types of data modules according to a uniform data format, and converting each type of database module into a uniform wgs84 coordinate system so that the numerical value of each type of data can be spatially coupled with the numerical values of other types of data;
and (5.2) carrying out multi-level data format conversion, and converting the data types in different formats into a uniform or interconvertible data format.
And (5.3) unifying the six types of data after alignment to a block formed by enclosing Hangzhou city roads as a basic statistical unit, namely, each block basic unit comprises data information of four modules, namely a shape and landform database module, a traffic database module, a building-land database module and an elevation-contour line database module.
(6) And carrying out data mining analysis on the three-dimensional modeling data to obtain comprehensive information of space index characteristics of the Hangzhou city large data space, analyzing the three-dimensional construction condition of the Hangzhou city in real time, and further carrying out distribution and visual display through a geographic information system.
And (6.1) counting the number data, the base area data and the total area data of the buildings in the land block units of each land, and calculating the space indexes such as the building density, the volume rate and the like in each block.
(6.2) visually displaying the database through a geographic information system, and supporting the derivation of two-dimensional display image formats such as DWG, JPEG, PDF, EPS, PNG, GIF and TIFF; the formats supporting the derivation of three-dimensional display images are DWG, 3ds, skp and CityGML.
Claims (7)
1. The comprehensive acquisition and analysis method of the high-precision spatial big data of the urban three-dimensional building is characterized by comprising the following steps of: the method comprises the following steps:
(1) collecting high-definition remote sensing image data in a region to be measured, and establishing an urban landform database;
(2) extracting road and various traffic station information in the area through Internet open source city big data, and establishing an urban traffic database;
(3) identifying the outer contour points of the buildings and land units in the area range from the big data of the Internet city map codes, and establishing a building-land database;
(4) using an SRTM DEM to perform data acquisition on elevation points and contour line data in the area range, and establishing an elevation-contour line database;
(5) the database is converted into a unified wgs84 coordinate system after being subjected to translation, scaling and rotation space processing, data organization management and synthesis are carried out in a unified shp data format, and high-precision space big data of the urban three-dimensional building are established, and the method specifically comprises the following steps:
(5.1) carrying out comprehensive modeling on single type of big data, respectively introducing the big data into a geographic information system database, carrying out spatial position alignment on four types of data modules according to a uniform data format, and converting each type of database module into a uniform wgs84 coordinate system so that the numerical value of each type of data can be spatially coupled with the numerical values of other types of data;
(5.2) carrying out multi-level data format conversion, and converting the data types in different formats into a unified or interconvertible data format;
(5.3) unifying the six types of data after alignment to a block formed by enclosing urban roads as a basic statistical unit, wherein each block basic unit comprises data information of four modules, namely a shape and landform database module, a traffic database module, a building-land database module and an elevation-contour line database module;
(6) and carrying out data mining analysis on the three-dimensional modeling data, and carrying out distribution and visual display through a geographic information system.
2. The method for comprehensively acquiring and analyzing high-precision spatial big data of urban three-dimensional buildings according to claim 1, wherein the method comprises the following steps: the step (1) of establishing the landform and landform database module comprises the steps of distinguishing the internal mountain, water and greening vegetation basic elements by adopting an infrared remote sensing sub-band technology, and simultaneously extracting the basic elements for vectorization treatment.
3. The comprehensive acquisition and analysis method for high-precision spatial big data of urban three-dimensional buildings according to claim 1 or 2, characterized in that: the urban landform database module established in the step (1) comprises the following specific steps:
(1.1) acquiring technical data of high-definition remote sensing images in a certain area, absorbing colors of mountain bodies, water bodies and greening vegetation elements in the high-definition remote sensing image in the area based on a straw tool in an infrared band distinguishing platform, further setting color standard thresholds of the mountain bodies, the water bodies and the greening vegetation elements, acquiring image parts in the area range, which meet the threshold values of the mountain bodies, the water bodies and the greening vegetation elements, and setting the image parts to be in an independent file format;
(1.2) respectively importing mountain, water and greening vegetation element data into VPstudio vectorization software, carrying out A contour line vectorization command operation, simultaneously adjusting the strength of universal straightening commands to be a 'strong' grade, adjusting a 'layer' threshold grade to be a head-tail strongest grade, and finally respectively carrying out vectorization treatment on four types of elements by adopting a V vectorization command to form a basic base map used as vectorization;
and (1.3) respectively importing the vectorized data of the mountain, the water body and the greening vegetation elements into a geographic information system, and respectively storing the vectorized data into shp format files, wherein the mountain and the water body elements are processed by converting multiple sections, and the three types of data are converted into three-dimensional modeling basic data of a landform and landform database module.
4. The method for comprehensively acquiring and analyzing high-precision spatial big data of urban three-dimensional buildings according to claim 1, wherein the method comprises the following steps: the establishing of the urban traffic database module in the step (2) specifically comprises the following steps:
(2.1) accessing an Openstreet map background API port by using a URL coding method, framing out an area in the same range as that in the step A, and downloading data by using an 'Overpass API' command;
(2.2) importing the data obtained in the step (2.1) into JSM software, searching and searching spatial elements of roads and traffic stations by adopting a keyword search method, and respectively and independently exporting the spatial elements to independent OSM format data for extracting and screening the spatial element data;
and (2.3) importing the OSM format data into a geographic information system and converting the OSM format data into an independent SHP format file.
5. The method for comprehensively acquiring and analyzing high-precision spatial big data of urban three-dimensional buildings according to claim 1, wherein the method comprises the following steps: the step (3) of establishing the urban building-land database module specifically comprises the following steps:
(3.1) picking up longitude and latitude coordinates of boundary nodes in the same area range by using a Gade map coordinate picking tool, and checking and recording;
(3.2) grabbing building outer contour points in a boundary node range under accurate longitude and latitude coordinates by using a building coding program in an IDLE (Python GUI) module in Python software, and simultaneously converting the building outer contour points into building outer contour lines by using a txttopoly coding program and giving building height attribute information to the building outer contour lines;
(3.3) using land use coding program in IDLE (Python GUI) module in Python software to capture land use outline points in the boundary node range under accurate longitude and latitude coordinates, and simultaneously using txtToPolygon coding program to convert the land use outline points into land use outline lines and endowing the land use outline lines with land use property attribute information.
6. The method for comprehensively acquiring and analyzing high-precision spatial big data of urban three-dimensional buildings according to claim 1, wherein the method comprises the following steps: establishing an elevation-contour database in the step (4) comprises capturing elevation points and contour line data in an area range by using a valley geographical information system platform through an API code development method and vectorization, selecting required precision and a coordinate system, and exporting the precision and the coordinate system to a csv format file; and then importing the csv format file into an Arcgis platform, and converting the csv format file into an SHP format file.
7. The method for comprehensively acquiring and analyzing high-precision spatial big data of urban three-dimensional buildings according to claim 1, wherein the method comprises the following steps: and (6) counting the number data, the base area data and the total area data of the buildings in the land block units of each land, and calculating the density and the volume rate of the buildings in each block.
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Application publication date: 20180209 Assignee: SHENZHEN GENERAL INSTITUTE OF ARCHITECTURAL DESIGN AND RESEARCH Co.,Ltd. Assignor: SOUTHEAST University Contract record no.: X2022320000042 Denomination of invention: Comprehensive collection and analysis method of high-precision spatial big data of urban three-dimensional buildings Granted publication date: 20210601 License type: Common License Record date: 20220414 |