CN113159406A - Multi-source data-based town entity range identification and planning information processing method - Google Patents
Multi-source data-based town entity range identification and planning information processing method Download PDFInfo
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- CN113159406A CN113159406A CN202110398278.9A CN202110398278A CN113159406A CN 113159406 A CN113159406 A CN 113159406A CN 202110398278 A CN202110398278 A CN 202110398278A CN 113159406 A CN113159406 A CN 113159406A
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Abstract
The invention relates to a town entity range identification and planning information processing method based on multi-source data, which specifically comprises the following steps: s1, obtaining remote sensing image data in the target research area, and processing the remote sensing image data to obtain a land classification grid; s2, vectorizing according to the land classification grids, and extracting to obtain the actual built-up area boundary in the target research area; s3, obtaining land ownership elements in the target research area, and extracting and obtaining urban construction land property right boundaries in the target research area according to the land ownership elements; s4, acquiring a map spot for controlling the gauge in the target research area, and extracting to obtain a dynamic development boundary in the target research area according to the map spot for controlling the gauge; and S5, merging the actual built area boundary, the town construction land property right boundary and the dynamic development boundary to define the city entity boundary. Compared with the prior art, the method has the advantages of convenience, quickness, improvement of dynamic management and control of urban boundary development and the like.
Description
Technical Field
The invention relates to the technical field of homeland space planning, in particular to a town entity range identification and planning information processing method based on multi-source data.
Background
At present, in the territorial space planning, the problems caused by town spreading are generally recognized, and research and solution are attempted. However, the ecological space and agricultural space defined by the ecological protection red line and the permanent basic farmland protection red line are only used as rigid constraints to limit the scale and shape of the urban space, and the actual space of the current city and the dynamic changing area in the future are not further defined. Moreover, the ability to dynamically monitor and warn about changes in urban entity boundaries for a long period of time is lacking as a management and control boundary during the planning period. Therefore, the technology and application for building special urban entity boundary recognition based on the land use basic information platform are urgent.
As described above, without dealing with the possible problem of town spread due to fuzzy urban entity boundaries caused by technical methods and regulatory mechanisms, it is also necessary to propose a comprehensive planning method from the perspective of spatial analysis. In the multi-rule-in-one process, theoretical practice of rechecking and correcting local area line shapes and defining the envelope line of the construction land is already provided by combining the current situation of urban and rural construction land and referring to relevant data such as land utilization planning and examination and approval information and the like. However, the current situation boundary checked for the multi-rule conflict is more games only reflecting the planning right, and the concept definition and the range definition still mainly take the current situation of the construction space as the base, so that the consideration on the land ownership verification, the administrative resource allocation and the city development dynamics is lacked. Therefore, the boundary range of the urban entity is accurately defined by extracting and fusing the multi-dimensional space boundary, and the urban development monitoring is facilitated.
However, at the present stage, no such technical tools exist in the field of homeland space planning, and the definition of urban entity space as a technical process of related links is not paid special attention. The actual dynamics of the urban entity space development cannot be displayed, and the effective management and control of the space distribution state cannot be realized.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a town entity range identification and planning information processing method based on multi-source data, which integrates multi-source spatial information to accurately plan a city entity boundary and guide city management and control, city development boundary planning and administrative division adjustment.
The purpose of the invention can be realized by the following technical scheme:
a town entity range identification and planning information processing method based on multi-source data specifically comprises the following steps:
s1, obtaining remote sensing image data in a target research area, and processing the remote sensing image data to obtain a land classification grid;
s2, vectorizing according to the land classification grid, and extracting to obtain an actual built-up area boundary in the target research area;
s3, obtaining land ownership elements in the target research area, and extracting and obtaining the property right boundary of the urban construction land in the target research area according to the land ownership elements;
s4, acquiring a map spot for controlling the gauge in the target research area, and extracting to obtain a dynamic development boundary in the target research area according to the map spot for controlling the gauge;
and S5, merging the actual built area boundary, the town construction land property right boundary and the dynamic development boundary to define a city entity boundary.
And processing the remote sensing image data on a remote sensing image processing platform to obtain a land classification grid.
And inputting the land classification grids, the land ownership elements and the control and regulation land pattern spots into a geographic information system, thereby extracting and obtaining the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary.
The step S1 specifically includes the following steps:
s101, acquiring remote sensing image data subjected to radiometric calibration and atmospheric correction in a target research area and splicing;
s102, obtaining classified land vector data under the same coordinate with the remote sensing image data, and calculating separability of land classification;
s103, combining the land use classifications according to the separability calculation result of the land use classification, inputting the combined land use classification into a supervision classification-neural network for classification, and outputting various land use classification grids.
Further, the types of the right-of-way classification grids include construction right-of-way grids and non-construction right-of-way grids.
Further, in the step S101, the images are stitched by an image stitching tool of the remote sensing image processing platform.
The step S2 specifically includes the following steps:
s201, extracting a construction land grid from the land classification grid through attribute extraction;
s202, converting the construction land grid into vector surface data through grid conversion;
s203, acquiring a first pattern spot threshold value, and eliminating land pattern spots with the area smaller than the first pattern spot threshold value in the vector surface data according to the first pattern spot threshold value;
and S204, acquiring a construction land record of the target research area, and calculating the intersection of the construction land record and the eliminated vector plane data to obtain the actual built-up area boundary in the target research area.
Further, in step S201, the attribute is extracted by an attribute extraction tool in the geographic information system.
Further, the first speckle threshold is preferably 4 ha.
The step S3 specifically includes the following steps:
s301, obtaining land ownership elements in a target research area, and obtaining land ownership pattern spots according to the land ownership elements;
s302, combining the land ownership pattern spots, and splitting the combined land ownership pattern spots into a plurality of land ownership pattern spots corresponding to the land ownership elements;
s303, removing land use map spots with the area smaller than a second map spot threshold value from the land ownership map spots corresponding to the plurality of land ownership elements by an attribute extraction method;
s304, national land records of the target research area are obtained, and the intersection of the national land records and the removed land ownership pattern spots is calculated to obtain the property right boundary of the urban construction land.
Further, the second speckle threshold is preferably 4 ha.
Further, the attribute of the land ownership pattern spot is specifically a state land, and the corresponding property right boundary of the town construction land is specifically a state land property right boundary.
The step S4 specifically includes the following steps:
s401, obtaining control land pattern spots in a target research area, and combining the control land pattern spots to obtain corresponding control boundary;
s402, calculating the intersection of the control gauge boundary and the target research area to obtain a dynamic development boundary in the target research area.
Further, the calculation of the intersection set in the steps 204, 304 and 402 is performed by an intersection tool in the geographic information system.
The step S5 specifically includes the following steps:
s501, combining the layers of the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary to obtain a combined layer of the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary;
and S502, fusing the merged image layers into the city entity boundary.
Further, in step S502, after the union of the actual built-up area boundary and the urban construction land property right boundary is obtained through calculation, the intersection with the dynamic development boundary is calculated to obtain the urban entity boundary.
Further, in the steps S103, S302, and S501, merging is performed by a merging tool in the geographic information system, and in the step S502, merging layers is performed by a merging tool in the geographic information system.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can determine the urban entity boundary by synthesizing the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary, breaks through the limitation of urban area concept definition and fuzzy range determination, can conveniently generate various boundaries for defining urban entity space on the basis of the basic land information, and enables the operation of the homeland space planning to have more basis and logical support.
2. The invention uses a space analysis method to classify, screen, combine and determine the city entity space, and has higher objectivity and scientificity.
3. The invention adopts integral modular operation, controls the unit range according to different requirements and different spaces, does not need waste of a large amount of manpower and material resources, and reduces the cost for a user unit.
4. The invention can carry out the macroscopic integral analysis across regions and cities, can effectively reflect the correlation of urban groups, urban circles and administrative divisions on the spatial distribution, reveal the difference of various socioeconomic data statistical units and guide the adjustment of the administrative division range of villages and towns.
5. The invention can carry out cross-time longitudinal comparison to reflect the development scale, direction, speed and degree of the city and the dynamic change trend of the boundary of the urban space management unit, and can assist in the definition of the development boundary of the city.
6. The land use basic information platform is the necessary construction content for the current homeland space planning, so that the city entity boundary planning technology is the application direction of platform construction, can be used as the designated content of dynamic monitoring and early warning, and has a supporting effect on intelligent city management.
7. The calculation process of the invention is based on GIS and ENVY software environment, and various final sub-boundaries and synthetic boundaries are formed in a plug-in module form, so that the boundary synthesis efficiency is improved.
8. The invention has low maintenance cost, can be used for a long time, and can conveniently develop maintenance and upgrade when the software environment changes.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 1, a town entity scope identification and planning information processing method based on multi-source data specifically includes the following steps:
s1, obtaining remote sensing image data in the target research area, and processing the remote sensing image data to obtain a land classification grid;
s2, vectorizing according to the land classification grids, and extracting to obtain the actual built-up area boundary in the target research area;
s3, obtaining land ownership elements in the target research area, and extracting and obtaining urban construction land property right boundaries in the target research area according to the land ownership elements;
s4, acquiring a map spot for controlling the gauge in the target research area, and extracting to obtain a dynamic development boundary in the target research area according to the map spot for controlling the gauge;
and S5, merging the actual built area boundary, the town construction land property right boundary and the dynamic development boundary to define the city entity boundary.
The remote sensing image data is processed on a remote sensing image processing platform to obtain a land use classification grid, and in the embodiment, the remote sensing image processing platform is specifically ENVI 5.0.
The land classification grid, the land ownership elements and the control and regulation land pattern spots are input into a geographic information system, so that the actual construction area boundary, the urban construction land property right boundary and the dynamic development boundary are extracted, and in the embodiment, the geographic information system is specifically ArcGIS 10.2.
Step S1 specifically includes the following steps:
s101, acquiring remote sensing image data subjected to radiometric calibration and atmospheric correction in a target research area and splicing;
s102, obtaining classified land vector data under the same coordinate with the remote sensing image data, and calculating Separability (computer ROI Separability) of land classification;
s103, combining the land use classifications according to the separability calculation result of the land use classification, inputting the combined land use classification into a supervision classification-neural network for classification, and outputting various land use classification grids.
The types of the right-of-way classification grids include construction right-of-way grids and non-construction right-of-way grids.
In step S101, the images are spliced by an image splicing tool of the remote sensing image processing platform, and in this embodiment, the image splicing tool is specifically a Seamless mobile tool.
In step S102, separability is calculated by a separation tool of the remote sensing image processing platform.
Step S2 specifically includes the following steps:
s201, extracting a construction land grid from land classification grids through attribute extraction;
s202, converting the construction land grid into vector surface data through grid conversion;
s203, acquiring a first pattern spot threshold value, and eliminating land pattern spots with areas smaller than the first pattern spot threshold value in the vector plane data according to the first pattern spot threshold value;
and S204, acquiring the construction land record of the target research area, and calculating the intersection of the construction land record and the eliminated vector surface data to obtain the actual built area boundary in the target research area.
In step S201, attribute extraction is performed by an attribute extraction tool in the geographic information system.
In this embodiment, the first speckle threshold is preferably 4 ha.
Step S3 specifically includes the following steps:
s301, obtaining land ownership elements in the target research area, and obtaining land ownership pattern spots according to the land ownership elements;
s302, combining the land ownership pattern spots, and splitting the combined land ownership pattern spots into a plurality of land ownership pattern spots corresponding to the land ownership elements;
s303, removing land use map spots with the area smaller than a second map spot threshold value from the land ownership map spots corresponding to the plurality of land ownership elements by an attribute extraction method;
s304, national land records of the target research area are obtained, and the intersection of the national land records and the removed land ownership pattern spots is calculated to obtain the property right boundary of the urban construction land.
In this embodiment, the second speckle threshold is preferably 4 ha.
The property of the land ownership pattern spot is specifically the state-owned land, and the corresponding property right boundary of the town construction land is specifically the property right boundary of the state-owned land.
Step S4 specifically includes the following steps:
s401, obtaining control land pattern spots in a target research area, and combining the control land pattern spots to obtain corresponding control boundaries;
s402, calculating the intersection of the control gauge boundary and the target research area to obtain the dynamic development boundary in the target research area.
The calculation of the intersection set in steps 204, 304 and 402 is performed by an intersection tool in the geographic information system.
Step S5 specifically includes the following steps:
s501, combining the layers of the boundary of the actual built-up area, the property right boundary of the urban construction land and the dynamic development boundary to obtain a combined layer of the boundary of the actual built-up area, the property right boundary of the urban construction land and the dynamic development boundary;
and S502, fusing the merged layers into a city entity boundary.
In step S502, after the union of the actual built-up area boundary and the town construction land property right boundary is obtained by calculation, the intersection with the dynamic development boundary is calculated to obtain the city entity boundary.
Merging is performed through a merging tool in the geographic information system in steps S103, S302, and S501, and merging layers is performed through a merging tool in the geographic information system in step S502.
The application scenario of the present embodiment is as follows:
1) dynamic monitoring of changes in spatial extent of town entities by government authorities
By periodically inputting the remote sensing data, the land property data and the control and regulation pattern spot data, the change of the urban entity boundary can be updated and displayed in real time, the urban development behavior can be dynamically monitored, and processes such as planning evaluation and policy inspection can be assisted. And adjusting a land use policy based on the dynamic monitoring result, and guiding land utilization.
2) Planning and designing unit for defining town development boundary
By defining the entity boundary of the current city in the planning range, the result is helpful for defining the city development boundary and determining the future development direction of the city, anchoring the total development scale and the spatial structure, and assisting in forming the development strategy. The content can form the subcontent of the special topic of the urban development strategy in the homeland space planning and compiling, and propose a planning strategy.
3) Evaluating the use condition of the edge region of the town entity
The influence of the two processes on the land space pattern is evaluated, the determination of urban land and activity range is assisted, and a series of planning, managing and developing coping strategies can be formulated especially for the problems of fragmentation and disordering in the development of urban marginal areas.
4) Adjusting and optimizing township and street administrative division of urban area edge
By analyzing the relation between the urban entity area and the administrative divisions of the township streets, for townships which are connected with the main urban area in a large scale and have construction land occupying more than the township area 1/3, the township street removal can be considered so as to facilitate the consistency of social and economic data and spatial data statistical units.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. Minor or simple variations in the structure, features and principles of the present invention are included within the scope of the present invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A town entity range identification and planning information processing method based on multi-source data is characterized by comprising the following steps:
s1, obtaining remote sensing image data in a target research area, and processing the remote sensing image data to obtain a land classification grid;
s2, vectorizing according to the land classification grid, and extracting to obtain an actual built-up area boundary in the target research area;
s3, obtaining land ownership elements in the target research area, and extracting and obtaining the property right boundary of the urban construction land in the target research area according to the land ownership elements;
s4, acquiring a map spot for controlling the gauge in the target research area, and extracting to obtain a dynamic development boundary in the target research area according to the map spot for controlling the gauge;
and S5, merging the actual built area boundary, the town construction land property right boundary and the dynamic development boundary to define a city entity boundary.
2. The method for identifying and defining the town entity scope based on the multi-source data as claimed in claim 1, wherein the step S1 specifically comprises the following steps:
s101, acquiring remote sensing image data subjected to radiometric calibration and atmospheric correction in a target research area and splicing;
s102, obtaining classified land vector data under the same coordinate with the remote sensing image data, and calculating separability of land classification;
s103, combining the land use classifications according to the separability calculation result of the land use classification, inputting the combined land use classification into a supervision classification-neural network for classification, and outputting various land use classification grids.
3. The multi-source data-based town entity scope identification and delineation information processing method as claimed in claim 2, wherein the type of the land classification grid comprises construction land grid and non-construction land grid.
4. The method for identifying and defining the town entity scope based on the multi-source data as claimed in claim 3, wherein the step S2 specifically comprises the following steps:
s201, extracting a construction land grid from the land classification grid through attribute extraction;
s202, converting the construction land grid into vector surface data through grid conversion;
s203, acquiring a first pattern spot threshold value, and eliminating land pattern spots with the area smaller than the first pattern spot threshold value in the vector surface data according to the first pattern spot threshold value;
and S204, acquiring a construction land record of the target research area, and calculating the intersection of the construction land record and the eliminated vector plane data to obtain the actual built-up area boundary in the target research area.
5. The method for town entity scope identification and demarcation information based on multi-source data according to claim 4, wherein the first speckle threshold is preferably 4 ha.
6. The method for identifying and defining the town entity scope based on the multi-source data as claimed in claim 1, wherein the step S3 specifically comprises the following steps:
s301, obtaining land ownership elements in a target research area, and obtaining land ownership pattern spots according to the land ownership elements;
s302, fusing the land ownership pattern spots, and splitting the fused land ownership pattern spots into a plurality of land ownership pattern spots corresponding to the land ownership elements;
s303, removing land use map spots with the area smaller than a second map spot threshold value from the land ownership map spots corresponding to the plurality of land ownership elements by an attribute extraction method;
s304, national land records of the target research area are obtained, and the intersection of the national land records and the removed land ownership pattern spots is calculated to obtain the property right boundary of the urban construction land.
7. The method for town entity scope identification and demarcation information based on multi-source data according to claim 6, wherein the second speckle threshold is preferably 4 ha.
8. The method for identifying and defining the information of the town entity scope based on the multi-source data as claimed in claim 6, wherein the property of the land ownership pattern is a national land, and the property right boundary of the corresponding town construction land is a national land property right boundary.
9. The method for identifying and defining the town entity scope based on the multi-source data as claimed in claim 1, wherein the step S4 specifically comprises the following steps:
s401, obtaining control land pattern spots in a target research area, and combining the control land pattern spots to obtain corresponding control boundary;
s402, calculating the intersection of the control gauge boundary and the target research area to obtain a dynamic development boundary in the target research area.
10. The method for identifying and defining the town entity scope based on the multi-source data as claimed in claim 1, wherein the step S5 specifically comprises the following steps:
s501, combining the layers of the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary to obtain a combined layer of the actual built-up area boundary, the town construction land property right boundary and the dynamic development boundary;
and S502, fusing the merged image layers into the city entity boundary.
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