CN106021499B - Construction land classification method and device based on volunteer geographic information - Google Patents
Construction land classification method and device based on volunteer geographic information Download PDFInfo
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- CN106021499B CN106021499B CN201610342022.5A CN201610342022A CN106021499B CN 106021499 B CN106021499 B CN 106021499B CN 201610342022 A CN201610342022 A CN 201610342022A CN 106021499 B CN106021499 B CN 106021499B
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Abstract
The invention relates to a construction land classification method and a device based on volunteer geographic information, wherein the method comprises the following steps: determining the area of the construction land to be classified; acquiring geographic information of volunteers in the area; determining all roads in the area and trunk roads in all the roads; dividing the region according to the trunk road to obtain a plurality of first-level plots; dividing the area according to all roads to obtain a plurality of second-level plots; determining the level of each interest point in the area; determining the grade of the region to which the interest point belongs according to the grade of each interest point, and endowing the attribute of the interest point to the region of the grade of the region to which the interest point belongs; and determining the construction land types of the plots with the attributes given by the interest points according to the land classification standard to obtain a classification result. The invention can reduce the cost, improve the automation, shorten the period, improve the real-time performance and simultaneously improve the accuracy of the classification of the construction land.
Description
Technical Field
The invention relates to the technical field of land classification, in particular to a construction land classification method and device based on volunteer geographic information.
Background
In recent years, China is in the process of high-speed urbanization. Although the growth of the construction land is synchronous with the economic growth, the land used in cities and towns, rural residential sites and industrial and mining areas in China grow rapidly, and the urban and rural construction land is expanded in a two-way mode, which indicates that the construction land in China does not run on a road for saving the land used. The utilization efficiency of construction land is low, under equal demand, can lead to more other land types to change into construction land, and then can influence the arable land and keep the volume to reduce ecological land. The spatial layout of each type of construction land is one of the foundations for improving utilization efficiency and enhancing monitoring and control of planning implementation, and the construction land needs to be classified to realize the spatial layout of each type of construction land.
According to the classification standard of the current land utilization (GB/T21010-. At present, a mode of combining the traditional remote sensing technology and a geographic information system can be adopted, and the construction land is difficult to be subdivided in the mode, so a field investigation mode is generally adopted, and the method has the following defects: (1) the automation degree is low, a large amount of human resources, capital and time are consumed, and the cost is too high; (2) the period is long, and after one period is finished, the use of part of construction land is changed, namely the real-time performance is poor.
The Goodchild teaching proposed the geographical Information of volunteers (VGI for short) for the first time in 2007, and the Goodchild teaches that in the Web 2.0 era, spatial data is not only produced by experts and consumed by people, but also is a sensor for acquiring data, not only a data user, but also a data producer and a data propagator. In 2010, Liderren et al defined generalized volunteer geographic information as concepts, patterns, methods and techniques related to narrowly defined spontaneous geographic information. Therefore, crowdsourcing data and crowdsourcing platforms are more and more driving to scientific development and are further brought into the field of view of scientific researchers. In 2012, Sarah Elwood et al explored the application potential of volunteer geographic information in various fields, and in the same year, Rodrigues F. In 2013, the land utilization status Map of vienna is drawn by J.J.Arsanjani et al only by using an Open road Map (OSM for short), and official data is used for checking, so that the accuracy can reach over 65%. In the same year, the research area of the people is functionally divided by the type and the number of the new wave microblog attendance point data by the people like the Longying. Although this study has many improvements over traditional construction-site classification methods, there are still some problems: (1) the used volunteer geographic information data volume is small, and the accuracy is not high in a city with high construction land complexity; (2) the different actual feature footprints represented by different types of point data are not considered, and the importance degree of the point data is different in the process of type determination, so that the accuracy is not high.
Disclosure of Invention
In view of the above defects, the invention provides a construction land classification method and device based on volunteer geographic information, which can reduce cost, improve automation, shorten period, improve real-time performance, and improve accuracy of construction land classification.
In a first aspect, the construction land classification method based on volunteer geographic information provided by the invention comprises the following steps:
determining the region of the construction land to be classified according to the remote sensing image data;
acquiring geographical information of the volunteers in the area, wherein the geographical information of the volunteers comprises all road data and all interest point data in the area;
determining all roads in the area and trunk roads in all the roads according to the road data;
dividing the region according to the trunk road to obtain a plurality of first-level plots;
dividing the area according to all the roads to obtain a plurality of plots of a second level;
determining the level of each interest point in the area according to the interest point data;
determining the grade of the region to which each interest point belongs according to the grade of the interest point, and endowing the attribute of the interest point to the region of the grade of the region to which the interest point belongs;
and determining the construction land types of the plots with the attributes given by the interest points according to the land classification standard to obtain a classification result.
Optionally, the levels of the interest points include a first level, a second level and a third level;
the first-level interest points comprise a plurality of buildings, and the number of the internal roads is more than the preset number;
the second level of interest points are interest points which comprise a plurality of buildings and the number of internal roads is less than the preset number;
the third level of interest points are interest points that occupy an entire building.
Optionally, the determining, according to the level of each interest point, the parcel level to which the interest point belongs includes:
the level of the interest point is a first level, and the level of the region block to which the interest point belongs is determined to be the first level; or
The level of the interest point is a second level or a third level, and the grade of the region block to which the interest point belongs is determined to be the second level.
Optionally, the levels of the interest points further include a fourth level, where the interest points of the fourth level share one building with other interest points or are single-layer buildings whose floor area is smaller than a preset area; the method further comprises the following steps:
and if the fourth level of interest points are judged to exist in Atlas market indoor map data, removing the fourth level of interest points.
Optionally, before determining the level of each point of interest in the area according to the point of interest data, the method further includes:
and removing the other types of land used except the construction land in the area according to all the interest point data.
Optionally, before determining the level of each point of interest in the area according to the point of interest data, the method further includes:
and constructing a corresponding regular expression according to the character characteristics of each attribute in the interest point data, and setting matched attribute information for each interest point according to the regular expression.
Optionally, the volunteer geographic information further includes sign-in data of a social platform and/or a ground feature picture with a geographic location; the method further comprises the following steps:
and verifying the classification result according to the check-in data of the social platform and/or the ground feature picture with the geographic position, and determining the classification precision of the construction land.
Optionally, before the area is divided according to the trunk road to obtain a plurality of first-level plots, the method further includes: and (4) extending each road by a preset length.
In a second aspect, the present invention provides a construction site classification device based on volunteer geographic information, comprising:
according to the construction land classification method and device based on the geographic information of the volunteers, two types of blocking are performed when the region is blocked, a plurality of first-level blocks with larger occupied areas are obtained by using trunk road blocking, and a plurality of second-level blocks with smaller occupied areas are obtained by using all road blocking. And then determining the level of the proper assigned land parcel according to the level of the interest points so as to avoid separating one interest point or not separating a plurality of interest points, thereby improving the accuracy of the classification of the construction land. The classification method provided by the invention does not need manual field investigation, so that the method has the advantages of low cost, high automation degree, short period and high real-time performance. Meanwhile, the land parcels are classified and the interest points are classified, then the interest points of different levels classify the land parcels of different levels, namely the determined levels of the land parcels belong to different interest points, and the accuracy of classification of the construction land parcels is improved by considering that the actual land parcels represented by the interest points of different levels have different occupied areas.
Drawings
The characteristic information and advantages of the invention will be more clearly understood by reference to the accompanying drawings, which are schematic and should not be understood as imposing any limitation on the invention, in which:
FIG. 1 is a flow chart illustrating a construction site classification method based on volunteer geographic information according to an embodiment of the present invention;
fig. 2 is a block diagram illustrating a construction site classification apparatus based on volunteer geographic information according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
As shown in fig. 1, the present invention provides a construction site classification method based on volunteer geographic information, the method comprising:
s101, determining the region of the construction land to be classified according to the remote sensing image data;
it can be understood that the remote sensing image data is analyzed to determine the regions to be classified.
S102, obtaining geographical information of the volunteers in the area, wherein the geographical information of the volunteers comprises all road data and all interest point data in the area;
wherein, the geographic information of the volunteers can be obtained on the Internet, and the information is open and free. So-called point of interest data is POI (Point of interest) related data. The road data can be acquired in OSM (open Street map), and the acquired road data is large in quantity, high in accuracy and high in updating speed.
S103, determining all roads in the area and trunk roads in all the roads according to the road data;
s104, dividing the region according to the trunk road to obtain a plurality of first-level plots;
it can be understood that the area of the land parcel of the first level obtained by dividing the region by the trunk road is generally large.
S105, dividing the area according to all the roads to obtain a plurality of plots of a second level;
it can be understood that the area of the second level land is generally smaller by dividing the area by using all roads.
S106, determining the level of each interest point in the area according to the interest point data;
s107, determining the grade of the region to which the interest point belongs according to the grade of each interest point, and endowing the attribute of the interest point to the region of the grade of the region to which the interest point belongs;
here, the parcel level is the first level and the second level described above. Due to the difference of the interest point levels, the assigned land parcel levels are different.
And S108, determining the construction land types of the plots endowed with the attributes by the interest points according to the land classification standard to obtain a classification result.
The land classification standard can be a current land utilization classification standard used currently, and the construction land can be classified into a business land, an industrial and mining storage land, a residential land, a public management and public service land, a transportation land or other construction land types according to the classification standard.
For example, a certain interest point includes a plurality of buildings and the number of internal roads is greater than a preset number, such an interest point generally occupies a large area and even occupies the entire block, such as an institution, an airport, etc., and is defined as a first-level interest point, and since such an interest point includes more roads, when performing assignment, the attribute of the interest point should be assigned to the first-level block to which the interest point belongs, so as to avoid that the interest point is segmented and the construction land classification is wrong.
For another example, a point of interest is a point of interest that includes a plurality of buildings but has a number of internal roads less than the preset number, and such a point of interest that includes a plurality of buildings but has less roads and is more concentrated in various buildings, such as residential districts, industrial parks, and the like, is defined as a second level point of interest. Because the number of the interest points is small, a trunk road can have a plurality of the interest points, and therefore when the interest points are assigned, the attributes of the interest points are assigned to the plots of the second level to which the interest points belong, so that the situation that the construction land is wrongly classified due to the fact that the interest points are not divided is avoided.
For another example, a certain interest point occupies an interest point of a whole building, such as a large company, a star hotel, a library, etc., and since such an interest point has no road, the attribute of the interest point should be assigned to the second-level parcel to which the interest point belongs when the value is assigned.
In the construction land classification method provided by the invention, two types of blocks are carried out when the region is blocked, a plurality of first-level blocks with larger occupied area are obtained by using the trunk road blocks, and a plurality of second-level blocks with smaller occupied area are obtained by using all the road blocks. And then determining the level of the proper assigned land parcel according to the level of the interest points so as to avoid separating one interest point or not separating a plurality of interest points, thereby improving the accuracy of the classification of the construction land. The classification method provided by the invention does not need manual field investigation, so that the method has the advantages of low cost, high automation degree, short period and high real-time performance. Meanwhile, the land parcels are classified and the interest points are classified, then the interest points of different levels classify the land parcels of different levels, namely the determined levels of the land parcels belong to different interest points, and the accuracy of classification of the construction land parcels is improved by considering that the actual land parcels represented by the interest points of different levels have different occupied areas.
Of course, a level of interest points is also used, and the interest points share a building or a single-layer building with a floor area smaller than a preset area with other interest points, such as a restaurant, a shopping mall, and the like, and are defined as the interest points of the fourth level. Wherein, the floor area is less than the single-storey building of the predetermined area, for example, the area is less than 30 square meters of the flat-roofed house. The processing method for such points of interest may include:
and if the fourth level of interest points are judged to exist in Atlas market indoor map data, removing the fourth level of interest points.
The Atlas market indoor map provides a market map updated by developers in real time, and the market map comprises data of Shanghai, Beijing, Guangzhou and Chengdu (market and shop data of more than 400 places, and is matched with group buying, movie tickets and comment data of various shops at the same time.
Here, if the interest points existing in the Atlas mall indoor map data are assigned and classified, the classification will overflow, and therefore the interest points are deleted, and the classification accuracy is further improved. Of course, if the interest point does not exist in the Atlas market indoor map data, the interest point is regarded as a single feature, and at this time, the grade of the parcel can be determined according to the floor space size of the interest point, and then the value is assigned, so that the classification of the parcel to which the interest point belongs is completed.
In specific implementation, since the interest points are from the electronic map, the method has the advantages of detailed classification and wide types, and in order to further ensure the classification accuracy, the method can further comprise the following steps of: and removing the other types of land used except the construction land in the area according to all the interest point data, thereby avoiding the other types of land used except the construction land from causing interference on classification.
In specific implementation, data inspection shows that there are situations where the attribute information of a small number of interest points does not correspond, and then the following method may be adopted to set the matched attribute information for each interest point: and constructing a corresponding regular expression according to the character characteristics of each attribute in the interest point data, and setting matched attribute information for each interest point according to the regular expression.
Here, by constructing the regular expression and then setting the matched attribute information, i.e., the corresponding text and numerical information, for each interest point according to the regular expression, the accuracy of the subsequent assignment is improved, and the classification accuracy is further improved.
In specific implementation, because the problem that roads are not connected due to operation errors of OSM volunteers exists, each road can be extended by a preset length before the area where the road is located is divided according to the trunk road. Of course, it is also possible to trim the parts that have not yet been connected to other roads after the extension.
In specific implementation, step S103 may further include setting buffers with different widths according to different levels of the road, that is, widening different widths of different types of roads, so as to improve the accuracy of determining the interest point level.
In particular implementation, the volunteer geographic information further comprises sign-in data of the social platform and/or a surface feature picture with a geographic location; the method may further comprise:
and verifying the classification result according to the check-in data of the social platform and/or the ground feature picture with the geographic position, and determining the classification precision of the construction land.
Wherein, the ground feature picture with the geographic position can be acquired from Panoramio platform. Sign-in data of the social platform, such as sign-in data of a Sing microblog.
Here, the precision of the classification is determined by means of verification, so that whether the classification method is reliable or not is known. Through verification, the classification accuracy is over 90%.
Based on the same inventive concept, the present invention also provides a construction site classifying device based on volunteer geographic information, as shown in fig. 2, the device 200 includes:
the first determining module 201 is used for determining the region of the construction land to be classified according to the remote sensing image data;
an obtaining module 202, configured to obtain geographic information of a volunteer in the located area, where the geographic information of the volunteer includes all road data and all interest point data in the located area;
a second determining module 203, configured to determine all roads in the area and trunk roads in all roads according to the road data;
the first dividing module 204 is configured to divide the area according to the trunk road to obtain a plurality of first-level land parcels;
the second dividing module 205 is configured to divide the area according to all the roads to obtain a plurality of plots at a second level;
a third determining module 206, configured to determine, according to the point of interest data, a level of each point of interest in the area where the point of interest is located;
the assignment module 207 is configured to determine, according to the level of each interest point, a parcel level to which the interest point belongs, and assign an attribute of the interest point to a parcel of the parcel level;
and a fourth determining module 208, configured to determine the construction land types of the plots to which the attributes are assigned according to the land classification criteria, so as to obtain a classification result.
Optionally, the levels of the interest points include a first level, a second level and a third level;
the first-level interest points comprise a plurality of buildings, and the number of the internal roads is more than the preset number;
the second level of interest points are interest points which comprise a plurality of buildings and the number of internal roads is less than the preset number;
the third level of interest points are interest points that occupy an entire building.
In the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The term "plurality" means two or more unless expressly limited otherwise.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A construction land classification method based on volunteer geographic information is characterized by comprising the following steps:
determining the region of the construction land to be classified according to the remote sensing image data;
acquiring geographical information of the volunteers in the area, wherein the geographical information of the volunteers comprises all road data and all interest point data in the area;
determining all roads in the area and trunk roads in all the roads according to the road data;
dividing the region according to the trunk road to obtain a plurality of first-level plots;
dividing the area according to all the roads to obtain a plurality of plots of a second level;
determining the level of each interest point in the area according to the interest point data;
determining the grade of the region to which each interest point belongs according to the grade of the interest point, and endowing the attribute of the interest point to the region of the grade of the region to which the interest point belongs;
determining the construction land types of plots endowed with attributes by the interest points according to land classification standards to obtain classification results;
the parcel level is the first level or the second level.
2. The method of claim 1, wherein the levels of interest include a first level, a second level, and a third level;
the first-level interest points comprise a plurality of buildings, and the number of the internal roads is more than the preset number;
the second level of interest points are interest points which comprise a plurality of buildings and the number of internal roads is less than the preset number;
the third level of interest points are interest points that occupy an entire building.
3. The method of claim 2, wherein determining the parcel level to which each point of interest belongs according to the level of the point of interest comprises:
the level of the interest point is a first level, and the level of the region block to which the interest point belongs is determined to be the first level; or
The level of the interest point is a second level or a third level, and the grade of the region block to which the interest point belongs is determined to be the second level.
4. The method of claim 2, wherein the levels of interest points further include a fourth level, and the interest points of the fourth level are single-story buildings that share a building or occupy a floor area smaller than a preset area with other interest points; the method further comprises the following steps:
and if the fourth level of interest points are judged to exist in Atlas market indoor map data, removing the fourth level of interest points.
5. The method of claim 1, wherein before determining the level of each point of interest in the area based on the point of interest data, the method further comprises:
and removing the other types of land used except the construction land in the area according to all the interest point data.
6. The method of claim 1, wherein before determining the level of each point of interest in the area based on the point of interest data, the method further comprises:
and constructing a corresponding regular expression according to the character characteristics of each attribute in the interest point data, and setting matched attribute information for each interest point according to the regular expression.
7. The method of claim 1, wherein the volunteer geographic information further comprises check-in data of a social platform and/or a geo-feature picture with a geographic location; the method further comprises the following steps:
and verifying the classification result according to the check-in data of the social platform and/or the ground feature picture with the geographic position, and determining the classification precision of the construction land.
8. The method of claim 1, wherein before the dividing the area according to the trunk road to obtain a plurality of first-level plots, the method further comprises: and (4) extending each road by a preset length.
9. The utility model provides a construction land classification device based on volunteer geographic information which characterized in that includes:
the first determination module is used for determining the region of the construction land to be classified according to the remote sensing image data;
the obtaining module is used for obtaining the geographical information of the volunteers in the area, and the geographical information of the volunteers comprises all road data and all interest point data in the area;
the second determining module is used for determining all roads in the area and trunk roads in all the roads according to the road data;
the first dividing module is used for dividing the region according to the trunk road to obtain a plurality of first-level plots;
the second division module is used for dividing the area according to all the roads to obtain a plurality of second-level plots;
the third determining module is used for determining the level of each interest point in the area according to the interest point data;
the assignment module is used for determining the grade of the place to which the interest point belongs according to the grade of each interest point and endowing the attribute of the interest point to the place of the grade of the place to which the interest point belongs;
the fourth determining module is used for determining the construction land types of the plots endowed with the attributes by the interest points according to the land classification standard to obtain a classification result;
the parcel level is the first level or the second level.
10. The apparatus of claim 9, wherein the levels of interest comprise a first level, a second level, and a third level;
the first-level interest points comprise a plurality of buildings, and the number of the internal roads is more than the preset number;
the second level of interest points are interest points which comprise a plurality of buildings and the number of internal roads is less than the preset number;
the third level of interest points are interest points that occupy an entire building.
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| CN106778547B (en) * | 2016-11-29 | 2020-06-30 | 中南大学 | Method and device for constructing remote sensing image ground feature classification reference library |
| CN107133900B (en) * | 2017-04-05 | 2021-07-27 | 中国农业大学 | Urban land mixed utilization feature grid computing method and device |
| CN107247938B (en) * | 2017-06-08 | 2019-12-06 | 中国科学院遥感与数字地球研究所 | high-resolution remote sensing image urban building function classification method |
| CN112069273A (en) * | 2020-08-13 | 2020-12-11 | 丰图科技(深圳)有限公司 | Address text classification method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102521598A (en) * | 2011-12-16 | 2012-06-27 | 北京市城市规划设计研究院 | Identification method based on remote-sensing image |
| CN104834666A (en) * | 2015-03-06 | 2015-08-12 | 中山大学 | Acoustic environment functional area partitioning method based on road network and interest points |
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| US9147132B2 (en) * | 2013-09-11 | 2015-09-29 | Digitalglobe, Inc. | Classification of land based on analysis of remotely-sensed earth images |
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| CN102521598A (en) * | 2011-12-16 | 2012-06-27 | 北京市城市规划设计研究院 | Identification method based on remote-sensing image |
| CN104834666A (en) * | 2015-03-06 | 2015-08-12 | 中山大学 | Acoustic environment functional area partitioning method based on road network and interest points |
Non-Patent Citations (1)
| Title |
|---|
| 基于城市公交刷卡数据和兴趣点的城市功能区识别研究——以北京市为例;于翔;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150315(第2期);第3.2节、4.2节、5.2节 * |
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