CN115272854A - Palm area identification method and product based on multi-source information analysis - Google Patents
Palm area identification method and product based on multi-source information analysis Download PDFInfo
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Abstract
The embodiment of the invention provides a palm area identification method and a product based on multi-source information analysis. By adopting the brown land identification method provided by the embodiment of the invention, the identification target area (the area where the potential brown land is located) can be determined based on POI data in the open source map service interface, then the potential brown land in the remote sensing image of the target area is visually interpreted based on the spatial characteristics of the identification target area, and the spatial boundaries and the distribution of brown land categories such as raw material mining categories, tailing pond categories, raw material processing and manufacturing categories, non-raw material processing and manufacturing categories, infrastructure categories, refuse landfill and the like are obtained, and further the non-idle land is removed by utilizing multi-source spatial and non-spatial data to correct the identification result, so that the final brown land identification result of the target area is obtained. Therefore, the brown land identification method provided by the embodiment of the invention can obtain batch brown land identification results of the target area based on remote sensing images and open source POI data of the large-range target area.
Description
Technical Field
The embodiment of the invention relates to the technical field of information processing, in particular to a palm area identification method and a palm area identification product based on multi-source information analysis.
Background
Brown generally refers to a site with known or potential contamination due to human activities, and its reuse needs to be based on site risk assessment and remediation based on the intended use. Brown land and its regeneration are global issues that require global serious treatment due to its enormous number and widespread distribution throughout the world.
In the related art, scholars at home and abroad have conducted several studies on the brown land recognition method, and the conventional brown land recognition technology can be divided into 3 main types. The method is high in efficiency (automatic identification), but the identification result is greatly influenced by image data, open source data cannot be obtained, and the method has no universality and popularization value. And secondly, a brown zone artificial identification method based on non-open source information is low in efficiency, and key information has no public obtaining way and has no universality and popularization value. And thirdly, a brown land manual identification method based on open source information, and although the identification technical process can be copied and has a certain popularization value, the identified brown land type is limited. In addition, the above 3-type recognition method has a single approach to determining the site abandonment state, and is mostly investigated in the field, which increases the investment of time and labor cost.
In addition, because the types of the brown lands are more and the remote sensing image characteristics with uniform identification degree are not available, the remote sensing technology is used for automatically identifying a large number of types of brown lands in a large range in the field of landscape and garden discipline at present, and the method is difficult to realize. Compared with the identification of urban land cover by using high-resolution remote sensing images, the research of carrying out fine identification on urban land use distribution by using remote sensing supervision and sub-supervision classification technology is less.
Different from the land cover, the land utilization refers to the use mode of people in the aspect of social and economic functions, and is difficult to directly extract from the remote sensing image data. Regarding the brown land, because the types are various and have high heterogeneity, the method does not have uniform spectral features, geometric features and textural features with identification degree, and when the remote sensing images with the resolution of 30m in the Landsat series are used for identifying the brown land, the method is easily confused with bare land, sand, roads and other construction lands, and the effect is not good. Therefore, the method for identifying the vegetation, the water body, the urban buildings, the roads and other ground features by utilizing supervised and unsupervised classification based on the Landsat series free remote sensing images is not suitable for brown land identification.
Therefore, a new brown land identification method is needed.
Disclosure of Invention
Embodiments of the present invention provide a brown land identification method, apparatus, electronic device, computer-readable storage medium, and computer program product based on multi-source information analysis, so as to solve at least some problems in the related art.
The first aspect of the embodiment of the invention provides a brown land identification method based on multi-source information analysis, which comprises the following steps:
determining a potential brown region in a remote sensing image of a target region according to map data of the target region and POI data of the target region;
according to the spatial features contained in the remote sensing image of the target area, potential brown areas are determined from the areas where the potential brown areas are located, the potential brown areas are classified, and a multi-class potential brown area distribution diagram is obtained;
correcting the multi-class potential brown land distribution map according to multi-source spatial and non-spatial data to obtain identification results of brown lands of multiple classes, wherein the multi-source spatial and non-spatial data at least comprise: a multi-time sequence people flow thermodynamic diagram;
wherein the various categories of brown land include: the method comprises the following steps of refuse landfill, raw material mining similar-brown land, tailing pond similar-brown land, raw material processing and manufacturing similar-brown land, non-raw material processing and manufacturing similar-brown land and infrastructure similar-brown land.
Optionally, determining a region where a potential brown spot is located in the remote sensing image of the target region according to the map data of the target region and the POI data of the target region, including:
acquiring map POI data of a target area, and selecting industrial POI data related to industry from the map POI data;
the method comprises the steps of obtaining mining area POI data of a target area, and screening idle mining POI data from the mining area POI data;
acquiring map POI data of a target area, and selecting landfill POI data related to landfill from the map POI data;
and determining the region where the potential brown areas are located according to the map data of the target region, the industrial POI data, the mining POI data and the landfill POI data.
Optionally, selecting industry-class POI data related to industry from the map POI data includes:
acquiring map POI data of a target area;
selecting POI data of a category related to industrial brown fields from the map POI data of the target area as first industrial POI data;
screening keywords of data except the first industrial POI data in the map POI data to obtain supplementary industrial POI data;
and obtaining industrial POI data according to the first industrial POI data and the supplementary industrial POI data.
Optionally, screening the mining POI data for idle mining POI data, including:
screening idle mining POI data from the mining area POI data according to the attribute of each mining area in the mining area POI data of the target area;
the attributes of the respective mining areas at least include: the current situation is utilized.
Optionally, selecting, from the map POI data, landfill-like POI data related to landfill, including:
acquiring map POI data of a target area;
selecting POI data of a category relevant to landfill from the map POI data of the target area as first landfill POI data;
performing keyword screening on data except the first landfill POI data in the map POI data to obtain supplementary landfill POI data;
and obtaining landfill POI data according to the first landfill POI data and the supplementary landfill POI data.
Optionally, determining a potential brown zone from the zone where the potential brown zone is located according to spatial features included in the remote sensing image of the target zone, and classifying the potential brown zone to obtain a multi-class potential brown zone distribution map, including:
obtaining the category and the space boundary of each potential brown zone in the region where the potential brown zone is located according to the space boundary, facilities, hue, elevation, area and texture of the remote sensing image of the target region;
and identifying in the remote sensing image of the target area according to the category and the space boundary of each potential brown area to obtain a multi-category potential brown area distribution map.
Optionally, the multi-source spatial and non-spatial data further comprises: the method comprises the following steps of correcting the multi-class potential brown land distribution map according to multi-source space and non-space data by using a city land current situation map, a city land planning map and Shi Zhi data, wherein the steps comprise:
according to the urban land present map, the urban land planning map, the Shi Zhi data and the multi-time sequence people flow thermodynamic map, removing land blocks still in operation in the multi-class potential brown land distribution map, and supplementing the missed brown lands identified in the multi-class potential brown land distribution map.
A second aspect of the embodiments of the present invention provides a brown land identification apparatus based on multi-source information analysis, where the apparatus includes:
the determining module is used for determining a region where a potential brown region is located in a remote sensing image of a target region according to map data of the target region and POI data of the target region;
the classification module is used for determining potential brown areas from the areas where the potential brown areas are located according to the spatial features contained in the remote sensing images of the target areas, and classifying the potential brown areas to obtain multi-class potential brown area distribution maps;
the correction module is used for correcting the multi-class potential brown land distribution map according to multi-source spatial and non-spatial data to obtain identification results of brown lands of multiple classes, and the multi-source spatial and non-spatial data at least comprise: a multi-sequence people flow thermodynamic diagram;
wherein the various categories of brown land include: the method comprises the following steps of refuse landfill, raw material mining similar-brown land, tailing pond similar-brown land, raw material processing and manufacturing similar-brown land, non-raw material processing and manufacturing similar-brown land and infrastructure similar-brown land.
Optionally, the determining module includes:
the first obtaining sub-module is used for obtaining map POI data of a target area and selecting industrial POI data related to industry from the map POI data;
the second acquisition sub-module is used for acquiring mining area POI data of the target area and screening idle mining POI data from the mining area POI data;
the third obtaining sub-module is used for obtaining map POI data of a target area of the target area and selecting landfill POI data related to landfill from the map POI data;
and the determining sub-module is used for determining a potential brown region according to the map data of the target region, the industrial POI data, the mining POI data and the landfill POI data.
Optionally, the first obtaining sub-module is specifically configured to:
acquiring map POI data of a target area;
selecting POI data of a category related to industrial brown-like areas from the map POI data of the target area as first industrial POI data;
performing keyword screening on data except the first industrial POI data in the map POI data to obtain supplementary industrial POI data;
and obtaining industrial POI data according to the first industrial POI data and the supplementary industrial POI data.
Optionally, the second obtaining sub-module is specifically configured to:
screening idle mining POI data from the mining area POI data according to the attribute of each mining area in the mining area POI data of the target area;
the attributes of the respective mining areas at least include: the current situation is utilized.
Optionally, the third obtaining sub-module is specifically configured to:
acquiring map POI data of a target area;
selecting POI data of a category related to landfill from the map POI data of the target area as first landfill POI data;
performing keyword screening on data except the first landfill POI data in the map POI data to obtain supplementary landfill POI data;
and obtaining the landfill POI data according to the first landfill POI data and the supplementary landfill POI data.
Optionally, the classification module is specifically configured to:
obtaining the category and the spatial boundary of each potential brown zone in the zone where the potential brown zones are located according to the spatial boundary, facilities, hue, elevation, area and texture of the remote sensing image of the target zone;
and identifying in the remote sensing image of the target area according to the category and the space boundary of each potential brown land to obtain a multi-category potential brown land distribution map.
Optionally, the multi-source spatial and non-spatial data further includes: the correction module is specifically used for the following data of the urban land current situation map, the urban land planning map and Shi Zhi:
according to the urban land present map, the urban land planning map, the Shi Zhi data and the multi-time sequence people flow thermodynamic map, eliminating land blocks still in operation in the multi-class potential brown land distribution map, and supplementing missed brown lands identified in the multi-class potential brown land distribution map.
A third aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps in the method for identifying brown areas based on multi-source information analysis according to the first aspect of the present invention.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the brown land identification method based on multi-source information analysis according to the first aspect of the present invention.
A fifth aspect of embodiments of the present invention provides a computer program product, which includes a computer program/instruction, when executed by a processor, for implementing steps in the brown identification method based on multi-source information analysis according to the first aspect of the present invention.
By adopting the palm area identification method based on multi-source information analysis provided by the embodiment of the invention, the identification target area (the area where the potential palm area is located) can be determined based on POI data in the open source map service interface, then the potential palm area in the remote sensing image of the target area is visually interpreted based on the spatial characteristics of the identification target area, and the distribution of palm area categories such as raw material mining categories, tailing pond categories, raw material processing and manufacturing categories, non-raw material processing and manufacturing categories, infrastructure categories, landfill sites and the like is obtained, and further the non-idle areas are removed based on multi-source space and non-spatial data to correct the identification result, so that the final palm area identification result of the target area is obtained. Therefore, the brown land identification method provided by the embodiment of the invention can obtain batch brown land identification results of the target area based on remote sensing images and open source POI data of the large-range target area.
In the embodiment of the invention, the target area is really identified by utilizing the open source data POI, and then the brown land is identified and classified, so that the identification efficiency can be improved; in addition, in the embodiment of the invention, the land parcel correction is carried out by using multi-source spatial and non-spatial data, so that the field investigation is avoided, and the time and labor cost can be saved; in addition, in the embodiment of the invention, six types of brown lands can be identified in batches from a large-scale target area, so that the method is favorable for carrying out regional brown land research and practice in a more macroscopic and integral visual angle.
In the brown zone identification method provided by the embodiment of the invention, the used data (including POI data of the target area, map data of the target area, remote sensing images of the target area and multi-source space and non-space data) are open source data, the acquisition way is simple, the universality is high, and the method is easy to popularize (for example, the method can be applied to brown zone identification of any city capable of acquiring the data).
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of a brown land identification method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a potential brown zone area obtained by a specific embodiment of the brown zone identification method according to the embodiment of the present invention;
FIG. 3 is a multi-class potential brown zone distribution diagram obtained by a specific embodiment of the brown zone identification method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of identification results of various types of brown lands obtained by a specific embodiment of the brown land identification method according to the embodiment of the present invention;
fig. 5 is a block diagram of another brown land identification apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, a flowchart of a brown land identification method based on multi-source information analysis according to an embodiment of the present invention is shown, and specifically, the brown land identification method provided by the embodiment of the present invention may include the following steps:
s101, determining a potential brown region in the remote sensing image of the target region according to the map data of the target region and the POI data of the target region.
The POI data refers to a Point of Interest (Point of Interest), and each POI data at least contains four-aspect information: name, category, longitude, latitude.
In the embodiment of the present invention, the map data of the target area may be derived from open source map service interfaces such as a high-grade map, a Baidu map, and a Google Earth, and the POI data of the target area may be derived from POI data of open source map service interfaces such as a high-grade map and a Baidu map, a national mining area database, and other related databases. Based on the map data and POI data of the target area, specific coordinates of potential brown areas can be obtained, and therefore the areas where the potential brown areas are located can be identified in the remote sensing image.
In the embodiment of the present invention, the target region refers to a research region identified in brown, for example: a province, a city, a district, etc.
Specifically, the step S101 may include the following sub-steps:
and S1011, acquiring map POI data of the target area, and selecting industrial POI data related to industry from the map POI data.
In the embodiment of the invention, the industrial POI data can be determined by using the map POI data of the target area, and the map POI data of the target area can be derived from the POI data in open source map service interfaces such as a Gauss map, a Baidu map and the like.
After map POI data of a target area are obtained, due to the complex data type, the embodiment of the invention firstly needs to clean the data to obtain the required target POI data, and specifically comprises the following substeps:
s10111, map POI data of the target area are obtained.
In the embodiment of the invention, the multi-source map POI data can be fused (for example, the Gauder map POI data and the Baidu map POI data are fused) to obtain the map POI data of the target area.
S10112, selecting POI data of a category related to the industrial brown-like region from the map POI data of the target region as first industrial POI data.
S10113, performing keyword screening on data except the first industrial POI data in the map POI data to obtain supplementary industrial POI data.
S10114, obtaining industrial POI data according to the first industrial POI data and the supplementary industrial POI data.
In the embodiment of the present invention, from the map POI data (data description includes POI name, category, address, longitude and latitude, name of located province, name of located city, name of located region, located region code, etc.), data of 5 subclasses of factory, metallurgical chemical industry, building company, mechanical and electronic company in the large class of company and enterprise with the highest brown correlation with the industrial class, and data of the subclass of industrial park in the business residence may be selected as the first industrial POI data, and then the rest of the map POI data may be screened with keywords (for example, "coal", "iron", "lime", "factory", "process", "manufacture", etc.) to obtain the supplementary industrial POI data. And finally, merging the first industrial POI data and the supplementary industrial POI data to obtain industrial POI data.
S1012, mining area POI data of the target area are obtained, and idle mining POI data are screened from the mining area POI data.
In an embodiment of the present invention, the POI data of the mineral area of the target region may be derived from a national database of mineral areas. And determining idle mining POI data according to the data description corresponding to each mining area in the national mining area database.
The method specifically comprises the following steps: screening idle mining POI data from the mining area POI data according to the attribute of each mining area in the mining area POI data of the target area; the attributes of the respective mining areas at least comprise: the current situation is utilized.
In the embodiment of the invention, each piece of stored data in the related database has corresponding data description (such as mining area, longitude, latitude, mining type, scale, current utilization situation, deposit cause class, geological work schedule, traffic position and the like), so that the idle mining POI data can be determined according to the attribute marking (such as undeveloped, developing, idle and the like according to the current utilization situation of each mining area in the national mining area database).
And S1013, acquiring map POI data of the target area, and selecting landfill POI data related to landfill from the map POI data.
In the embodiment of the invention, map POI data of the target area can be used for determining the POI data of the refuse landfill, and the POI data of the target area can be derived from POI data in open source map service interfaces such as a Gauss map, a Baidu map and the like.
After map POI data of a target area is acquired, due to the complex data types, the embodiment of the invention firstly needs to clean the data to acquire the required target POI data, and specifically comprises the following substeps:
s10131, map POI data of the target area is acquired.
In the embodiment of the invention, the multi-source map POI data can be fused (for example, the Gauder map POI data and the Baidu map POI data are fused) to obtain the map POI data of the target area.
S10132, selecting the POI data of the category related to landfill from the map POI data of the target area as the first landfill POI data.
And S10133, performing keyword screening on the data except the first landfill POI data in the map POI data to obtain supplementary landfill POI data.
And S10134, obtaining landfill POI data according to the first landfill POI data and the supplementary landfill POI data.
In the embodiment of the invention, data such as a landfill, a sanitary landfill, a landfill and the like related to refuse landfill can be selected from the map POI data (data description comprises POI name, classification, address, longitude and latitude, province name, city name, region code and the like) to be used as first landfill POI data, and then the rest map POI data is screened by keywords (such as 'landfill', 'sanitary', 'heap' and the like) to obtain supplementary landfill POI data. And finally, merging the first landfill POI data and the supplementary landfill POI data to obtain the landfill POI data.
S1014, determining the potential brown region according to the map data of the target region, the industrial POI data, the mining POI data and the landfill POI data.
In the embodiment of the invention, after the industrial POI data, the idle mining POI data and the refuse landfill POI data are determined, the specific coordinates of the data can be determined according to the data description corresponding to the data, and the area where the potential brown areas are located is identified in the remote sensing image.
In the embodiment of the invention, most POI data space coordinate systems are GCJ-02 coordinate systems (also called Mars coordinate systems) and have certain space deviation, so that space deviation correction is required to be carried out on the POI data space coordinate systems, and finally, the potential brown region is identified in the remote sensing image according to specific coordinates after the space deviation correction.
In the embodiment of the present invention, when determining the potential brown zone, some other database data may also be referred to, for example: shut-down/treatment conditions of mines and tailings ponds, mineral distribution, important brown land projects and the like.
S102, according to the spatial features contained in the remote sensing image of the target area, potential brown areas are determined from the areas where the potential brown areas are located, the potential brown areas are classified, and a multi-class potential brown area distribution diagram is obtained.
In consideration of the difference of the spatial features corresponding to different types of brown areas, the embodiment of the invention provides that potential brown areas are classified based on the spatial features contained in the remote sensing image of the target area. In the embodiment of the invention, the remote sensing image of the target area can be visually interpreted based on manpower, and the multi-class potential brown land distribution diagram is obtained. And the machine learning model can be used for learning the spatial features of various types of brown lands for automatic classification.
In the embodiment of the invention, the remote sensing image of the target area is a high-definition satellite image and can be derived from Google earth satellite images.
In an alternative embodiment, the S102 includes the following sub-steps:
and S1021, obtaining the category and the space boundary of each potential brown zone in the region where the potential brown zone is located according to the space boundary, facilities, hue, elevation, area and texture of the remote sensing image of the target region.
And S1022, identifying in the remote sensing image of the target area according to the category and the space boundary of each potential brown zone to obtain a multi-category potential brown zone distribution map.
In the embodiment of the invention, the spatial characteristics of various types of brown lands are predetermined as follows:
1) Refuse landfill: the space boundary is clear; a leachate and biogas guide and discharge treatment facility is arranged in the field; the whole body is in an irregular or regular mountain shape, the top is relatively flat usually, and the two sides are in a multistage slope shape with the slope controlled.
2) Digging brown-like land with raw materials: the working surface is clear, the boundary is irregular, and the earth surface is damaged to different degrees; the stack body or the pit is provided with digging machines, large supports, conveying belts and other facilities; the elevation change is large, or the pit shape is concave or the step shape is formed at the waist; the raw material mining fields with different resources have different colors and areas; the road in the field is rugged.
3) Tailings pond brown field: have a more regular, clear boundary; the elevation change is large, or the elevation is convex or the elevation changes are in a table shape at the waist of a mountain, and the elevation changes more and more along with the migration of time; the area is usually large; there is basically no road in the field.
4) Processing raw materials to manufacture brown-like land: the space boundary is clear, and obvious enclosing walls are generally generated; there are constructions with obvious industrial characteristics in the field; the inside of the field is generally flat; the area is large, and the building groups are often tightly connected together through pipelines, conveyor belts, railways and the like; the traffic in the field is more regular, and a railway is arranged in the field or the field is directly connected with the railway.
5) Non-raw material processing to manufacture brown-like land: the space boundary is clear, and obvious enclosing walls are generally generated; a large area of blue or red color steel sheds or solar panels are arranged in the field; the inside of the field is generally flat; a group with a large number of buildings is formed, and the area of the group is larger than that of the residential buildings; and (5) regulating the underground road.
6) Infrastructure brown-like land: the space boundary is clearer; the inside of the field is provided with a marking structure such as a rail; the field is generally relatively flat.
Based on the six types of spatial features of the brown lands, the embodiments of the present invention can perform visual interpretation according to the spatial boundary, facility, hue, elevation, area, texture of the remote sensing image of the target area, and analyze and refine the distribution and spatial boundary of the subdivided brown land types such as raw material mining, tailing pond, raw material processing and manufacturing, non-raw material processing and manufacturing, infrastructure, landfill, and the like.
S103, correcting the multi-category potential brown land distribution map according to multi-source space and non-space data to obtain identification results of brown lands of multiple categories, wherein the multi-source space and non-space data at least comprise: a multi-sequence people flow thermodynamic diagram.
After obtaining the multi-class potential brown region distribution map, the multi-class potential brown region distribution map also needs to be corrected. The embodiment of the invention provides that the data are corrected based on multi-source space and non-space data (such as people flow thermodynamic diagram data and city planning diagram data) so as to eliminate the still-running land blocks and supplement the missing brown lands.
In an optional embodiment, the non-spatial multi-source data further comprises: the current city land map, the planning map of the city land, and the Shi Zhi data, in this case, the step S103 specifically includes:
according to the urban land present map, the urban land planning map, the Shi Zhi data and the multi-time sequence people flow thermodynamic map, removing land blocks still in operation in the multi-class potential brown land distribution map, and supplementing the missed brown lands identified in the multi-class potential brown land distribution map.
For convenience of understanding, the brown land identification method based on multi-source information analysis provided by the embodiment of the present invention is further explained by a specific embodiment, and it is understood that this embodiment is only used as an example:
1. determining a target volume using POI data: firstly, preprocessing work such as cleaning, space correction and the like is carried out on industrial, mining and landfill POI data of the Pingxiang city to obtain a POI distribution map of the Pingxiang city, as shown in fig. 2.
2. Identifying the brown land: on the basis of the POI determined space identification target area, the refuse landfill potential brown land, the raw material mining class potential brown land, the tailing pond class potential brown land, the raw material and non-raw material processing class potential brown land and the infrastructure class potential brown land which are positioned on the earth surface are identified through established visual interpretation rules, and a multi-class potential brown land distribution map is obtained, as shown in figure 3.
3. Land parcel correction: on the basis of obtaining the multi-class potential brown land distribution diagram, the field which is still in operation is removed on the basis of the current situation diagram of the Pingxiang city land, the land planning diagram, the multi-time-sequence Baidu people flow thermodynamic diagram and the Shi Zhi data, and the missed brown land is identified and supplemented. Thereby obtaining a distribution map of the corrected brown land of the land mass as shown in fig. 4.
Based on the same inventive concept, an embodiment of the present invention provides a palm area identification device based on multi-source information analysis, and referring to fig. 5, fig. 5 is a schematic diagram of the palm area identification device provided in the embodiment of the present invention. As shown in fig. 5, the apparatus includes:
the determining module 501 is configured to determine, according to map data of a target area and POI data of the target area, an area where a potential brown spot is located in a remote sensing image of the target area;
the classification module 502 is configured to determine potential brown areas from the areas where the potential brown areas are located according to spatial features included in the remote sensing images of the target areas, and classify the potential brown areas to obtain multi-class potential brown area distribution maps;
a correcting module 503, configured to correct the multi-class potential brown land distribution map according to multi-source spatial and non-spatial data, so as to obtain identification results of brown lands of multiple classes, where the multi-source spatial and non-spatial data at least includes: a multi-sequence people flow thermodynamic diagram;
wherein the various categories of brown land include: the method comprises the steps of refuse landfill, raw material mining similar brown land, tailing pond similar brown land, raw material processing and manufacturing similar brown land, non-raw material processing and manufacturing similar brown land and infrastructure similar brown land.
Optionally, the determining module 501 includes:
the first obtaining sub-module is used for obtaining map POI data of a target area and selecting industrial POI data related to industry from the map POI data;
the second acquisition sub-module is used for acquiring mining area POI data of the target area and screening idle mining POI data from the mining area POI data;
the third acquisition sub-module is used for acquiring map POI data of a target area and selecting landfill POI data related to landfill from the map POI data;
and the determining submodule is used for determining the region where the potential brown areas are located according to the map data of the target region, the industrial POI data, the mining POI data and the landfill POI data, and identifying the potential brown areas in the remote sensing image of the target region.
Optionally, the first obtaining sub-module is specifically configured to:
acquiring map POI data of a target area;
selecting POI data of a category related to industrial brown-like areas from the map POI data of the target area as first industrial POI data;
screening keywords of data except the first industrial POI data in the map POI data to obtain supplementary industrial POI data;
and obtaining industrial POI data according to the first industrial POI data and the supplementary industrial POI data.
Optionally, the second obtaining sub-module is specifically configured to:
screening idle mining POI data from the mining area POI data according to the attribute of each mining area in the mining area POI data of the target area;
the attributes of the respective mining areas at least comprise: the current situation is utilized.
Optionally, the third obtaining sub-module is specifically configured to:
acquiring map POI data of a target area;
selecting POI data of a category relevant to landfill from the map POI data of the target area as first landfill POI data;
performing keyword screening on data except the first landfill POI data in the map POI data to obtain supplementary landfill POI data;
and obtaining landfill POI data according to the first landfill POI data and the supplementary landfill POI data.
Optionally, the classification module 502 is specifically configured to:
obtaining the category and the spatial boundary of each potential brown zone in the zone where the potential brown zones are located according to the spatial boundary, facilities, hue, elevation, area and texture of the remote sensing image of the target zone;
and identifying in the remote sensing image of the target area according to the category and the space boundary of each potential brown area to obtain a multi-category potential brown area distribution map.
Optionally, the multi-source spatial and non-spatial data further includes: the current urban land map, the urban land planning map and the Shi Zhi data, the correction module 503 is specifically configured to:
according to the urban land present map, the urban land planning map, the Shi Zhi data and the multi-time sequence people flow thermodynamic map, eliminating land blocks still in operation in the multi-class potential brown land distribution map, and supplementing missed brown lands identified in the multi-class potential brown land distribution map.
Based on the same inventive concept, embodiments of the present invention provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for palm recognition based on multi-source information analysis according to any of the above embodiments.
Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the brown land identification method based on multi-source information analysis according to any of the embodiments.
Based on the same inventive concept, embodiments of the present invention provide a computer program product, which includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the computer program/instruction implements the steps of the method for brown land identification based on multi-source information analysis described in any of the above embodiments.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are all described in a progressive manner, and each embodiment focuses on differences from other embodiments, and portions that are the same and similar between the embodiments may be referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal apparatus that comprises the element.
The method, apparatus, electronic device, computer-readable storage medium and computer program product for palm rejection identification provided by the present invention are described in detail above, and specific examples are applied herein to explain the principles and embodiments of the present invention, and the descriptions of the above examples are only used to help understand the method and its core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A brown land identification method based on multi-source information analysis is characterized by comprising the following steps:
determining a potential brown region in a remote sensing image of a target region according to map data of the target region and POI data of the target region;
according to the spatial features contained in the remote sensing image of the target area, potential brown areas are determined from the areas where the potential brown areas are located, the potential brown areas are classified, and a multi-class potential brown area distribution diagram is obtained;
correcting the multi-class potential brown land distribution map according to multi-source spatial and non-spatial data to obtain identification results of brown lands of multiple classes, wherein the multi-source spatial and non-spatial data at least comprise: a multi-time sequence people flow thermodynamic diagram;
wherein the various categories of brown land include: the method comprises the steps of refuse landfill, raw material mining similar brown land, tailing pond similar brown land, raw material processing and manufacturing similar brown land, non-raw material processing and manufacturing similar brown land and infrastructure similar brown land.
2. The method of claim 1, wherein determining the region of potential brown locations in the remote sensing image of the target region according to the map data of the target region and the POI data of the target region comprises:
acquiring map POI data of a target area, and selecting industrial POI data related to industry from the map POI data;
the method comprises the steps of obtaining mining area POI data of a target area, and screening idle mining POI data from the mining area POI data;
acquiring map POI data of a target area, and selecting landfill POI data related to landfill from the map POI data;
and determining the region where the potential brown areas are located according to the map data of the target region, the industrial POI data, the mining POI data and the landfill POI data.
3. The method of claim 2, wherein selecting industry-like POI data from the map POI data that is relevant to an industry comprises:
acquiring map POI data of a target area;
selecting POI data of a category related to industrial brown fields from the map POI data of the target area as first industrial POI data;
screening keywords of data except the first industrial POI data in the map POI data to obtain supplementary industrial POI data;
and obtaining industrial POI data according to the first industrial POI data and the supplementary industrial POI data.
4. The method of claim 2, wherein screening the mining POI data for idleness comprises:
screening idle mining POI data from the mining area POI data according to the attribute of each mining area in the mining area POI data of the target area;
the attributes of the respective mining areas at least include: the current situation is utilized.
5. The method of claim 2, wherein selecting landfill-like POI data related to landfill from the map POI data comprises:
acquiring map POI data of a target area;
selecting POI data of a category relevant to landfill from the map POI data of the target area as first landfill POI data;
performing keyword screening on data except the first landfill POI data in the map POI data to obtain supplementary landfill POI data;
and obtaining landfill POI data according to the first landfill POI data and the supplementary landfill POI data.
6. The method according to any one of claims 1-5, wherein determining potential brown areas from the areas where the potential brown areas are located according to spatial features contained in the remote sensing images of the target areas, and classifying the potential brown areas to obtain a multi-category potential brown area distribution map, comprises:
obtaining the category and the space boundary of each potential brown zone in the region where the potential brown zone is located according to the space boundary, facilities, hue, elevation, area and texture of the remote sensing image of the target region;
and identifying in the remote sensing image of the target area according to the category and the space boundary of each potential brown area to obtain a multi-category potential brown area distribution map.
7. The method of any of claims 1-5, wherein the multi-source spatial and non-spatial data further comprises: the method comprises the following steps of correcting the multi-class potential brown land distribution map according to multi-source space and non-space data by using a city land current situation map, a city land planning map and Shi Zhi data, wherein the correction comprises the following steps:
according to the urban land present map, the urban land planning map, the Shi Zhi data and the multi-time sequence people flow thermodynamic map, eliminating land blocks still in operation in the multi-class potential brown land distribution map, and supplementing missed brown lands identified in the multi-class potential brown land distribution map.
8. A brown land identification device based on multi-source information analysis, characterized in that the device comprises:
the determining module is used for determining a region where a potential brown region is located in a remote sensing image of a target region according to map data of the target region and POI data of the target region;
the classification module is used for determining potential brown areas from the areas where the potential brown areas are located according to the spatial features contained in the remote sensing images of the target areas, and classifying the potential brown areas to obtain multi-class potential brown area distribution maps;
the correction module is used for correcting the multi-class potential brown land distribution map according to multi-source spatial and non-spatial data to obtain identification results of brown lands of multiple classes, and the multi-source spatial and non-spatial data at least comprise: a multi-sequence people flow thermodynamic diagram;
wherein the various categories of brown land include: the method comprises the following steps of refuse landfill, raw material mining similar-brown land, tailing pond similar-brown land, raw material processing and manufacturing similar-brown land, non-raw material processing and manufacturing similar-brown land and infrastructure similar-brown land.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the multi-source information analysis-based brown land identification method according to any one of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for brown identification based on multi-source information analysis according to any one of claims 1 to 7.
11. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method for brown identification based on multi-source information analysis according to any of claims 1-7.
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