CN111832527A - Residential community extraction and type identification method based on remote sensing and social perception data - Google Patents
Residential community extraction and type identification method based on remote sensing and social perception data Download PDFInfo
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Abstract
The invention relates to a residential community extraction and type identification method based on remote sensing and social sensing data, which comprises the steps of preprocessing a remote sensing image, calculating angle difference characteristics, identifying the Point of Interest of a residential community, extracting the boundary of the residential community and identifying the type of the residential community; aiming at the blank of extraction and type identification of the urban residential area at present, remote sensing and social perception data are fused, a technical scheme for quickly and effectively developing the extraction and type identification method of the urban residential area is provided, and an accurate data source is provided for an urban planning department and urban researchers; the effect of extracting residential cells in real time can be achieved; in addition, because text similarity analysis is combined, the method can quickly and effectively provide the vector file of the residential area for the city researchers and the city planning department.
Description
Technical Field
The invention relates to the technical field of remote sensing and geographic information system application, in particular to a residential community extraction and type identification method based on remote sensing and social sensing data.
Background
Urban land only accounts for 1% of the global land, but does accommodate over 50% of the urban population; although there is controversy in terms of energy consumption and social impact on the environmental sustainability of residential quarters of high-rise buildings, residential quarters of medium-high-rise buildings are becoming the mainstream of residential construction of cities in china in order to accommodate more urban population; one of the main advantages of the residential community of the medium and high-rise building is intensive utilization of urban land, and a typical high-rise residential district in Beijing can accommodate 1 to 1.5 thousands of residents; for the health of residents, it is very important to create good residential district climate and environment;
at present, studies have begun to explore residential habitability (Zhanget al.,2019), microclimate (Wu et al., 2016; Zhang et al.,2018) and impact on residential health (zenland Li,2018) with residential cells as study objects; a fast, high-efficient residential area draws and type identification method, can offer the data source for the research unit taking residential area as study object;
in the prior art, urban residential area extraction (Zhang et al, 2015) can be performed by combining remote sensing and a geographic information system; however, the technology is only suitable for extracting coarse-grained residential land and is not suitable for fine-grained residential cell extraction and type identification.
Disclosure of Invention
The invention aims to solve the technical problem of providing a residential area extraction and type identification method based on remote sensing and social perception data. Aiming at the blank of extraction and type recognition of urban residential districts, remote sensing and social perception data are fused, the method for extracting and identifying the types of the urban residential districts is developed through scientific method design, a quick and effective technical scheme is provided, and an accurate data source is provided for urban planning departments and urban researchers.
A residential community extraction and type identification method based on remote sensing and social perception data comprises the following technical steps:
step 1: preprocessing a remote sensing image;
further, extracting Angular Difference Features (ADF) by using front-view, front-view and rear-view remote sensing images provided by resource satellites, wherein the Angular Difference features are used for identifying the types of residential cells;
as an illustration, the resource satellite uses the resource satellite number 3;
further, before the angle difference feature extraction, the resolution (2.5 meters) of the forward-looking remote sensing image and the backward-looking remote sensing image provided by the resource satellite needs to be resampled to be the same as the resolution (2.1 meters) of the front-looking remote sensing image;
further, after resampling, taking the front-view remote sensing image as a reference, and performing histogram matching on the front-view, front-view and rear-view remote sensing images;
as an illustration, the operation tool for resampling and histogram matching adopts ENVI software;
step 2: calculating the angle difference characteristic;
further, when the operation of step one is completed, the angle difference characteristic is calculated by formula (1):
ADF＝max(Xb-Xn,Xn-Xf,Xb-Xf) Formula (1)
Wherein, Xn、Xf、XbRespectively an orthophoria remote sensing image, a foresight remote sensing image and a rearview remote sensing image; the extraction result is shown in FIG. 1;
and step 3: identifying a residential community Point of Interest;
further, the Point of Interest is referred to as a POI for short, and the POI data is collected by an intelligent map and used for map navigation service;
as an example, the smart map is: one or a combination of a Baidu map, a Gaode map or an Tencent map;
further, each piece of POI data is presented in a vector format, and each piece of POI data has three pieces of attribute information, that is: type, name, latitude and longitude location;
as an illustration, the original POI type classification framework can refer to the description content in the link https:// lbs.
Wherein the categories relating to the residential cell include (in a broad-medium-small format): business residence-residential area-villa, business residence-residential area-residential district, business residence-residential area-community center; in place name address information-house number information-building number, passage facility-building door-building front door, passage facility-courtyard door-courtyard front door, there are also POIs related to residential quarters, which are scattered on building numbers and doors inside residential quarters, as shown in fig. 2; because the intelligent map assigns the POI points to the types irrelevant to the residential area, the POI points relevant to the residential area but not relevant to the residential area need to be identified from the categories of the "address information of place name" and the "traffic facility" through the text similarity analysis, and the specific operations include:
i, POI category reclassification: reclassifying the POI categories according to Table 1;
TABLE 1 POI reclassification operation reference table
Secondly, performing text similarity analysis on the POI to be identified and the POI of the house;
the method uses Jaro-winkler text similarity measurement to calculate the text similarity between the POI to be identified and the POI of the house; if the similarity is greater than 0.85, the "to-be-recognized" POI will be recognized as a "home" POI, and if the similarity is less than 0.85, the "to-be-recognized" POI will not be recognized as a "home" POI;
as an example, the analysis function packet for implementing text similarity measurement calculation adopts a Python packet;
and 4, step 4: extracting boundaries of residential areas;
firstly, extracting land parcels;
the method uses road network data provided by an Open Street Map to generate a land parcel which is used as an analysis unit for extracting the boundary of a residential area;
by way of illustration, the Open Street Map is an Open source, editable Map service application co-produced by the network public;
the road network data is presented in a vector format, and roads are divided into different levels according to the relative importance of the roads; the method comprises the steps of classifying the original classification of roads into 3 grades according to the classification, referring to the table 2, and establishing buffer areas with the radiuses of 40 meters, 20 meters and 10 meters; the extraction result is shown in FIG. 3;
TABLE 2 road grade reclassification and buffer radius description
Secondly, identifying the residential community POI cluster;
performing text similarity measurement clustering on the character strings of the POI names, wherein the analysis object is a 'house' POI, the analysis unit is used for clustering 'house' POIs with similar names into a class through similarity measurement for each land parcel; the method adopts Jaro-winkler text similarity to calculate text similarity, and selects 0.85 as a threshold value of the text similarity to generate different residential POI clusters, and the clustered residential POIs form different residential district POI cluster clusters;
as an illustration, the identification of the residential community POI cluster clusters is the same as the third step, and a Jaro-winkler text similarity calculation method is also adopted;
thirdly, extracting the boundary of the residential area;
acquiring a residential community boundary from a residential community POI cluster by adopting concave packet analysis; adopting a concave packet algorithm based on a triangular irregular network;
further, the pocking algorithm is as follows: searching neighborhood points in the clustering cluster by calculating the circle center and the radius of a triangle circumcircle, constructing a Delaunay triangulation network by a recursive growth method, and finally obtaining a concave packet of the clustering cluster based on the outer boundary of the generated triangulation network; the residential community POI clustering result and the boundary extraction result are shown in FIG. 4;
as an illustration, the foveal bag analysis refers to finding its minimum polygon boundary based on a set of cluster clusters;
as an illustration, the triangular irregular network prefers TIN;
and 5: identifying the type of a residential area;
extracting the average value of ADFs in the surfaces by taking the surface vector file of the residential area as a unit; setting ADF threshold values, and dividing the ADF threshold values into low-layer residential cells (ADF <60), middle-layer residential cells (ADF is more than or equal to 60 and less than or equal to 80) and high-layer residential cells (ADF is more than 80); the extraction result is shown in FIG. 5;
the invention has the following beneficial effects:
the method integrates remote sensing data and social sensing data, combines text analysis technology and POI data name analysis for the first time, and can achieve the effect of extracting residential districts in real time; in addition, ADF characteristics are used for identifying types of residential cells for the first time, and due to the combination of text similarity analysis, the method can quickly and effectively provide the residential cell face vector files for city researchers and city planning departments.
Drawings
FIG. 1 is a diagram showing the resource No. 3 star angle difference feature extraction result of the residential quarter extraction and type identification method based on remote sensing and social perception data of the present invention
FIG. 2 is a schematic diagram showing the relationship between the residential POI and the building door and building number POI of the residential district extraction and type recognition method based on remote sensing and social perception data according to the present invention
FIG. 3 is a plot of the result of the extraction of residential areas and the type recognition method based on remote sensing and social perception data according to the present invention
FIG. 4 is a graph of residential community POI cluster results and boundary extraction results of the residential community extraction and type identification method based on remote sensing and social perception data according to the present invention
FIG. 5 is a diagram of residential community type recognition results of the residential community extraction and type recognition method based on remote sensing and social perception data of the present invention
Detailed Description
The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings;
referring to fig. 1 to 5, the residential community extraction and type identification method based on remote sensing and social perception data comprises the following technical steps:
step 1: preprocessing a remote sensing image;
further, extracting Angular Difference Features (ADF) by using front-view, front-view and rear-view remote sensing images provided by resource satellites, wherein the Angular Difference features are used for identifying the types of residential cells;
as an illustration, the resource satellite uses the resource satellite number 3;
further, before the angle difference feature extraction, the resolution (2.5 meters) of the forward-looking remote sensing image and the backward-looking remote sensing image provided by the resource satellite needs to be resampled to be the same as the resolution (2.1 meters) of the front-looking remote sensing image;
further, after resampling, taking the front-view remote sensing image as a reference, and performing histogram matching on the front-view, front-view and rear-view remote sensing images;
as an illustration, the operation tool for resampling and histogram matching adopts ENVI software;
step 2: calculating the angle difference characteristic;
further, when the operation of step one is completed, the angle difference characteristic is calculated by formula (1):
ADF＝max(Xb-Xn,Xn-Xf,Xb-Xf) Formula (1)
Wherein, Xn、Xf、XbRespectively an orthophoria remote sensing image, a foresight remote sensing image and a rearview remote sensing image; the extraction result is shown in FIG. 1;
and step 3: identifying a residential community Point of Interest;
further, the Point of Interest is referred to as a POI for short, and the POI data is collected by an intelligent map and used for map navigation service;
as an example, the smart map is: one or a combination of a Baidu map, a Gaode map or an Tencent map;
further, each piece of POI data is presented in a vector format, and each piece of POI data has three pieces of attribute information, that is: type, name, latitude and longitude location;
as an illustration, the original POI type classification framework can refer to the description content in the link https:// lbs.
Wherein the categories relating to the residential cell include (in a broad-medium-small format): business residence-residential area-villa, business residence-residential area-residential district, business residence-residential area-community center; in place name address information-house number information-building number, passage facility-building door-building front door, passage facility-courtyard door-courtyard front door, there are also POIs related to residential quarters, which are scattered on building numbers and doors inside residential quarters, as shown in fig. 2; because the intelligent map assigns the POI points to the types irrelevant to the residential area, the POI points relevant to the residential area but not relevant to the residential area need to be identified from the categories of the "address information of place name" and the "traffic facility" through the text similarity analysis, and the specific operations include:
firstly, re-classifying POI categories; reclassifying the POI categories according to Table 1;
TABLE 1 POI reclassification operation reference table
Secondly, performing text similarity analysis on the POI to be identified and the POI of the house;
the method uses Jaro-winkler text similarity measurement to calculate the text similarity between the POI to be identified and the POI of the house; if the similarity is greater than 0.85, the "to-be-recognized" POI will be recognized as a "home" POI, and if the similarity is less than 0.85, the "to-be-recognized" POI will not be recognized as a "home" POI;
as an example, the analysis function packet for implementing text similarity measurement calculation adopts a Python packet;
and 4, step 4: extracting boundaries of residential areas;
firstly, extracting land parcels;
the method uses road network data provided by an Open Street Map to generate a land parcel which is used as an analysis unit for extracting the boundary of a residential area;
by way of illustration, the Open Street Map is an Open source, editable Map service application co-produced by the network public;
the road network data is presented in a vector format, and roads are divided into different levels according to the relative importance of the roads; the method comprises the steps of classifying the original classification of roads into 3 grades according to the classification, referring to the table 2, and establishing buffer areas with the radiuses of 40 meters, 20 meters and 10 meters; the extraction result is shown in FIG. 3;
TABLE 2 road grade reclassification and buffer radius description
Secondly, identifying the residential community POI cluster;
performing text similarity measurement clustering on the character strings of the POI names, wherein the analysis object is a 'house' POI, the analysis unit is used for clustering 'house' POIs with similar names into a class through similarity measurement for each land parcel; the method adopts Jaro-winkler text similarity to calculate text similarity, and selects 0.85 as a threshold value of the text similarity to generate different residential POI clusters, and the clustered residential POIs form different residential district POI cluster clusters;
as an illustration, the identification of the residential community POI cluster clusters is the same as the third step, and a Jaro-winkler text similarity calculation method is also adopted;
thirdly, extracting the boundary of the residential area;
acquiring a residential community boundary from a residential community POI cluster by adopting concave packet analysis; adopting a concave packet algorithm based on a triangular irregular network;
further, the pocking algorithm is as follows: searching neighborhood points in the clustering cluster by calculating the circle center and the radius of a triangle circumcircle, constructing a Delaunay triangulation network by a recursive growth method, and finally obtaining a concave packet of the clustering cluster based on the outer boundary of the generated triangulation network; the residential community POI clustering result and the boundary extraction result are shown in FIG. 4;
as an illustration, the foveal bag analysis refers to finding its minimum polygon boundary based on a set of cluster clusters;
as an illustration, the triangular irregular network prefers TIN;
and 5: identifying the type of a residential area;
extracting the average value of ADFs in the surfaces by taking the surface vector file of the residential area as a unit; setting ADF threshold values, and dividing the ADF threshold values into low-layer residential cells (ADF <60), middle-layer residential cells (ADF is more than or equal to 60 and less than or equal to 80) and high-layer residential cells (ADF is more than 80); the extraction result is shown in FIG. 5;
the invention integrates remote sensing data and social sensing data, and can achieve the effect of extracting residential districts in real time; in addition, because text similarity analysis is combined, the method can quickly and effectively provide the vector file of the residential area for the city researchers and the city planning department.
The above embodiments are only preferred embodiments of the present invention, and it should be understood that the above embodiments are only for assisting understanding of the method and the core idea of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalents and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A residential community extraction and type identification method based on remote sensing and social perception data is characterized by comprising the following technical steps:
step 1: preprocessing a remote sensing image;
extracting angle difference characteristics by adopting front-view, front-view and rear-view remote sensing images provided by a resource satellite, wherein the angle difference characteristics are used for identifying the types of residential areas;
before the angle difference feature extraction is carried out, the resolution of the forward-looking remote sensing image and the backward-looking remote sensing image provided by the resource satellite needs to be resampled to be the same as the resolution of the front-looking remote sensing image;
after resampling, taking the front-view remote sensing image as a reference, and performing histogram matching on the front-view, front-view and rear-view remote sensing images;
step 2: calculating the angle difference characteristic;
after the operation of the first step is completed, the angle difference characteristic is calculated by formula 1:
ADF＝max(Xb-Xn,Xn-Xf,Xb-Xf) Formula (1)
Wherein, Xn、Xf、XbRespectively an orthophoria remote sensing image, a foresight remote sensing image and a rearview remote sensing image; ADF is an angular difference Feature, namely Angularidiference Feature;
and step 3: identifying a residential community Point of Interest;
the Point of Interest is short for POI, and the POI data is collected by an intelligent map and is used for map navigation service;
each piece of POI data is presented in a vector format, and each piece of POI data has three attribute information, namely: type, name, latitude and longitude location;
wherein the categories relating to the residential cell include (in a broad-medium-small format): business residence-residential area-villa, business residence-residential area-residential district, business residence-residential area-community center; in place name address information-house number information-building number, passing facility-building door-building front door, passing facility-gate, and residential district-gate front door, there are also POIs related to residential districts, and these POIs are scattered in building number and gate inside residential districts, because the intelligent map classifies these POIs as a type unrelated to residential districts, it is necessary to identify the POIs related to the residential districts but classified as unrelated from the categories of "place name address information" and "passing facility" through text similarity analysis, and the concrete operations include:
firstly, re-classifying POI categories; reclassifying the POI categories according to Table 1;
TABLE 1 POI reclassification operation reference table
Secondly, performing text similarity analysis on the POI to be identified and the POI of the house;
calculating the text similarity between the POI to be identified and the POI of the house by using the Jaro-winkler text similarity measurement; if the similarity is greater than 0.85, the "to-be-recognized" POI will be recognized as a "home" POI, and if the similarity is less than 0.85, the "to-be-recognized" POI will not be recognized as a "home" POI;
and 4, step 4: extracting boundaries of residential areas;
firstly, extracting land parcels;
generating a land parcel which is to be used as an analysis unit for residential community boundary extraction by using road network data provided by an Open Street Map;
the road network data is presented in a vector format, and roads are divided into different levels according to the relative importance of the roads; the method comprises the steps of classifying the original classification of roads into 3 grades according to the classification, referring to the table 2, and establishing buffer areas with the radiuses of 40 meters, 20 meters and 10 meters;
TABLE 2 road grade reclassification and buffer radius description
Secondly, identifying the residential community POI cluster;
performing text similarity measurement clustering on the character strings of the POI names, wherein the analysis object is a 'house' POI, the analysis unit is used for clustering 'house' POIs with similar names into a class through similarity measurement for each land parcel; adopting Jaro-winkler text similarity to calculate text similarity, selecting 0.85 as a threshold value of the text similarity to generate different residential POI clusters, wherein the clustered residential POIs form different residential district POI cluster clusters;
thirdly, extracting the boundary of the residential area;
acquiring a residential community boundary from a residential community POI cluster by adopting concave packet analysis; adopting a concave packet algorithm based on a triangular irregular network;
and 5: identifying the type of a residential area;
extracting the average value of ADFs in the surfaces by taking the surface vector file of the residential area as a unit; and setting ADF threshold values, wherein the ADF threshold values are divided into a low-layer residential cell ADF of less than 60, a middle-layer residential cell ADF of less than or equal to 60 and less than or equal to 80 and a high-layer residential cell ADF of more than 80.
2. The remote sensing and socially aware data-based residential quarter extraction and type recognition method as claimed in claim 1, wherein the resource satellite uses resource number 3 satellite 02.
3. The method for residential cell extraction and type identification based on remote sensing and socially perceptual data as claimed in claim 1, wherein the operational tool for resampling and histogram matching employs ENVI software.
4. The residential community extraction and type identification method based on remote sensing and social perception data according to claim 1, wherein the intelligent map is: one or a combination of a Baidu map, a Gade map or an Tencent map.
5. The method for residential cell extraction and type recognition based on remote sensing and socially aware data according to claim 1, wherein the original POI type classification framework can refer to the description in the link https:// lbs.
6. The method for residential district extraction and type recognition based on remote sensing and socially aware data according to claim 1, characterized in that the analysis function package for implementing text similarity measurement calculation employs Python package.
7. The method for residential cell extraction and type identification based on remote sensing and socially aware data as claimed in claim 1, wherein the Open Street Map is an Open source, editable Map service application co-produced by the network public.
8. The residential community extraction and type identification method based on remote sensing and social perception data according to claim 1, wherein the concave packet algorithm is as follows: and searching neighborhood points in the cluster by calculating the circle center and the radius of a triangle circumcircle, constructing a Delaunay triangulation network by a recursive growth method, and finally obtaining a concave packet of the cluster based on the outer boundary of the generated triangulation network.
9. The method for residential area extraction and type identification based on remote sensing and socially perceptual data as claimed in claim 1, wherein the foveal bag analysis is based on a set of cluster clusters to find its minimum polygon boundary.
10. The remote sensing and socially aware data-based residential cell extraction and type recognition method of claim 1, wherein the triangular irregular network prefers TIN.
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