A block division method based on urban multi-dimensional crown layer morphological characteristics

Abstract

The present application belongs to the technical field of data analysis, and particularly relates to a street block division method based on urban multi-dimensional crown layer morphological characteristics, comprising the following steps: step 1, collecting and processing vector boundary data of a target research area to obtain a vector boundary graph of the target research area; step 2, collecting and processing vegetation crown layer height, building height, vegetation density and water area data to obtain a water area vector graph, vegetation crown layer grid data, building height grid data and street block division graph of the target research area. The present application combines high-resolution remote sensing extraction, spatial structure index construction and street block granularity division to provide a basic support for subsequent thermal effect analysis, forms a whole-process integrated technical chain from data acquisition-index system construction-street block unit division-classification identification-thermal effect analysis, breaks through the limitations of traditional methods in structural dimension integration, scale matching and thermal response mechanism modeling, and exhibits high integration and system innovation.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and in particular relates to a method for dividing urban blocks based on the multidimensional canopy morphological characteristics of cities. Background Technology

[0002] In recent years, the urban heat island effect (UHI) has intensified, becoming a key environmental problem affecting the stability of urban ecosystems and the health and safety of residents. To address the challenges posed by the heterogeneity of the urban thermal environment, researchers rely heavily on remote sensing data to analyze land surface temperature (LST) and urban morphological characteristics. In existing research, the role of remote sensing data in spatial heat distribution analysis is increasingly prominent; however, related technologies still face the following technical bottlenecks in analyzing the driving factors of the urban thermal environment.

[0003] First, many existing studies still rely heavily on low- or medium-resolution remote sensing data, such as MODIS or GOES, with spatial resolutions mostly around 1 km. While these datasets offer good coverage at macroscopic scales, they are insufficient to reveal the spatial heterogeneity of the thermal environment at the block scale within cities, and cannot accurately characterize the fine-grained structural features of building clusters or vegetation canopies, thus limiting the ability to accurately identify thermal risks (R. Wang et al., 2022).

[0004] Secondly, traditional urban block division methods, such as Urban Morphological Blocks (UMBs) or Local Climate Zones (LCZs), are mostly based on two-dimensional surface features (such as land use type and impervious surface ratio), lacking a systematic consideration of three-dimensional structural features (such as building height and vegetation canopy height) and spatiotemporal thermal response mechanisms during the modeling process. Taking LCZ as an example, although it is widely used in urban classification, it does not combine vertical structural elements or specific thermal effect indicators, making it difficult to reflect the structural-functional linkage characteristics in the urban microclimate regulation mechanism (Han et al., 2022).

[0005] Finally, while some studies have attempted to incorporate 3D building data or thermal infrared remote sensing imagery for spatial modeling, most only consider building structures or impermeable surfaces in isolation, lacking quantitative analysis of the coupling mechanism between urban green space (such as canopy height) and thermal effects (Guo et al., 2023; Yu et al., 2020). Furthermore, the response relationship between thermal environment and morphological structure exhibits significant temporal and scale dependence (Liu et al., 2024), but existing methods are typically based on static block boundaries or simple grid divisions, making it difficult to support the coordinated modeling of multi-scale, multi-source structural factors.

[0006] Since most of the above-mentioned technical solutions only remain at the stage of two-dimensional indicators or single-factor analysis, their direct technical effect is that they cannot accurately reveal the comprehensive impact of the multi-dimensional structural combination of buildings and vegetation on the distribution of surface temperature. This limits the accuracy of urban thermal risk identification and the scientific nature of urban classification, ultimately resulting in a lack of suitable countermeasures in urban planning or thermal environment management. Summary of the Invention

[0007] The purpose of this invention is to address the aforementioned technical problems by providing a method for dividing urban blocks based on the multidimensional canopy morphology characteristics.

[0008] In view of this, the present invention provides a method for dividing urban blocks based on the multidimensional canopy morphological characteristics, comprising the following steps:

[0009] Step 1: Collect and process vector boundary data of the target study area to obtain a vector boundary map of the target study area;

[0010] Step 2: Collect and process data on vegetation canopy height, building height, vegetation density, and water area to obtain a vector map of water area, raster data of vegetation canopy, raster data of building height, and street zoning map of the target study area.

[0011] Step 3: Obtain the five-level street block data from high to low and perform subsequent processing. The granularity of the higher-level street blocks is greater than that of the lower-level street blocks.

[0012] Step 4: Perform dimensionality reduction analysis on building height, vegetation canopy height, impermeable surface density, and vegetation cover density, and then cluster the dimensionality reduction results.

[0013] Preferably, in step 1, the vector boundary data of the target study area is obtained through a GIS platform, which includes, but is not limited to, at least one of Google Earth, ArcGIS, or QGIS. The method of obtaining the vector boundary data of the target study area includes delineating and exporting the polygonal boundary of the target study area using the spatial vector drawing tool of the GIS platform, or retrieving preset boundary data from an existing database.

[0014] Preferably, the building height data is generated by fusing remote sensing images, street view images, and POI data using a machine learning model or a multimodal model, or directly obtained from an existing database.

[0015] Preferably, the block data is generated based on road network data and administrative boundary data through a multi-level block division algorithm. The division algorithm uses at least one of the following indicators—area variation coefficient, shape index, or boundary consistency index—to compare and verify with official data, or directly obtains data from an existing database.

[0016] Preferably, the vegetation canopy height data is generated by fusing satellite remote sensing data and field measurement data using a convolutional neural network model, or directly obtained from an existing database.

[0017] Preferably, step 3 further includes assigning values ​​to the street blocks, with the specific steps as follows:

[0018] Step 301: Import the water vector map, vegetation canopy raster data, building height raster data, street zoning map, and vector boundary map of the target study area into the geographic information system software;

[0019] Step 302: Using the cropping tool, crop the street zoning map, the target study area water vector map, and the target study area vector boundary map respectively to obtain the street zoning map and the target study area water vector map at the corresponding level; use the raster to vector tool to convert the vegetation canopy raster data into vector data to obtain the vegetation canopy vector map.

[0020] Step 303: Use the intersection tool to link the vegetation canopy vector map with the corresponding level of street zoning map, add a new field and calculate the vegetation canopy area; use the union tool to link the water body vector map with the vegetation canopy vector map, and use the intersection tool again to link the street zoning map to obtain the area of ​​the linked graphic;

[0021] Step 304: Based on the street district number, use the summary statistics tool to summarize and statistically analyze the vegetation canopy area and the area of ​​the associated graphic, respectively;

[0022] Step 305: Using a tabular display tool for zoning statistics, assign the average values ​​of the vegetation canopy raster data and building height raster data to the street zoning map, link the summary statistics results with the street zoning attribute table, and obtain an attribute table containing street area, vegetation canopy height, building height, vegetation canopy area, and permeable surface area.

[0023] Step 306: Calculate the vegetation canopy density based on the vegetation canopy area and the street area, calculate the impermeable surface density based on the permeable surface area and the street area, and export the attribute table containing four-dimensional data for subsequent clustering.

[0024] Preferably, the formula for calculating the vegetation canopy density (VCdensity) is:

[0025]

[0026] Where VCarea is the area of ​​the vegetation canopy in a certain block, and area is the area of ​​that block.

[0027] Preferably, the formula for calculating the impermeable surface density ISdensity is as follows:

[0028]

[0029] Where PSarea is the permeable surface area of ​​a certain block, and area is the area of ​​that block.

[0030] Preferably, the dimensionality reduction analysis adopts principal component analysis. During the dimensionality reduction process, a preset number of principal components is retained, redundant indicators are removed, and the main information of the source data is retained.

[0031] Preferably, the clustering adopts K-Means clustering, and the elbow rule is introduced to initially determine the number of clusters, and the clustering results are evaluated by combining the silhouette coefficient and the Calinski-Harabasz index.

[0032] The beneficial effects of this invention are:

[0033] This invention organically combines high-resolution remote sensing extraction, spatial structure index construction, and street block granularity division, providing fundamental support for subsequent thermal effect analysis. It forms a fully integrated technology chain from data acquisition to index system construction, street block unit division, classification and identification, and thermal effect analysis. The modules support each other and work together, breaking through the limitations of traditional methods in structural dimension integration, scale matching, and thermal response mechanism modeling, demonstrating a high degree of integration and system innovation. Attached Figure Description

[0034] Figure 1 A diagram illustrating the classification of neighborhoods;

[0035] Figure 2 This is a flowchart illustrating an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0037] It should be noted that all directional and positional terms used in this invention, such as "up," "down," "left," "right," "front," "back," "vertical," "horizontal," "inner," "outer," "top," "lower," "lateral," "longitudinal," and "center," are only used to explain the relative positional relationships and connections between components in a specific state (as shown in the accompanying drawings). They are merely for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. Furthermore, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0038] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0040] In view of the shortcomings of the existing technology, the purpose of this invention is:

[0041] 1) Construct a new urban block division scheme that integrates two-dimensional (2D) and three-dimensional (3D) canopy morphology features across multiple scales and dimensions, and extract multi-dimensional indicators such as building height, vegetation canopy height, impermeable surface density and vegetation density based on high-resolution remote sensing images.

[0042] 2) Six types of complex morphological blocks (CMBs) were identified using principal component analysis and K-Means clustering to facilitate thermal risk analysis of blocks in conjunction with land surface temperature (LST) retrieved from thermal infrared bands.

[0043] Unlike existing technologies that only optimize specific areas, this invention, through a holistic design encompassing data acquisition, structural index construction, spatial unit division, and clustering, forms a systematic and innovative chain from structural feature identification to thermal environment response modeling. This chain possesses strong integration capabilities and engineering application value. Therefore, this solution can comprehensively and systematically reflect the causal coupling relationship between urban spatial structure and the thermal environment, providing scientific support for urban heat island control and environmental zoning management at a fine scale, as detailed below:

[0044] like Figures 1-2 As shown, a method for dividing urban blocks based on multidimensional canopy morphology features is implemented through the following technical steps:

[0045] Step 1: Use Google Earth to define the vector boundary of the target research area within the Fifth Ring Road (the area within the Fifth Ring Road of Beijing);

[0046] Step 2: Collection and processing of data on vegetation canopy height, building height, vegetation density, and water area.

[0047] Step 3: Obtain the five-level street block data from high to low (L1 to L5) and perform subsequent processing. The granularity of the higher-level street blocks is greater than that of the lower-level street blocks.

[0048] Step 4: Principal Component Analysis (PCA) is used to perform K-Means clustering on the dimensionality reduction results of building height, vegetation canopy height, water area and vegetation cover density.

[0049] The process of defining the vector boundary of the target study area is as follows:

[0050] First, use Google Earth to visually interpret and determine the basic location of the target area. Then, use the "Add Polygon" tool to define the vector boundary of the industrial target area and save it as a kml or kmz format. After that, use ArcGIS's "Export from kml" tool to convert the vector data into a shp format and save it again.

[0051] The sources of multidimensional canopy data are as follows:

[0052] Building height data was provided by Zhang, Y, et al. (Zhang, et al. 2025). This dataset is based on the fusion of Google Earth imagery, street view images and POI data with a resolution of 0.3-1 meter, and is generated through machine learning and large multimodal models such as OCRNet and XGboost. After validation by multiple methods (model benchmarking, etc.), it shows that more than 80% of the results meet the standards.

[0053] The street block data is a dataset generated by dividing the streets into multiple levels based on OSM road data and GADM administrative boundaries. It is compared with official data using indicators such as area variation coefficient, shape index, and boundary consistency.

[0054] The vegetation canopy height data is based on data from the Gaofen-7 stereo satellite and data from on-site measurements of tree height using a handheld laser rangefinder. It was generated using a multi-task convolutional neural network model, with a root mean square error (RMSE) of 3.16 m (from Wu, et al. 2024). The water area data was provided by Open Street Map. The high precision of the building height and vegetation canopy data can directly support the three-dimensional morphological characterization of the street blocks. The high-resolution three-dimensional data and multi-scale segmentation work together to enhance the explanatory power of thermal environment driving factors and better characterize the impact of the "building-vegetation" vertical structure on heat exchange than traditional two-dimensional data (such as NDVI).

[0055] The process of assigning values ​​to neighborhoods is as follows:

[0056] Import the data obtained in the previous step, including the water vector map of the target study area, vegetation canopy raster data, building height raster data, L1 to L5 street zoning maps of the target study area, and vector boundary map data of the target study area, into ArcGIS Pro. The detailed steps are as follows:

[0057] The first step is to use the clip tool to clip the street zoning maps of L1 to L5 and the vector boundary map of the target study area to obtain the street zoning maps of the target study area L1 to L5 (hereinafter referred to as L1 to L5, and taking L5 as an example; the same applies to L1 to L4). Similarly, the same tool is used to clip the vector map of the water bodies in the target study area to obtain the vector map of the water bodies in the target study area. The vector map obtained by this method has the street number and area of ​​each block and the area of ​​each water body in the target study area. Then, the raster to vector tool is used to convert the raster data of the vegetation canopy into vector data.

[0058] The second step is to use the intersection tool to combine the vegetation canopy vector map with L5. Then, the attribute table of vegetation canopy height will have the L5 block number where each small vegetation canopy layer is located. Next, a new field, vegetation canopy area (VCarea), is added, and the computational geometry tool is used to calculate this field.

[0059] The third step is to use the union tool to combine the water vector map and the vegetation canopy vector map of the target study area. Then, the newly obtained union vector map is intersected with L5 to link the union vector map with the L5 block number. Similarly, the area of ​​each graphic (PSarea) of the intersected vector map is obtained.

[0060] The fourth step is to use a summary statistical tool to summarize the vector maps obtained in the second and third steps by summing the VCarea and PSAera using the L5 block number, and obtain an attribute table indexed by the L5 block number.

[0061] The fifth step involves using a tabular display tool to assign the average values ​​of the vegetation canopy raster data and building height raster data to the L5 street map. Then, the two attribute tables obtained in the fourth step are linked to the L5 street attribute table through the street number. This gives each street its own area, vegetation canopy height (VCH), building height (UBH), vegetation canopy area (VCarea), and permeable surface area (PSarea).

[0062] Step 6: Calculate the vegetation canopy density (VCdensity) and impermeable surface density (ISdensity) for each block, using the following formula:

[0063]

[0064] Finally, the resulting four-dimensional data attribute tables for each block are exported to a CSV file for subsequent clustering operations.

[0065] The clustering operation uses the K-Means algorithm, and the process is as follows:

[0066] Principal Component Analysis (PCA) was used to reduce the dimensionality of the data, retaining the first two principal components with scores of 0.73 and 0.23. This effectively removed redundancy between indicators and covered the main information of the source data. K-Means clustering was then performed on the dimensionality-reduced data. The elbow method was introduced to initially determine the number of clusters. Furthermore, the silhouette score and Calinski-Harabasz index were used as evaluation metrics for the K-Means algorithm. This step was implemented using ENVI and Python.

[0067] Compared to existing technologies, this invention achieves the following:

[0068] 1) Construction of a multi-source, high-resolution spatial structure index system:

[0069] For the first time, building height, vegetation canopy height, vegetation cover density, and impermeable surface density were incorporated into a unified urban morphology index system. Based on the fusion of remote sensing images, street view images, and POI data, high-precision extraction and normalization standard processing of two-dimensional and three-dimensional structural data were completed.

[0070] 2) Multi-scale street block spatial unit division for the main urban area:

[0071] A street-level classification algorithm based on Open Street Map (OSM) and administrative boundary data is proposed to achieve five-level block granularity division of urban space. It has good controllability and scale adaptability, and lays the spatial unit foundation for subsequent structure-thermal environment modeling.

[0072] 3) Street type identification combining principal component analysis (PCA) and K-Means clustering:

[0073] Based on the constructed multidimensional structural feature data, the main features are extracted through principal component analysis for dimensionality reduction. Then, the K-Means clustering algorithm is used to achieve automatic identification and spatial classification of six types of composite morphological blocks (CMBs), which has good interpretability and classification stability.

[0074] Compared to the fragmented processing of urban structure, remote sensing indicators, and cluster analysis in existing technologies, the significant advantage of this invention lies in its organic integration of high-resolution remote sensing extraction, spatial structure indicator construction, street block granularity division, and structure-thermal coupling analysis. This forms a fully integrated technology chain encompassing data acquisition, indicator system construction, street block unit division, classification and identification, and thermal effect analysis (it should be noted that this invention focuses on providing basic support for thermal effect analysis, such as data, indicators, and unit division, and does not improve the core logic of the thermal effect analysis algorithm or model itself). The modules support each other and work collaboratively, overcoming the limitations of traditional methods in structural dimension integration, scale matching, and thermal response mechanism modeling, demonstrating a high degree of integration and system innovation.

[0075] Addressing the highly spatial heterogeneous nature of urban thermal environments, this study resolves common issues in existing research regarding insufficient dimensional information, inconsistent classification scales, and weak coupling between structural types and thermal effects in street block division and structural thermal response modeling. Particularly in the multidimensional representation and analysis of building and vegetation structural combinations, the lack of a systematic and operational technical approach has resulted in limited accuracy in urban heat island identification, hindering refined governance. This paper proposes a systematic street block classification method by integrating high-resolution remote sensing, multi-source heterogeneous data fusion, principal component analysis, and clustering classification, providing fundamental support for subsequent thermal analysis. This approach forms a technical closed loop in two-dimensional, three-dimensional, and spectral multidimensional structural index extraction, multi-scale street block spatial division, and automatic street block type identification, effectively improving the quantitative analysis capability of urban structural thermal environment responses and demonstrating strong innovation, interpretability, and practical application value.

[0076] Additionally, it should be noted that in this application:

[0077] Building height data can be replaced by: reconstructing the three-dimensional building form using commercial stereoscopic images such as Cartosat and WorldView;

[0078] The height was estimated by combining open-source building footprint data with a building year estimation model.

[0079] Principal component analysis (PCA) can be replaced by:

[0080] Independent component analysis (ICA) is suitable for extracting statistically independent structural features;

[0081] Linear discriminant analysis (LDA) enhances inter-class separability under existing classification labels; t-SNE or UMAP are used for non-linear dimensionality reduction while preserving local structural relationships.

[0082] In addition to the K-Means algorithm, this invention can use the following alternative clustering methods to achieve the division of complex morphological blocks: DBSCAN (density clustering): suitable for data with significant spatial density differences; Spectral Clustering: suitable for clustering problems with non-convex shapes.

[0083] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for dividing urban blocks based on multidimensional canopy morphological features, characterized in that: Includes the following steps: Step 1: Collect and process vector boundary data of the target study area to obtain a vector boundary map of the target study area; Step 2: Collect and process data on vegetation canopy height, building height, vegetation density, and water area to obtain a vector map of water area, raster data of vegetation canopy, raster data of building height, and street zoning map of the target study area. Step 3: Obtain the five-level street block data from high to low and perform subsequent processing. The granularity of the higher-level street blocks is greater than that of the lower-level street blocks. Step 4: Perform dimensionality reduction analysis on building height, vegetation canopy height, impermeable surface density, and vegetation cover density, and then cluster the dimensionality reduction results. Step 3 also includes assigning values ​​to the blocks, the specific steps of which are as follows: Step 301: Import the water vector map, vegetation canopy raster data, building height raster data, street zoning map, and vector boundary map of the target study area into the geographic information system software; Step 302: Using the cropping tool, crop the street zoning map, the target study area water vector map, and the target study area vector boundary map respectively to obtain the street zoning map and the target study area water vector map at the corresponding level; use the raster to vector tool to convert the vegetation canopy raster data into vector data to obtain the vegetation canopy vector map. Step 303: Use the intersection tool to link the vegetation canopy vector map with the corresponding level of street zoning map, add a new field and calculate the vegetation canopy area; use the union tool to link the water body vector map with the vegetation canopy vector map, and use the intersection tool again to link the street zoning map to obtain the area of ​​the linked graphic; Step 304: Based on the street district number, use the summary statistics tool to summarize and statistically analyze the vegetation canopy area and the area of ​​the associated graphic, respectively; Step 305: Using a tabular display tool for zoning statistics, assign the average values ​​of the vegetation canopy raster data and building height raster data to the street zoning map, link the summary statistics results with the street zoning attribute table, and obtain an attribute table containing street area, vegetation canopy height, building height, vegetation canopy area, and permeable surface area. Step 306: Calculate the vegetation canopy density based on the vegetation canopy area and the street area, calculate the impermeable surface density based on the permeable surface area and the street area, and export the attribute table containing four-dimensional data for subsequent clustering.

2. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 1, characterized in that: In step 1, the vector boundary data of the target study area is obtained through a GIS platform, which includes, but is not limited to, at least one of Google Earth, ArcGIS, or QGIS. The methods for obtaining the vector boundary data of the target study area include outlining and exporting the polygonal boundary of the target study area using the spatial vector drawing tool of the GIS platform, or retrieving preset boundary data from an existing database.

3. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 1, characterized in that: The building height data is generated by fusing remote sensing imagery, street view images, and POI data using machine learning models or multimodal models, or obtained directly from existing databases.

4. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 1, characterized in that: The block data is generated based on road network data and administrative boundary data through a multi-level block division algorithm. The division algorithm uses at least one of the following indicators—area variation coefficient, shape index, or boundary consistency index—to compare and verify with official data, or directly obtains data from an existing database.

5. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 1, characterized in that: The vegetation canopy height data is generated by fusing satellite remote sensing data and field measurement data using a convolutional neural network model, or obtained directly from an existing database.

6. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 5, characterized in that: The formula for calculating the vegetation canopy density (VCdensity) is as follows: ； Where VCarea is the area of ​​the vegetation canopy in a certain block, and area is the area of ​​that block.

7. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 6, characterized in that: The formula for calculating the impermeable surface density ISdensity is as follows: ； Where PSarea is the permeable surface area of ​​a certain block, and area is the area of ​​that block.

8. The method for dividing urban blocks based on multidimensional canopy morphology features according to claim 7, characterized in that: The dimensionality reduction analysis uses principal component analysis. During the dimensionality reduction process, a preset number of principal components is retained, redundant indicators are removed, and the main information of the source data is preserved.

9. A method for dividing urban blocks based on multidimensional canopy morphology features according to claim 8, characterized in that: The clustering method used is K-Means clustering, and the elbow rule is introduced to initially determine the number of clusters. The clustering results are evaluated by combining the silhouette coefficient and the Calinski-Harabasz index.

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