A method for measuring the potential of irregular zero landscape for urban micro-updating

Abstract

The application discloses a kind of city micro-update-oriented abnormal zero landscape potential measurement method, it is related to the technical field of city micro-update evaluation.The present application comprises: obtaining multi-source spatial data and preprocessing;Adopting remote sensing-street view collaborative identification method identifies city abnormal zero object;Establish a standardized attribute library;Build the landscape potential index system containing four dimensions of spatial form availability, greening promotion potential, function service supplement potential and governance implementation constraint;Determine the index weight using entropy weight method, build city abnormal zero landscape potential index model for comprehensive measurement, combined with TOPSIS method for priority ranking;Adopting geographic detector identifies leading influence factor;According to the potential grade matching different micro-update strategies and output.The present application realizes the complete technical closed loop from automatic identification to update strategy output of city abnormal zero, and can be embedded in city update auxiliary decision platform.

Description

Technical Field

[0001] This invention relates to the field of urban micro-renewal assessment technology, specifically to a method for measuring the landscape potential of irregularly shaped land in urban micro-renewal. Background Technology

[0002] As my country's urban development shifts from incremental expansion to stock optimization, urban micro-renewal and refined governance have become important pathways to improve the quality of the living environment. In this process, numerous isolated plots of land scattered throughout densely built-up areas have gradually attracted attention. These spaces are typically small in size, fragmented in form, have irregular boundaries, ambiguous functions, and are often long-term idle or underutilized. They are widely distributed at road intersections, between buildings, at plot edges, in ancillary spaces of facilities, in areas where walls have been set back, and along the edges of infrastructure. Although their individual size is limited, they have potential value in supplementing public activity spaces, improving the local ecological environment, enhancing pedestrian connectivity, and strengthening the resilience of community services.

[0003] Existing research on urban irregular sites mainly focuses on case-based renewal design, empirical classification, and manual reconnaissance identification. Some studies introduce methods such as remote sensing interpretation, GIS spatial analysis, or questionnaires and interviews to describe spatial characteristics. However, from the perspective of the actual needs of urban micro-renewal, existing technologies still have significant shortcomings and face obvious limitations in the identification and evaluation of urban irregular sites. On the one hand, existing identification methods rely heavily on manual reconnaissance, single image interpretation, or empirical screening, which are inefficient and highly subjective, making it difficult to adapt to the needs of large-scale, dynamic, and batch-based urban renewal. On the other hand, existing evaluation systems often focus on single dimensions, such as site area, morphological regularity, green space, or accessibility, lacking a comprehensive coupled analysis of factors such as spatial morphology, service functions, vegetation ecology, and renewal needs, making it difficult to fully reflect the landscape potential of irregular sites.

[0004] Furthermore, existing technologies mostly focus on displaying identification results or static ranking, with insufficient connection between evaluation results and update strategies, making it difficult to directly serve planning, design, and governance decisions. Especially in urban micro-renewal practices, the real issues that need to be addressed are not only "where do irregular plots of land exist," but also key decision-making questions such as "which irregular plots are more worthy of priority renewal," "what factors drive their potential," and "what type of renewal strategy should be adopted."

[0005] Therefore, this invention proposes a method for measuring the landscape potential of irregular and isolated land areas for urban micro-renewal. Summary of the Invention

[0006] The purpose of this invention is to provide a method for measuring the landscape potential of irregular and isolated land in urban micro-renewal, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for measuring the landscape potential of anomalous land in urban micro-renewal, comprising the following steps: Acquire and preprocess multi-source spatial data within the study area to construct a basic urban spatial database. The multi-source spatial data includes remote sensing image data, street view image data, road network data, building outline data, land parcel boundary data, point of interest data, population activity data, vegetation index data, public service facility data, and current land use data. Based on the urban spatial database, spatial overlay analysis is performed on remote sensing image data, street view image data, road network data, building outline data, and land parcel boundary data to obtain candidate spatial units. The candidate spatial units are then filtered according to preset morphological rules and combined with the street view image data for collaborative recognition to identify a set of urban odd-numbered land objects. Based on the aforementioned urban irregular land objects, a standardized attribute library is established, and the spatial boundary field, morphological feature field, street view perception field, greening basic field, functional service field, and governance constraint field corresponding to each urban irregular land object are extracted. Based on each field, the attribute vector of the corresponding urban irregular land object is constructed. Based on the attribute vector, an urban aberration landscape potential index system is constructed. Each evaluation index in the urban aberration landscape potential index system is standardized and the weight of each evaluation index is determined. The urban aberration landscape potential index system includes four evaluation dimensions: spatial morphology usability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints. Based on standardized evaluation indicators and corresponding weights, a landscape potential index model for urban odd-shaped land is constructed to calculate the landscape potential index corresponding to each urban odd-shaped land object and the contribution rate of each evaluation dimension. Based on the landscape potential index and the contribution rate of each evaluation dimension, the irregular land objects in each city are comprehensively ranked to determine the dominant potential type and update priority of the irregular land objects in each city. Based on the landscape potential index, renewal priority, dominant potential type, and governance implementation constraints, the potential levels of irregular land parcels in each city are classified, and corresponding urban micro-renewal strategies are generated.

[0008] Furthermore, coordinate unification, spatial registration, format conversion, noise removal, missing value processing, and scale matching are performed on the remote sensing image data, street view image data, road network data, building outline data, plot boundary data, point of interest data, population activity data, vegetation index data, public service facility data, and current land use data to obtain a unified spatial reference urban spatial basic database.

[0009] Furthermore, the set of irregular urban land parcels was identified, specifically including: Based on the remote sensing image data, land cover information, vegetation index information, and candidate spatial boundary information are extracted; Spatial overlay analysis is performed by combining road network data, building outline data, and land parcel boundary data to obtain candidate spatial units; The candidate spatial units are filtered based on area thresholds, morphological rules, and road adjacency rules; The idle status, interface closure, lack of greenery, and accessibility characteristics of candidate spatial units are identified and screened using the street view image data, and a set of urban irregular land objects is obtained by combining the identification with manual verification.

[0010] Furthermore, based on the aforementioned urban irregular land objects, a standardized attribute library is established, specifically including: Establish a unique identifier for each irregular land parcel in each city; Extract the plot number, plot coordinates, area, perimeter, and adjacency relationship to form a spatial boundary field; The morphological feature fields are formed by extracting morphological complexity, land use angle, spatial openness, and road adjacency. Green view rate, interface closure, accessibility, dwell time, and idle visibility are extracted to form street view perception fields; Extract vegetation coverage, the proportion of exposed hard paving, tree canopy coverage, and the remaining greening improvement to form basic greening fields; Extract functional service fields by considering factors such as the gap in surrounding public service facilities, population activity intensity, pedestrian accessibility, and the supply and demand relationship of community public spaces. Extract fields for governance constraints based on ownership complexity, construction accessibility, current occupancy level, safety risks, and implementation difficulty; Generate attribute vectors for the corresponding city's odd-numbered land objects based on each field.

[0011] Furthermore, the four evaluation dimensions of the urban asymmetric land landscape potential index system—spatial morphology usability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints—specifically include: The evaluation dimensions for spatial morphology availability include area suitability, morphological complexity, road adjacency, spatial openness, and slow-moving accessibility evaluation indicators. The evaluation dimensions for greening enhancement potential include current vegetation coverage, green visibility rate, hard paving ratio, and greening enhancement capacity evaluation indicators. The evaluation dimensions for the potential to supplement functional services include the intensity of surrounding population activity, gaps in public service facilities, degree of insufficient coverage of service radius, and evaluation indicators for the demand for available living space. The evaluation dimensions for governance implementation constraints include evaluation indicators for ownership complexity, current occupancy intensity, construction accessibility, safety risks, and maintenance difficulty.

[0012] Furthermore, the evaluation indicators in the urban irregular land landscape potential index system are standardized, and the weights corresponding to each evaluation indicator are determined, specifically including: Positive and negative indicators are determined based on the attributes of each evaluation indicator. Positive indicators are those with higher values, indicating higher potential, while negative indicators are those with higher values, indicating lower potential or stronger constraints. Positive indicators are standardized using the following formula: Negative indicators are standardized using the following formula: In the formula, Indicates the first The oddly positioned object in the first Standardized values ​​for each indicator This represents the original index value. and They represent the first The maximum and minimum values ​​of each indicator across all samples; The weight of each evaluation index in each sample is calculated based on the standardized evaluation index matrix: The objective weights of each evaluation index are determined using the entropy weight method: Calculate the first The first indicator in the The proportion in each sample: Among them, when At that time, it can be made ; Calculate the first Information entropy of each indicator: in, Calculate the first Coefficient of variation for each indicator: Finally, we get the number. Weights of each indicator: In the formula, The total number of urban irregular land parcels participating in landscape potential measurement. The total number of evaluation indicators; Indicates the first The oddly positioned object in the first Standardized values ​​for each indicator Indicates the first The oddly positioned object in the first The proportion of each indicator Indicates the first The sum of the standardized values ​​of each indicator across all irregular objects; Indicates the first Information entropy of each indicator This represents the normalization coefficient used in entropy calculation; Indicates the first The coefficient of difference of each indicator Indicates the first The weight of each evaluation indicator, This represents the sum of the coefficients of difference for all evaluation indicators; The greater the difference in a certain indicator among different distorted objects, the greater its information entropy. The smaller the coefficient of variation, the higher the difference. The larger the value, the higher the corresponding weight. The higher the value, the stronger the indicator is at distinguishing the landscape potential of different aberration sites.

[0013] Furthermore, based on the standardized evaluation indicators and corresponding weights, a landscape potential index model for urban irregular land is constructed. This model calculates the landscape potential index for each urban irregular land object and the contribution rate of each evaluation dimension, specifically including: Spatial morphology usability is calculated based on the standardized evaluation indicators and their corresponding weights. : In the formula, Indicates the first The first irregular object in the spatial morphology usability dimension A standardized index value, Corresponding indicator weights The number of indicators representing the availability of spatial form; Calculate the potential for greening improvement : in, Indicates the first The first dimension of greening enhancement potential of individual odd-shaped land units A standardized index value, Corresponding indicator weights This indicates the number of indicators under the dimension of potential for greening improvement; Computing capabilities can fill gaps in service potential. : in, Indicates the first The first dimension of functional service supplementation potential of individual anomalous land units A standardized index value, Corresponding indicator weights This indicates the number of metrics under the dimension of functional service complementarity potential; Constraints for Implementing Computational Governance : in, Indicates the first The first irregular land unit under the constraint of governance implementation dimension A standardized index value, Corresponding indicator weights This indicates the number of indicators under the governance implementation constraint dimension; Constraints on governance implementation Convert into an actionable contribution : Calculate the landscape potential index corresponding to irregular and barren land in each city: in, Indicates the first Landscape potential index of isolated urban land parcels; Indicates the usability of spatial form; This indicates the potential for improved greening; This indicates the potential for functional services to fill gaps; This indicates that governance is subject to constraints. Indicates a contribution that can be implemented; , , , These are represented as the weights of the four primary dimensions, satisfying: when The larger the value, the more suitable this irregular land object is to be a priority object for urban micro-renewal; Calculate the contribution rate of each dimension: in, Indicates the first The urban odd-numbered land objects in the first Scores on each dimension .

[0014] Furthermore, based on the landscape potential index and the contribution rate of each evaluation dimension, the irregular land parcels in each city are comprehensively ranked to determine the dominant potential type and update priority corresponding to each irregular land parcel, specifically including: Construct a weighted standardization matrix based on the standardized evaluation indicators and their corresponding weights: Determine the positive and negative ideal solutions: in, , ; Calculate the first one respectively The distance between an odd-shaped urban land object and the positive and negative ideal solutions: (8-5) Finally, the TOPSIS proximity coefficient is obtained: in, , The higher the value, the closer the irregular land is to the ideal regeneration state, and the higher its landscape potential. and They are used together to update the priority sorting, where Used to explain landscape potential structure Used to enhance the stability and closeness of comprehensive sorting; The comprehensive ranking and update priority of irregular land parcels in each city are determined by combining the landscape potential index and TOPSIS proximity.

[0015] Furthermore, the dominant potential types corresponding to the irregular land parcels in each city were identified, including: Calculate the contribution rate of each evaluation dimension to the landscape potential index; The evaluation dimension with the highest contribution rate is determined as the dominant potential type of the corresponding urban asymmetric land objects; among them, when the contribution rate of functional service supplementation potential is the highest, it is determined as functional service supplementation type; when the contribution rate of greening enhancement potential is the highest, it is determined as greening enhancement type; and when the contribution rate of spatial form usability is the highest, it is determined as spatial optimization type.

[0016] Furthermore, based on the aforementioned landscape potential index, renewal priority, dominant potential type, and governance implementation constraints, the potential levels of irregular plots in each city are classified, and corresponding urban micro-renewal strategies are generated, as follows: Based on the Landscape Potential Index, TOPSI Proximity, and Dominant Constraint Factors, urban irregular sites are divided into high-potential, medium-potential, and low-potential zones, and differentiated micro-renewal strategies are implemented accordingly. Potential partitions are represented as: in, Indicates the first The potential level of isolated land parcels in a city and Indicates the partition threshold; A geographic detector is introduced to identify the dominant influencing factors and their interactions. When using a geographic detector, the single-factor explanatory power statistic is: in, Represents a hierarchical unit. Indicates the number of layers. Indicates the first Number of layer samples This indicates the total number of samples in the entire region. Indicates the first Layer sample variance This represents the variance of the entire region's samples; Based on potential partitioning, strategy matching is performed according to the dominant potential type and dominant limiting factor: in, Indicates the first Micro-update strategy types for irregularly sized land parcels in a city. Indicates potential level, This indicates the dominant dimension with the highest contribution rate. This indicates that governance is subject to constraints.

[0017] This invention has at least the following beneficial effects: 1. This invention, through a remote sensing-street view collaborative recognition mechanism, can simultaneously utilize spatial morphological information at the top-down scale and street interface information from the pedestrian's perspective, effectively overcoming the problem that single remote sensing images are difficult to identify micro-spaces such as building setbacks, street and alleyway gaps, and remaining land attached to infrastructure.

[0018] 2. This invention constructs a standardized database of odd-shaped land attributes and the URRI Urban Odd-Shaped Land Landscape Potential Index model, incorporating spatial morphology availability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints into a unified calculation framework, thereby transforming landscape potential evaluation from empirical judgment to a calculable, comparable, and interpretable quantitative measure.

[0019] 3. This invention, through the explanation of dominant factors and the strategy matching mechanism, further transforms the evaluation results into potential zoning, renewal priorities and differentiated strategy recommendations, which can directly serve the construction of urban micro-renewal project database, the optimization of community public spaces and the decision-making on the reuse of inefficient spaces.

[0020] 4. The system modules of this invention have strong scalability and deployability, and can be embedded in landscape architecture digital platforms, urban renewal auxiliary decision-making systems, or land space governance platforms. Furthermore, the indicator system, weighting methods, and strategy types can be adjusted according to the data conditions and update objectives of different cities, and it has good prospects for promotion and application.

[0021] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the overall process of the method described in this invention; Figure 2 This is a schematic diagram of the urban irregular land object identification results in this invention; Figure 3 This is the overall flowchart of the calculation of the landscape potential index of anomalous land, the entropy weight method weight calculation, the comprehensive ranking and update priority, and the calculation of the dominant limiting factor of the present invention. Figure 4 This is a schematic diagram of the prediction results of the urban aberration landscape potential index model according to the present invention; Figure 5 This is a schematic diagram of the potential partitioning and micro-update strategy matching output of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Example 1: Explanation of related terms: Urban odd-numbered plots refer to small, irregularly shaped, functionally ambiguous, inefficiently used, or long-neglected surplus urban spaces formed within the urban built environment due to factors such as planning adjustments, road construction, land boundary division, building setbacks, remaining infrastructure ancillary spaces, fragmented ownership, or spatial function mismatch. Typical types include roadside plots, alleyway gaps, building setbacks, surplus land ancillary to facilities, walled boundary spaces, and inefficient, idle micro-spaces.

[0026] Landscape potential: refers to the comprehensive ability of irregular urban land parcels to be transformed into public landscape resources, community service spaces, or ecological restoration nodes under existing spatial conditions, environmental foundations, and governance constraints through morphological optimization, greening enhancement, functional supplementation, slow-traffic connectivity, integration of public activities, and low-intervention renewal.

[0027] Urban micro-renewal refers to the gradual, low-cost, and refined spatial improvement activities carried out on inefficient, scattered, marginalized, or functionally deficient small spaces within urban built-up areas without large-scale demolition and reconstruction. It emphasizes small-scale intervention, activation of existing spaces, and improvement of community life quality.

[0028] Digital Intelligence Measurement: An intelligent evaluation method that integrates big data, artificial intelligence and geographic information systems to automatically identify, perceive the status and quantify the performance of spatial objects.

[0029] Urban Awkward Land Landscape Potential Index Model: This model is a comprehensive measurement model for urban micro-renewal scenarios, built around the usability of spatial form, potential for greening enhancement, potential for functional service supplementation, and constraints on governance implementation. It is used to quantify the landscape potential, renewal priority, and strategy adaptation direction of urban awkward land.

[0030] Please see Figure 1 This invention provides a technical solution: a method for measuring the landscape potential of anomalous land in urban micro-renewal, comprising the following steps: S1. Acquire and preprocess multi-source spatial data within the study area to construct a basic urban spatial database. The multi-source spatial data includes remote sensing imagery, street view imagery, road network data, building outline data, land parcel boundary data, points of interest data, population activity data, vegetation index data, public service facility data, and current land use data, specifically including: (1-1) Acquire high-resolution remote sensing images, street view images, road networks, plot boundaries, building outlines, POIs, population heat maps, vegetation indices, public service facilities and current land use data in the study area, and perform coordinate unification, spatial registration, format conversion, noise removal, missing value processing and scale matching on the above multi-source spatial data to form a basic database for urban aberration identification and landscape potential measurement. (1-2) Suppose that there are a total of Select from candidate spatial units. Using evaluation metrics, construct the original data matrix: in, Indicates the first The spatial unit in the first The original values ​​of each indicator Indicates the number of candidate spatial units. Indicates the number of evaluation indicators; (1-3) To achieve collaborative representation of remote sensing data and street view data, the first... Constructing a multi-source feature vector from candidate spatial units: in, This represents the land cover, vegetation index, and morphological boundary features extracted from remote sensing images. This represents the interface visibility, green view rate, idle status, and accessibility features extracted from street view images. It represents basic spatial features such as roads, land parcel boundaries, and building outlines. Indicates the characteristics of POI and public service facilities. Indicates population thermal or activity intensity characteristics; The purpose of this step is to avoid the limitations of a single data source in identifying and assessing the potential of anomalous land, so that subsequent evaluations can reflect not only spatial morphology, but also street interface, usage needs, and micro-update conditions. In this embodiment, the study scope includes high-density old urban communities, rail stations, and riverside block complexes. Data such as 0.5m high-resolution remote sensing images, street view image sampling points, road centerlines, building outlines, plot boundaries, points of interest, population heat maps, vegetation indices, and current land use are acquired. The study area is divided into a 100m x 100m regular network, and the above data are unified to the same projection coordinate system. Spatial registration, format conversion, missing value handling, and scale matching are completed. After data cleaning, a total of 186 candidate spatial units are formed, including road corner spaces, building setback spaces, street and alleyway gap spaces, remaining spaces attached to facilities, and inefficient and idle hard spaces, which are used for subsequent identification of irregular land and measurement of landscape potential. Table 1: Results of Multi-Source Data Input and Standardization Processing S2. Based on the urban spatial database, spatial overlay analysis is performed on remote sensing image data, street view image data, road network data, building outline data, and land parcel boundary data to obtain candidate spatial units. These candidate spatial units are then filtered according to preset morphological rules and collaboratively identified using street view image data to obtain a set of urban irregular land parcels. This step specifically includes: (2-1) Based on the preprocessed multi-source urban spatial data, a method combining remote sensing-street view collaborative identification, spatial overlay analysis, morphological rule screening and manual verification is used to identify candidate urban odd and even land areas; First, remote sensing imagery and building, road, and plot boundary data are used to extract potential inefficient spaces, including roadside plots, building setback plots, street and alleyway gaps, remaining plots attached to facilities, and underutilized hard vacant land. Secondly, street view images are used to identify the visible vacancy status, interface closure degree, lack of greenery, and accessibility characteristics of candidate spaces; Finally, objects that do not conform to the definition of malformed land are filtered out through spatial rules and manual verification; (2-2) Let the candidate spatial unit be Its area is The morphological complexity is The degree of road adjacency is Accessibility is The current utilization intensity is The result of the aberration identification can be expressed as: in, Indicates the first One candidate space was identified as an irregular urban site. This indicates that the candidate space does not belong to the urban irregular land area; and These represent the lower and upper limits of the area of ​​irregularly shaped land, respectively. , , , These represent the thresholds for morphological complexity, road adjacency, accessibility, and utilization intensity, respectively. (2-3) The morphological complexity can be expressed by the following formula: in, Perimeter of the candidate spatial plot, The area of ​​the candidate spatial plot. The larger the index value, the more irregular the boundaries of the plot, and the more it conforms to the irregular morphological characteristics of odd-shaped land. (2-4) The degree of road adjacency can be expressed by the following formula: in, Indicates the first The length of each candidate space adjacent to the boundary of a road or street. This represents the total perimeter of the candidate space, an indicator used to characterize the likelihood that anomalous areas will be perceived and updated by the public. (2-5) Accessibility can be expressed by the following formula: in, Indicates the first The distance from each candidate space to the nearest accessible road, street, or public passageway. The larger the value, the better the conditions for entering the space, and the stronger the foundation for micro-update utilization; In this embodiment, a distorted zero-area identification rule is applied to 186 candidate spatial units for screening. An area threshold is set as follows: The morphological complexity threshold is The road adjacency threshold is The accessibility threshold is After initial screening by remote sensing, street view verification, and manual verification, 68 urban odd-numbered land objects were finally identified. Table 2: Examples of Candidate Identification Results for Urban Awkward Land Plots As shown in Table 2, L01, L02, L03 and L04 meet the requirements for identifying irregular land in terms of area, morphological complexity, road adjacency and accessibility, and are therefore included in the subsequent landscape potential measurement; L05 is excluded due to low accessibility and insufficient road adjacency; L06 is not a priority measurement object because its current utilization intensity is too high and the possibility of micro-renewal implementation in the short term is low. S3. Based on urban irregular land parcels, establish a standardized attribute library, extract the spatial boundary field, morphological feature field, street view perception field, greening basic field, functional service field, and governance constraint field corresponding to each urban irregular land parcel, and construct the attribute vector of the corresponding urban irregular land parcel based on each field. This step specifically includes: (3-1) Establish a standardized attribute database for the identified urban irregular land objects. This attribute database includes spatial boundary fields, morphological feature fields, street view perception fields, greening basic fields, functional service fields, and governance constraint fields; The spatial boundary fields include plot number, plot coordinates, area, perimeter, and adjacency relationships. Morphological characteristic fields include morphological complexity, land use angle, spatial openness, and road adjacency. Street view perception fields include green view rate, interface enclosure, accessibility, dwelling potential, and vacancy visibility. Greening infrastructure fields include vegetation coverage, proportion of exposed hard paving, canopy coverage, and greening improvement potential. Functional service fields include gaps in surrounding public service facilities, population activity intensity, pedestrian accessibility, and the supply and demand relationship of community public spaces. Governance constraint fields include ownership complexity, construction accessibility, current occupancy level, safety risks, and implementation difficulty. (3-2) No. The attribute vector of a malformed unit can be represented as: in, This represents a set of indicators related to the usability of spatial form. This represents a set of indicators related to the potential for greening improvement. This represents a set of indicators related to the potential of functional services to supplement service offerings. This represents the set of indicators related to the constraints of governance implementation; In this embodiment, a standardized attribute library is established for 68 irregular land parcels, forming a sample data structure of "one file per parcel". The attribute library includes spatial boundary fields, morphological feature fields, street view perception fields, greening basic fields, functional service fields, and governance constraint fields; Table 3: Examples of Standardized Offset Attribute Database Fields The values ​​in Table 3, after normalization, can be included in the subsequent indicator system. Taking L01 as an example, the surrounding population activity intensity and public service gap are relatively high, while the current occupancy intensity and ownership complexity are relatively low, indicating that it has a good foundation for micro-renewal intervention. Although L07 has high surrounding demand, its ownership complexity and current occupancy intensity are relatively high, which will reduce its comprehensive potential in subsequent measurement due to the reduced governance implementation constraint score.

[0031] S4. Construct an index system for the landscape potential of urban irregular and barren lands based on attribute vectors. This step specifically includes: (4-1) Based on the standardized aberrant land attribute database, a landscape potential index system for urban micro-renewal is constructed. This index system does not adopt the general ecological value or wetland value evaluation logic, but focuses on the questions of "whether it can be changed, whether it is worth changing, and how it is appropriate to change" in urban micro-renewal. Specifically, it includes four primary dimensions.

[0032] (4-2) Construct a landscape potential indicator system, including four primary dimensions: First, the usability of spatial form , can be represented as: in, Indicates the first The first irregular land unit in the dimension of spatial morphological usability A standardized index value, Corresponding indicator weights The number of indicators representing the availability of spatial form; This dimension is used to reflect the basic spatial conditions of irregular land, including area suitability, morphological complexity, road adjacency, spatial openness, and slow-moving accessibility; Second, greening enhances potential. , can be represented as: in, Indicates the first The first dimension of greening enhancement potential of individual odd-shaped land units A standardized index value, Corresponding indicator weights This indicates the number of indicators under the dimension of potential for greening improvement; This dimension is used to reflect the possibility of landscape improvement in irregular areas through vegetation replanting, reduction of hard paving, increase of tree canopy and ecological micro-repair, including current vegetation coverage, green view rate, hard paving ratio and greening improvement margin; Third, functional services can fill potential gaps. , can be represented as: in, Indicates the first The first dimension of functional service supplementation potential of individual anomalous land units A standardized index value, Corresponding indicator weights This indicates the number of indicators under the dimension of functional service supplementation potential; this dimension is used to reflect the ability of asymmetrical land to supplement the shortcomings of surrounding community public space, recreational facilities, chronic connectivity and public services, including the intensity of surrounding population activity, gaps in public service facilities, degree of insufficient service radius coverage and demand for dwelling space; Fourth, constraints on governance implementation , can be represented as: in, Indicates the first The first irregular land unit under the constraint of governance implementation dimension A standardized index value, Corresponding indicator weights This indicates the number of indicators under the governance implementation constraint dimension; This dimension is used to reflect the constraints faced when transforming irregular land into micro-renewal projects, including ownership complexity, current occupancy intensity, construction accessibility, safety risks, and maintenance difficulty. Unlike the first three dimensions, governance implementation constraints are the reverse dimension, with higher values ​​indicating greater implementation difficulty. S5. Standardize the evaluation indicators in the urban anomalous land landscape potential indicator system. This step specifically includes: (5-1) Since the dimensions and directions of each indicator are different, each indicator needs to be standardized before comprehensive measurement; For positive indicators, i.e., those with higher values ​​indicating greater potential, the formula is: For negative indicators, i.e., those with larger values ​​indicating lower potential or stronger constraints, the formula is used: in, Indicates the first The odd-shaped unit in the first Standardized values ​​for each indicator This represents the original index value. and They represent the first The maximum and minimum values ​​of each indicator across all samples; For governance implementation constraint indicators, first perform positive normalization based on the original constraint strength to obtain... Then, in the urban anomalous land resilience index model, the following is adopted: Transform the approach into an actionable contribution; (5-2) After standardization, a dimensionless matrix is ​​obtained. All indicator values ​​are located at interval; S6. Calculate the weights of each evaluation indicator in the urban anomalous land landscape potential indicator system. This step specifically includes: (6-1) To reduce subjective bias caused by human weighting bias, the entropy weight method is used to determine the objective weight of each evaluation indicator; (6-2) First, calculate the first... The first indicator in the The proportion in each sample: Among them, when At that time, it can be made ; (6-3) Then, calculate the first... Information entropy of each indicator: in, (6-4) Calculate the next step. Coefficient of variation for each indicator: (6-5) Finally, we get the first... Weights of each indicator: In the formula, The total number of urban irregular land parcels participating in landscape potential measurement. The total number of evaluation indicators; Indicates the first The oddly positioned object in the first Standardized values ​​for each indicator Indicates the first The oddly positioned object in the first The proportion of each indicator Indicates the first The sum of the standardized values ​​of each indicator across all irregular objects; Indicates the first The information entropy of an indicator is used to reflect the degree of dispersion of that indicator. The normalization coefficient in entropy calculation is used to normalize the information entropy. The value remains Within the range; Indicates the first The coefficient of difference of each indicator Indicates the first The weight of each evaluation indicator, This represents the sum of the difference coefficients of all evaluation indicators. The greater the difference of a certain indicator among different abnormal objects, the greater its information entropy. The smaller the coefficient of variation, the higher the difference. The larger the value, the higher the corresponding weight. The higher the value, the stronger the indicator's ability to differentiate the landscape potential of different aberration sites; In this embodiment, the indicators of 68 irregular land samples were standardized, and the entropy weight method was used to determine the indicator weights. The calculation results show that, in the spatial morphology usability dimension, area suitability and road adjacency have relatively high weights; in the greening enhancement potential dimension, the proportion of hard paving and the greening enhancement margin have relatively high weights; in the functional service supplementation potential dimension, population activity intensity and public service gap have relatively high weights; and in the governance implementation constraint dimension, ownership complexity and current occupancy intensity have relatively high weights. Table 4: Examples of Weights for Landscape Potential Measurement Indicators S7. Based on the standardized evaluation indicators and corresponding weights, construct a landscape potential index model for urban irregular land, and calculate the landscape potential index and contribution rate of each evaluation dimension for each urban irregular land object. This step specifically includes: (7-1) After completing the standardization of indicators and the calculation of weights, a landscape potential index model for urban odd-shaped land is constructed to comprehensively measure the micro-renewal potential of each odd-shaped land unit. (7-2) The expression for the urban anomalous land landscape potential index model is as follows: in, Indicates the first Landscape potential index of urban anomalous land units; Indicates the usability of spatial form; This indicates the potential for improved greening; This indicates the potential for functional services to fill gaps; This indicates that governance is subject to constraints. Indicates a contribution that can be implemented; , , , These are represented as the weights of the four primary dimensions, satisfying: when The larger the value, the better the overall performance of the irregular land in terms of space utilization, greening improvement, functional supplementation and implementation feasibility, and the more suitable it is to become a priority target for urban micro-renewal. (7-3) To further highlight the dominant potential type, the contribution rate of each dimension can be calculated: in, Indicates the first The urban odd-numbered land units in the first Scores on each dimension When a certain dimension has the highest contribution rate, it can be identified as the dominant update direction for that anomalous area; In this embodiment, the weights of the first-level dimensions are set as follows: spatial shape usability. Greening enhances potential Functional services fill potential gaps Governance implementation constraints .

[0033] Table 5: Examples of Landscape Potential Sub-items and URRI Index for Awkward Land As shown in Table 5, L01 has the highest URRI index due to its better spatial morphology, higher gap in surrounding functional services, and lower governance constraints, making it suitable as a priority for renewal. Although L03 has a smaller spatial scale, it has high potential for greening improvement and can be used as a target for ecological micro-remediation or supplementary greening of streets and alleys. Although L07 has a strong demand for greening improvement, it has high constraints on governance implementation. Therefore, ownership coordination and safety rectification should be prioritized before subsequent renewal.

[0034] S8. Based on the landscape potential index and the contribution rate of each evaluation dimension, comprehensively rank the irregular land parcels in each city, and determine the dominant potential type and update priority corresponding to the irregular land parcels in each city. This step specifically includes: (8-1) To enhance the comparability between different irregular land units, after obtaining the weights, the TOPSIS method is used to calculate the urban irregular land landscape potential mapping progress. (8-2) First, construct the weighted normalization matrix: (8-3) Determine the positive and negative ideal solutions: in, , (8-4) Calculate the first one respectively Distance between each city's odd zero-land unit and the positive and negative ideal solutions: (8-5) The final proximity coefficient is obtained: in, , The higher the value, the closer the irregular land is to the ideal regeneration state, and the higher its landscape potential. and They are used together to update the priority sorting, where Used to explain landscape potential structure Used to enhance the stability of comprehensive sorting; In this embodiment, TOPSIS comprehensive sorting is performed on 68 odd zero land objects, and URRI index and TOPSIS proximity are used together to update priority judgment; Table 6: Examples of Combined Ranking Results of Abnormal Zero-Location URRI Index and TOPSIS S9. Based on the landscape potential index, renewal priority, dominant potential type, and governance implementation constraints, classify the potential levels of irregular and isolated land parcels in each city and generate corresponding urban micro-renewal strategies. This step specifically includes: (9-1) In order to further explain the reasons for the spatial differentiation of the landscape potential of anomalous and zero-land areas, a geographic detector was introduced to identify the dominant influencing factors and their interactions; (9-2) When using a geographic detector, the single-factor explanatory power statistic is: in, Represents a hierarchical unit. Indicates the number of layers. Indicates the first Number of layer samples This indicates the total number of samples in the entire region. Indicates the first Layer sample variance This represents the variance of the entire region sample. The larger the value, the stronger the explanatory power of the factor for the spatial differentiation of the landscape potential of anomalous zero areas; In this embodiment, the URRI index is used as the dependent variable, and population activity intensity, public service gap, road adjacency, hard paving ratio, green view insufficiency, ownership complexity and current occupancy intensity are used as explanatory variables. The results show that population activity intensity, public service gap and ownership complexity are the main factors affecting the differentiation of landscape potential in odd-shaped land. Table 7: Examples of Dominant Influence Factor Detection Results Furthermore, factor interaction detection shows that the explanatory power of the interaction between population activity intensity and public service facility gap is 0.587, which is higher than the explanatory power of any single factor, indicating that irregular land in areas with overlapping high demand and high gap is more suitable as a priority target for urban micro-renewal; the explanatory power of the interaction between ownership complexity and current occupancy intensity is 0.462, indicating that governance constraints will significantly affect the possibility of irregular land being transformed from potential space into implemented projects. (9-3) Based on the URRI index, TOPSI proximity and dominant limiting factors, urban irregular land is divided into high-potential areas, medium-potential areas and low-potential areas, and a differentiated micro-renewal strategy is implemented.

[0035] (9-4) Potential partitioning can be represented as: in, Indicates the first The potential level of an isolated urban land unit. and This represents the zoning threshold. The zoning threshold is set using the natural breakpoint method and planning management requirements.

[0036] In this embodiment, the natural breakpoint method is used to determine the threshold. , Of the 68 irregular land parcels, 18 were high-potential (26.47%), 31 were medium-potential (45.59%), and 19 were low-potential (27.94%).

[0037] Table 8: Examples of Statistical Results for Landscape Potential Zoning of Awkward Areas (9-5) To further highlight the dominant potential type, the contribution rate of each dimension can be calculated: in, Indicates the first The urban odd-numbered land units in the first Scores on each dimension When a certain dimension has the highest contribution rate, it can be identified as the dominant update direction for that anomalous area; (9-6) Based on potential partitioning, further strategy matching is performed according to the dominant potential type and dominant limiting factor: in, Indicates the first Micro-renewal strategy types for irregularly sized urban land units Indicates potential level, This indicates the dominant dimension with the highest contribution rate. This indicates that governance is subject to constraints. Differentiated update strategies are applied based on different potential levels. High-potential areas are prioritized for comprehensive enhancement strategies, suitable for introducing pocket parks, community activity points, pedestrian nodes, and supplementary public service facilities. Medium-potential areas are limited to functional supplementation strategies, suitable for street corner rest areas, pedestrian connections, interface optimization, greening replanting, and the provision of rest facilities. Low-potential areas are prioritized for low-intervention repair strategies, suitable for environmental remediation, ecological micro-repair, security cleanup, boundary beautification, and temporary use. In this embodiment, a matching table for micro-renewal strategies for urban irregular land is formed based on the URRI index, TOPSIS ranking, dominant factor interpretation, and potential zoning results. Table 9: Examples of Matching Results for Micro-Update Strategies for Aberration-Based Locations In this embodiment, 68 irregular land parcels were identified and zoning and strategy matching were completed. High-potential areas were mainly distributed around rail stations, areas with significant gaps in community public services, and corner spaces at road intersections; medium-potential areas were mainly distributed at the edges of infrastructure, under bridges, and along wall boundaries; low-potential areas were mainly constrained by factors such as complex ownership, existing occupancy, and safety risks. These results can serve as a preliminary decision-making basis for screening urban micro-renewal projects, selecting pocket park sites, improving street and alleyway environments, and supplementing community public spaces.

[0038] Through the above steps, this invention forms a complete technical closed loop from remote sensing-street view collaborative identification, construction of a database of odd-numbered land attributes, evaluation of urban odd-numbered land landscape potential, interpretation of dominant factors, to micro-renewal strategy zoning and matching implementation.

[0039] Example 2: This embodiment provides a landscape potential measurement system for irregularly shaped land in urban micro-renewal, used to implement the landscape potential measurement method for irregularly shaped land in urban micro-renewal described in Embodiment 1, including: The database construction module is used to acquire and preprocess multi-source spatial data within the study area to build a basic urban spatial database. The multi-source spatial data includes remote sensing image data, street view image data, road network data, building outline data, land parcel boundary data, point of interest data, population activity data, vegetation index data, public service facility data, and current land use data. The urban odd-numbered land object set identification module is used to perform spatial overlay analysis on remote sensing image data, street view image data, road network data, building outline data and land parcel boundary data based on the urban spatial basic database to obtain candidate spatial units. The candidate spatial units are then filtered according to preset morphological rules and combined with street view image data for collaborative identification to obtain the urban odd-numbered land object set. The standardized attribute library construction module is used to build a standardized attribute library based on urban odd-numbered land objects. It extracts the spatial boundary field, morphological feature field, street view perception field, greening basic field, functional service field and governance constraint field corresponding to each urban odd-numbered land object, and constructs the attribute vector of the corresponding urban odd-numbered land object based on each field. The weight calculation module is used to construct an urban aberration landscape potential index system based on attribute vectors, standardize each evaluation index in the urban aberration landscape potential index system, and determine the weight of each evaluation index. The urban aberration landscape potential index system includes four evaluation dimensions: spatial morphology usability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints. The model building module is used to construct a landscape potential index model for urban irregular land based on standardized evaluation indicators and corresponding weights, and to calculate the landscape potential index and contribution rate of each evaluation dimension for each urban irregular land object. The comprehensive ranking module is used to comprehensively rank the irregular land objects in each city based on the landscape potential index and the contribution rate of each evaluation dimension, and determine the dominant potential type and update priority of the irregular land objects in each city. The micro-renewal strategy generation module is used to classify the potential levels of irregular land parcels in each city based on landscape potential index, renewal priority, dominant potential type and governance implementation constraints, and generate corresponding urban micro-renewal strategies.

[0040] Example 3: The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts the method for measuring the landscape potential of anomalous land for urban micro-renewal described in Embodiment 1.

[0041] It should be noted that a terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0042] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0043] Example 4: The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the method for measuring the landscape potential of anomalous land for urban micro-renewal described in Embodiment 1.

[0044] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0045] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," 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 this application. 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.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of the claims of this application.

Claims

1. A method for measuring the landscape potential of asymmetrical plots in urban micro-renewal, characterized in that, Includes the following steps: Acquire and preprocess multi-source spatial data within the study area to construct a basic urban spatial database. The multi-source spatial data includes remote sensing image data, street view image data, road network data, building outline data, land parcel boundary data, point of interest data, population activity data, vegetation index data, public service facility data, and current land use data. Based on the urban spatial database, spatial overlay analysis is performed on remote sensing image data, street view image data, road network data, building outline data, and land parcel boundary data to obtain candidate spatial units. The candidate spatial units are then filtered according to preset morphological rules and combined with the street view image data for collaborative recognition to identify a set of urban odd-numbered land objects. Based on the aforementioned urban irregular land objects, a standardized attribute library is established, and the spatial boundary field, morphological feature field, street view perception field, greening basic field, functional service field, and governance constraint field corresponding to each urban irregular land object are extracted. Based on each field, the attribute vector of the corresponding urban irregular land object is constructed. Based on the attribute vector, an urban aberration landscape potential index system is constructed. Each evaluation index in the urban aberration landscape potential index system is standardized and the weight of each evaluation index is determined. The urban aberration landscape potential index system includes four evaluation dimensions: spatial morphology usability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints. Based on standardized evaluation indicators and corresponding weights, a landscape potential index model for urban odd-shaped land is constructed to calculate the landscape potential index corresponding to each urban odd-shaped land object and the contribution rate of each evaluation dimension. Based on the landscape potential index and the contribution rate of each evaluation dimension, the irregular land objects in each city are comprehensively ranked to determine the dominant potential type and update priority of the irregular land objects in each city. Based on the landscape potential index, renewal priority, dominant potential type, and governance implementation constraints, the potential levels of irregular land parcels in each city are classified, and corresponding urban micro-renewal strategies are generated.

2. The method for measuring the landscape potential of anomalous land in urban micro-renewal according to claim 1, characterized in that: The remote sensing image data, street view image data, road network data, building outline data, plot boundary data, point of interest data, population activity data, vegetation index data, public service facility data, and current land use data are subjected to coordinate unification, spatial registration, format conversion, noise removal, missing value processing, and scale matching to obtain a basic urban spatial database under a unified spatial reference.

3. The method for measuring the landscape potential of anomalous land in urban micro-renewal according to claim 1, characterized in that: The identified set of irregular urban land parcels includes: Based on the remote sensing image data, land cover information, vegetation index information, and candidate spatial boundary information are extracted; Spatial overlay analysis is performed by combining road network data, building outline data, and land parcel boundary data to obtain candidate spatial units; The candidate spatial units are filtered based on area thresholds, morphological rules, and road adjacency rules; The idle status, interface closure, lack of greenery, and accessibility characteristics of candidate spatial units are identified and screened using the street view image data, and a set of urban irregular land objects is obtained by combining the identification with manual verification.

4. The method for measuring the landscape potential of anomalous land areas for urban micro-renewal according to claim 1, characterized in that: Based on the aforementioned irregular urban land parcels, a standardized attribute library is established, specifically including: Establish a unique identifier for each irregular land parcel in each city; Extract the plot number, plot coordinates, area, perimeter, and adjacency relationship to form a spatial boundary field; The morphological feature fields are formed by extracting morphological complexity, land use angle, spatial openness, and road adjacency. Green view rate, interface closure, accessibility, dwell time, and idle visibility are extracted to form street view perception fields; Extract vegetation coverage, the proportion of exposed hard paving, tree canopy coverage, and the remaining greening improvement to form basic greening fields; Extract functional service fields by considering factors such as the gap in surrounding public service facilities, population activity intensity, pedestrian accessibility, and the supply and demand relationship of community public spaces. Extract fields for governance constraints based on ownership complexity, construction accessibility, current occupancy level, safety risks, and implementation difficulty; Generate attribute vectors for the corresponding city's odd-numbered land objects based on each field.

5. The method for measuring the landscape potential of anomalous land areas for urban micro-renewal according to claim 1, characterized in that: The urban irregular land landscape potential index system comprises four evaluation dimensions: spatial morphology usability, greening enhancement potential, functional service supplementation potential, and governance implementation constraints. Specifically, these include: The evaluation dimensions for spatial morphology availability include area suitability, morphological complexity, road adjacency, spatial openness, and slow-moving accessibility evaluation indicators. The evaluation dimensions for greening enhancement potential include current vegetation coverage, green visibility rate, hard paving ratio, and greening enhancement capacity evaluation indicators. The evaluation dimensions for the potential to supplement functional services include the intensity of surrounding population activity, gaps in public service facilities, degree of insufficient coverage of service radius, and evaluation indicators for the demand for available living space. The evaluation dimensions for governance implementation constraints include evaluation indicators for ownership complexity, current occupancy intensity, construction accessibility, safety risks, and maintenance difficulty.

6. The method for measuring the landscape potential of anomalous land areas for urban micro-renewal according to claim 5, characterized in that: The evaluation indicators in the urban irregular land landscape potential index system are standardized, and the weights of each evaluation indicator are determined, specifically including: Positive and negative indicators are determined based on the attributes of each evaluation indicator. Positive indicators are those with higher values, indicating higher potential, while negative indicators are those with higher values, indicating lower potential or stronger constraints. Positive indicators are standardized using the following formula: Negative indicators are standardized using the following formula: In the formula, Indicates the first The oddly positioned object in the first Standardized values ​​for each indicator This represents the original index value. and They represent the first The maximum and minimum values ​​of each indicator across all samples; The weight of each evaluation index in each sample is calculated based on the standardized evaluation index matrix: The objective weights of each evaluation index are determined using the entropy weight method: Calculate the first The first indicator in the The proportion in each sample: Among them, when At that time, it can be made ; Calculate the first Information entropy of each indicator: in, Calculate the first Coefficient of variation for each indicator: Finally, we get the number. Weights of each indicator: In the formula, The total number of urban irregular land parcels participating in landscape potential measurement. The total number of evaluation indicators; Indicates the first The oddly positioned object in the first Standardized values ​​for each indicator Indicates the first The oddly positioned object in the first The proportion of each indicator Indicates the first The sum of the standardized values ​​of each indicator across all irregular objects; Indicates the first Information entropy of each indicator This represents the normalization coefficient used in entropy calculation; Indicates the first The coefficient of difference for each indicator Indicates the first The weight of each evaluation indicator, This represents the sum of the coefficients of difference for all evaluation indicators; The greater the difference in a certain indicator among different distorted objects, the greater its information entropy. The smaller the coefficient of variation, the higher the difference. The larger the value, the higher the corresponding weight. The higher the value, the stronger the indicator is at distinguishing the landscape potential of different aberration sites.

7. The method for measuring the landscape potential of asymmetrical plots in urban micro-renewal according to claim 1, characterized in that: Based on standardized evaluation indicators and corresponding weights, a landscape potential index model for urban irregular land is constructed. This model calculates the landscape potential index for each urban irregular land object and the contribution rate of each evaluation dimension, specifically including: Spatial morphology usability is calculated based on the standardized evaluation indicators and their corresponding weights. : In the formula, Indicates the first The first irregular object in the spatial morphology usability dimension A standardized index value, Corresponding indicator weights The number of indicators representing the availability of spatial form; Calculate the potential for greening improvement : in, Indicates the first The first dimension of greening enhancement potential of individual odd-shaped land units A standardized index value, Corresponding indicator weights This indicates the number of indicators under the dimension of potential for greening improvement; Computing capabilities can fill gaps in service potential. : in, Indicates the first The first dimension of functional service supplementation potential of individual anomalous land units A standardized index value, Corresponding indicator weights This indicates the number of metrics under the dimension of functional service complementarity potential; Constraints for Implementing Computational Governance : in, Indicates the first The first irregular land unit under the constraint of governance implementation dimension A standardized index value, Corresponding indicator weights This indicates the number of indicators under the governance implementation constraint dimension; Constraints on governance implementation Convert into an actionable contribution : Calculate the landscape potential index corresponding to irregular and barren land in each city: in, Indicates the first Landscape potential index of isolated urban land parcels; Indicates the usability of spatial form; This indicates the potential for improved greening; This indicates the potential for functional services to fill gaps; This indicates that governance is subject to constraints. Indicates a contribution that can be implemented; , , , These are represented as the weights of the four primary dimensions, satisfying: when The larger the value, the more suitable this irregular land object is to be a priority object for urban micro-renewal; Calculate the contribution rate of each dimension: in, Indicates the first The urban odd-numbered land objects in the first Scores on each dimension .

8. The method for measuring the landscape potential of anomalous land in urban micro-renewal according to claim 7, characterized in that: Based on the landscape potential index and the contribution rate of each evaluation dimension, the irregular land parcels in each city are comprehensively ranked to determine the dominant potential type and update priority corresponding to each irregular land parcel, specifically including: Construct a weighted standardization matrix based on the standardized evaluation indicators and their corresponding weights: Determine the positive and negative ideal solutions: in, , ; Calculate the first one respectively The distance between an odd-shaped urban land object and the positive and negative ideal solutions: (8-5) Finally, the TOPSIS proximity coefficient is obtained: in, , The higher the value, the closer the irregular land is to the ideal regeneration state, and the higher its landscape potential. and They are used together to update the priority sorting, where Used to explain landscape potential structure Used to enhance the stability and closeness of comprehensive sorting; The comprehensive ranking and update priority of irregular land parcels in each city are determined by combining the landscape potential index and TOPSIS proximity.

9. The method for measuring the landscape potential of anomalous land areas for urban micro-renewal according to claim 8, characterized in that: Identify the dominant potential types corresponding to irregularly sized land parcels in each city, including: Calculate the contribution rate of each evaluation dimension to the landscape potential index; The evaluation dimension with the highest contribution rate is determined as the dominant potential type of the corresponding urban asymmetric land objects; among them, when the contribution rate of functional service supplementation potential is the highest, it is determined as functional service supplementation type; when the contribution rate of greening enhancement potential is the highest, it is determined as greening enhancement type; and when the contribution rate of spatial form usability is the highest, it is determined as spatial optimization type.

10. The method for measuring the landscape potential of anomalous land in urban micro-renewal according to claim 9, characterized in that: Based on the landscape potential index, renewal priority, dominant potential type, and governance implementation constraints, the potential levels of irregular plots in each city are classified, and corresponding urban micro-renewal strategies are generated, as follows: Based on the landscape potential index, TOPSI proximity, and dominant limiting factors, urban irregular land is divided into high-potential, medium-potential, and low-potential zones, and differentiated micro-renewal strategies are implemented accordingly. Potential partitions are represented as: in, Indicates the first The potential level of isolated land parcels in a city and Indicates the partition threshold; A geographic detector is introduced to identify the dominant influencing factors and their interactions. When using a geographic detector, the single-factor explanatory power statistic is: in, Represents a hierarchical unit. Indicates the number of layers. Indicates the first Number of layer samples This indicates the total number of samples in the entire region. Indicates the first Layer sample variance This represents the variance of the entire region's samples; Based on potential partitioning, strategy matching is performed according to the dominant potential type and dominant limiting factor: in, Indicates the first Micro-update strategy types for irregularly sized urban land objects Indicates potential level, This indicates the dominant dimension with the highest contribution rate. This indicates that governance is subject to constraints.

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