A decision-making system for the planning of urban green spaces

Abstract

A decision system (100) for planning urban green spaces in cities, wherein the system comprises: a remote sensing unit (102) configured to acquire satellite and aerial imagery for the assessment of land surface temperature (LST), land use, land cover and urban green areas; a unit (104) of a geographic information system (GIS) integrated with a remote sensing unit (102) and configured to collect and process spatial data, including land surface temperature (LST) data, land use data and green space distribution data; a computing unit (106) connected to the remote sensing unit (102) and configured to estimate the green space requirement based on LST reduction, wherein the computing unit (106) uses a curve fitting technique using historical and observed data sets to reduce the LST; a prediction unit (108) connected to a computing unit (106) and configured to predict the optimal grassland area to achieve a desired LST reduction, wherein the prediction unit (108) uses a third-order polynomial equation that correlates the grassland area with the land surface temperature; a data validation unit (110) used to validate generated outputs using statistical methods, such as the coefficient of determination (R²). 2 ), of the mean square error (RMSE) and the mean absolute error (MAE), is configured to ensure accuracy and reliability; a site suitability analysis unit (112) integrated into the GIS unit (104), configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; A processing unit (114) is configured so that urban planners and political decision-makers can enter parameters such as the desired temperature reduction and generate reports with detailed information on the need for green spaces and suitable locations for implementation.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to artificial intelligence systems. In particular, the present invention relates to artificial intelligence systems based on urban planning. More specifically, the invention relates to a decision-making system for planning urban green spaces in cities. BACKGROUND OF THE INVENTION

[0002] Urban green spaces (UGS) are essential components of modern cities and provide numerous ecological, social, and health benefits. These spaces play a critical role in mitigating the urban heat island effect (UHI), reducing land surface temperatures (LST), improving air quality, promoting biodiversity, enhancing resident well-being, and overall thermal comfort. However, in regions experiencing rapid urbanization, the effective planning, distribution, and provision of green spaces are often overlooked or insufficiently considered in urban development strategies.

[0003] Although urban planners and policymakers recognize the importance of UGS, existing methods for determining green space needs in cities are often subjective and lack a quantitative basis for site selection and space allocation. Furthermore, current literature points to the lack of a clear and comprehensive definition of UGS in the policy context, leading to inconsistencies in the provision of green space across different urban areas. Traditional approaches also fail to integrate advanced geospatial tools and data-driven models that can optimize UGS planning, especially in cities experiencing severe heat stress and environmental degradation.

[0004] To address the above limitations, a decision-making system for urban green space planning needs to be developed to address the above challenges. The system integrates geographic information systems (GIS) and remote sensing techniques to quantitatively analyze and plan green spaces in cities. The system also evaluates the spatial distribution of existing green spaces, analyzes land surface temperature (LST) data to understand the cooling effects of green spaces, and determines the optimal site suitability for new green spaces based on scientific models.

[0005] The technical advances disclosed by the present invention overcome the limitations and disadvantages of existing and conventional systems and methods. SUMMARY OF THE INVENTION

[0006] This disclosure relates to a decision-making system for urban green space planning in cities. The core of the system is a computing unit that objectively quantifies the required green space area based on the reduction in land surface temperature (LST). By establishing a correlation between the size of green spaces and their cooling effect, the model provides a practical and scalable solution for urban heat mitigation and environmental management. This invention fills a critical gap in urban planning by providing a scientifically validated approach to predict green space demand, increase thermal comfort, and improve urban quality of life.The system is particularly valuable for policymakers, urban planners, and environmental managers, as it provides practical guidelines for sustainable urban planning that balances urban growth with environmental protection. Based on interdisciplinary research in urban planning, environmental science, and geospatial technology, the invention meets the urgent need for evidence-based urban green space (UGS) planning, particularly in urban contexts.

[0007] One objective of this disclosure is to develop a decision-making system for the planning of urban green spaces in cities.

[0008] Another objective of this disclosure is to assess the need for green space in urban areas based on the reduction of land surface temperature (LST).

[0009] Another objective of the present disclosure is to establish a computing unit that correlates the green space area with the cooling effect on the LST and thus predicts the optimal size of green spaces required to mitigate the urban heat island effect (UHI).

[0010] Another objective of this disclosure is to provide a tool for the site selection of UGS that considers spatial distribution, urban density and environmental factors to ensure efficient and effective green space allocation.

[0011] Another objective of this disclosure is to improve urban quality of life by providing a sustainable and data-driven approach to UGS planning that considers thermal comfort, public health and environmental benefits.

[0012] Another objective of this disclosure is to fill the gap in existing policies by introducing a clear, quantitative system for providing UGS that can be integrated into urban planning strategies.

[0013] To provide a decision-making system for urban green space planning in cities, comprising: a remote sensing unit configured to acquire satellite and aerial imagery to assess land surface temperature (LST), land use, and urban green space; a geographic information system (GIS) unit integrated with the remote sensing unit configured to acquire and process spatial data, including land surface temperature (LST) data, land use data, and green space distribution data; a computing unit connected to the remote sensing unit and configured to estimate green space demand based on LST reduction, the computing unit using a curve-fitting technique using historical and observed LST reduction datasets;a prediction unit connected to the computing unit and configured to predict the optimal green space area to achieve a desired LST reduction, wherein the prediction unit uses a third-order polynomial equation correlating the green space area with the land surface temperature; a data validation unit configured to validate generated outputs using statistical methods such as the determination coefficient (R; 2), root mean square error (RMSE) and mean absolute error (MAE) to ensure accuracy and reliability; a site suitability analysis unit integrated into the GIS unit, configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; and a processing unit configured to allow urban planners and policy makers to input parameters, such as the desired temperature reduction, and generate reports detailing green space area requirements and suitable locations for implementation.

[0014] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0015] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig.1 shows a block diagram of a decision system for planning urban green spaces in cities according to an embodiment of the present disclosure. Fig. 2 shows a graphical representation of the trend check of the predicted data sets according to an embodiment of the present disclosure.

[0016] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Moreover, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION:

[0017] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that this is not intended to limit the scope of the invention, as such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0018] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0019] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0020] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0021] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0022] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0023] Fig. 1 shows a block diagram of a decision system (100) for planning urban green spaces in cities according to an embodiment of the present disclosure.

[0024] With reference to Fig.1, the decision system (100) comprises: a remote sensing unit (102) configured to acquire satellite and aerial images for assessing land surface temperature (LST), land use, land cover, and urban green space; a geographic information system (GIS) unit (104) integrated with the remote sensing unit (102) and configured to acquire and process spatial data, including land surface temperature (LST) data, land use data, and green space distribution data; a computing unit (106) connected to the remote sensing unit (102) and configured to estimate green space requirements based on LST reduction, wherein the computing unit (106) uses a curve fitting technique using historical and observed data sets to reduce LST;a prediction unit (108) connected to the computing unit (106) and configured to predict the optimal green space area to achieve a desired LST reduction, wherein the prediction unit (108) uses a third-order polynomial equation correlating the green space area with the land surface temperature; a data validation unit (110) responsible for validating the generated outputs using statistical methods such as the determination coefficient (R; 2), root mean square error (RMSE) and mean absolute error (MAE) to ensure accuracy and reliability; a site suitability analysis unit (112) integrated into the GIS unit (104) configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; and a processing unit (114) configured to allow urban planners and policy makers to input parameters such as the desired temperature reduction and generate reports detailing the need for green spaces and suitable locations for implementation.

[0025] In one embodiment, the GIS unit (104) is also used to analyze the effects of urban heat islands (UHI) in selected areas and thus assess the impacts of urban green spaces on the microclimate.

[0026] In one embodiment, the remote sensing unit (102) uses multi-temporal satellite data to track changes in land surface temperature over time.

[0027] In one embodiment, the computing unit (106) is capable of selecting multiple polynomial equations, with an emphasis on third-order polynomial equations for optimal temperature reduction predictions based on the green space size.

[0028] In one embodiment, the forecasting unit (108) integrates demographic, ecological and geographical parameters in addition to land surface temperature data to refine the need for green space.

[0029] In one embodiment, the site suitability analysis (112) takes into account criteria such as proximity to densely populated areas, existing vegetation cover and environmental stressors in the selection of green space locations.

[0030] In one embodiment, the processing unit (114) is equipped with real-time data visualization tools that display the relationship between green spaces and temperature reduction on city maps, wherein the processing unit (114) is configured to identify green spaces.

[0031] In one embodiment, the prediction unit (108) provides recommendations on the vegetation types and landscaping required to maximize the cooling effect of the planned urban green spaces.

[0032] In one embodiment, the computing unit (106) is further configured to provide guidelines for incorporating the estimated green space needs into urban planning policies and regulations for sustainable urban development.

[0033] In one embodiment, a mathematical model was developed to estimate urban green space demand based on land surface temperature (LST) reduction. Using a dataset and literature-based evidence, the model uses curve fitting to generate coefficients for 20 equations, 14 of which are considered to be suitable due to their high R 2 -values ​​were selected for further validation. The best-fitting equation, a third-order polynomial, was chosen for its superior performance in predicting green space demand based on temperature reduction, with an R 2 -value of 0.89, a root mean square error (RMSE) of 14.49 and a mean absolute error (MAE) of 9.61. y=5.17+(−8.83)∗X+(8.53)∗X2+−0.63∗X3)

[0034] The above equation demonstrates that green space area is a function of LST and can be used by urban planners and policymakers to design cities with sufficient green space to counteract rising temperatures. The study concludes with a call for further research to deepen the understanding of the role of urban green spaces in temperature regulation and promote more sustainable and livable urban environments.

[0035] Fig.Figure 2 shows a graphical representation of the trend check of the predicted data sets according to an embodiment of the present disclosure. The present system quantifies the need for urban green space (UGS) in Indian cities by examining the relationship between green space area and its cooling effect on land surface temperature (LST). The objective is to determine the minimum green space area required to mitigate the effects of urban heat islands (UHI), considering key factors such as thermal comfort, health, and environmental impacts. The model was built through an extensive literature review and data collection from previous studies, which provided insights into the correlation between green space area and temperature decline.By employing curve-fitting techniques and validating the model using statistical methods, the research establishes a strong relationship between green space and LST reduction. The resulting model serves as a valuable tool for urban planners and environmental scientists, providing a predictive framework for understanding how green space can cool urban environments and improve overall sustainability.

[0036] The system claimed in the present invention aims to develop a decision-making system for identifying suitable plots of land for allocation of Urban Green Spaces (UGS) in cities and its application has been demonstrated for five cities in Rajasthan - Sikar, Churu, Bhilwara, Sri Ganganagar and Sujangarh. The study addresses the challenge of providing green spaces in hot and dry climates by integrating various factors such as building density, urban land use and land cover (LULC), proximity to roads and waterbodies, slope gradient and proximity to green spaces. The methodology is divided into three phases: identification of factors affecting the suitability of green spaces, definition of criteria and sub-criteria based on scientific research and application of Analytical Hierarchy Process (AHP) through Weighted Overlay Method in GIS for site suitability analysis.Each criterion is assigned a weighting, with building density being given the highest importance (0.30), followed by LULC, distance to roads, and other factors. The system provides urban planners with a structured, quantitative approach to optimizing green space distribution and increasing environmental and social benefits in urban areas.

[0037] The developed system provides an estimate of cities' green space needs and identifies optimal locations for their provision based on the city's master plan and zoning plan. It supports policymakers and urban planners in making informed decisions on green space provision. The system outlines two key planning scenarios based on land ownership: base Scena rio Location priority Determination S n category Supported population / unit Area requirement (ha) Property 01 Free country Government country 1. Area 2. Population 1 Residential Park 5000 0,5 2 Neighborhood Park 15000 Private land 3 Community spark 1 lakh 5 4 District / city park 5 lakh 25 02 Existing buildings Government building 1. Roof gardens 2. Vertical greens 3. Parking spaces Percentile basis Commercial buildings Institutional buildings residential areas Phase-wise 1st place More than 2 hectares Free country 1. District / city park 2. Community park 3. Urban forest 4. Increase tree cover 1 District / city park 5 lakh 25 Action level 2 Community spark 1 lakh 5 Miyawaki Method - Planting Trees Together Existing building Percentile basis 2nd place 1 ha - 2 ha Free country 1. Neighborhood park 1 Residential area Park 5000 0,5 Action level 2. Residential Park 3. Pocket Parks 4. Green Buffers 2 Neighborhood Park 15000 1 Existing building based on an area of ​​0.4 ha to 2 ha Increase in tree cover 3rd place 0.5 ha - 1 ha Free country 1. Residential area park 2. Tree cover 1 Residential area Park 5000 0,5 Action level Increase in tree cover Existing building

[0038] Scenario O1 focuses on brownfield land, prioritizing government land for ease of acquisition, and proposes green spaces based on area or population limits, ranging from small residential parks (0.5 ha for 5,000 people) to large urban parks (5 ha for 2.5 million people). Scenario O2 addresses areas with existing development, giving government buildings priority for tree planting, green roofs, and vertical gardens to mitigate surface temperatures. The system also includes phased planning scenarios based on available land area. Phase P1 addresses parcels over 2 hectares and proposes urban parks or urban forests on brownfield land. Phase P2 focuses on land between 1 and 2 hectares for neighborhood or community parks, while Phase P3 focuses on smaller parcels between 0.Targeting 5 and 1 hectare for residential parks or tree cover, with recommendations based on population density. This comprehensive approach ensures that green space provision is optimized to utilize urban environments.

[0039] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0040] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 A decision system for the planning of urban green spaces in cities. 102 Remote Sensing Unit 104 Department of Geographical Information Systems (GIS) 106 computing unit 108 Forecast Unit 110 Data Validation Unit 112 Site Suitability Analysis Unit 114 processing unit

Claims

[1] A decision system (100) for planning urban green spaces in cities, the system comprising: a remote sensing unit (102) configured to acquire satellite and aerial images to assess land surface temperature (LST), land use, land cover, and urban green space; a geographic information system (GIS) unit (104) integrated with a remote sensing unit (102) and configured to collect and process spatial data, including land surface temperature (LST) data, land use data, and green space distribution data; a computing unit (106) connected to the remote sensing unit (102) and configured to estimate the green space requirement based on the LST reduction, wherein the computing unit (106) uses a curve fitting technique using historical and observed data sets to reduce the LST; a prediction unit (108) connected to a computing unit (106) and configured to predict the optimal green space area to achieve a desired LST reduction, wherein the prediction unit (108) uses a third-order polynomial equation correlating the green space area with the land surface temperature; a data validation unit (110) configured to validate generated outputs using statistical methods, such as the coefficient of determination (R 2 ), root mean square error (RMSE) and mean absolute error (MAE), to ensure accuracy and reliability; a site suitability analysis unit (112) integrated into the GIS unit (104) and configured to identify suitable locations for urban green spaces based on spatial distribution, land use patterns and LST data; a processing unit (114) is configured to allow urban planners and policy makers to enter parameters such as the desired temperature reduction and to produce reports detailing the need for green spaces and suitable locations for implementation. [2] The system (100) of claim 1, wherein the GIS unit (104) is further used to analyze urban heat island effects (UHI) in selected areas to assess the impact of urban green spaces on the microclimate. [3] The system (100) of claim 1, wherein the remote sensing unit (102) uses multi-temporal satellite data to track changes in land surface temperature over time. [4] The system (100) of claim 1, wherein the computing unit (106) is capable of selecting a plurality of polynomial equations, with an emphasis on third-order polynomial equations for optimal temperature reduction predictions based on the green space size. [5] The system (100) of claim 1, wherein the forecasting unit (108) integrates demographic, ecological and geographical parameters in addition to land surface temperature data to refine the need for green space. [6] The system (100) of claim 1, wherein the site suitability analysis (112) incorporates criteria such as proximity to densely populated areas, existing vegetation cover, and environmental stressors in selecting green space locations. [7] The system (100) of claim 1, wherein the processing unit (114) is equipped with real-time data visualization tools that display the relationship between green spaces and temperature reduction on city maps, the processing unit (114) being configured to identify green spaces. [8] The system (100) of claim 1, wherein the prediction unit (108) provides recommendations on the vegetation types and landscaping required to maximize the cooling effect of the planned urban green spaces. [9] The system (100) of claim 1, wherein the computing unit (106) is further configured to provide guidelines for incorporating the estimated green space needs into urban planning policies and regulations for sustainable urban development.

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