Advances in GIS-Based Assessment of Urban Green Infrastructure: A Systematic Review (2020–2024)

Advances in GIS-Based Assessment of Urban Green Infrastructure: A Systematic Review (2020–2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Advances in GIS-Based Assessment of Urban Green Infrastructure: A Systematic Review (2020–2024) Mahshid Gorjian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7349702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Geographic Information System (GIS) assessments that are comprehensive provide factual foundations for the design, construction, and enhancement of Urban Green Infrastructure (UGI) to foster sustainable development. Research methodology, application sectors, trends, and issues in GIS-based urban green infrastructure assessments are integrated in this study. The literature from the Scopus and Web of Science databases was carefully reviewed from January 1, 2020, to June 30, 2024. The initial dataset includes 640 items from Web of Science and 952 items from Scopus. The inclusion criteria were satisfied by 20 articles after 1,572 duplicates and irrelevant studies were eliminated. Accessibility, ecological services, resilience, environmental justice, social benefits, and aesthetics comprised the scope. Artificial intelligence improved data analysis, while the integration of GIS with multiple datasets and indicators increased comprehensiveness. However, it was important to establish connections with communities in the field to facilitate inclusion. The investigation recognized a growing interdisciplinarity that covered aesthetics, sociology, urban planning, and ecology. These methods demonstrate the necessity of sustainable urban development that is integrated with ecological, social, economic, and cultural factors. Urban Studies Geographic Information Systems Urban Green Infrastructure systematic review spatial analysis ecosystem services PRISMA multi-criteria decision analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Background and Rationale The increased speed of urbanization has added to the pressures on ecological systems, resource consumption, and urban living standards. The United Nations Sustainable Growth Goal 11 emphasizes the connection between urban growth, human well-being, and environmental sustainability (Wu et al., 2025). As a result of the persistent contradictions between the integrity of natural ecosystems and socioeconomic development, green infrastructure (GI) has emerged as a strategic approach that balances environmental, social, and economic interests. GI provides a variety of environmental services, including the regulation of climate, the enhancement of biodiversity, and the provision of cultural and recreational benefits (Wu et al., 2025). The guiding themes underscore the significance of interdisciplinary collaboration, the integration of urban and rural habitats, the further development of the connection between ecological structure and function, and the importance of multifunctionality. The absence of a globally accepted definition or systematic classification of GI, despite its significance, results in inconsistencies in planning, regulation, and implementation (Wu et al., 2025). Urban Natural Infrastructure (UGI), a subset of Green Infrastructure (GI), is primarily concerned with the natural assets that exist in urban environments. These assets consist of biological corridors, gardens, parks, wetlands, and green roofs. UGI strengthens environmental resilience, promotes social cohesion, elevates aesthetic and cultural values, and enhances public health. To effectively manage UGI, it is crucial to have instruments that can incorporate environmental and socioeconomic factors, evidence-based evaluation procedures, and accurate geographical data. Geographic Information Systems (GIS) have emerged as an essential tool in this field. GIS utilizes geographical data from remote sensing, government records, and in situ surveys to conduct multi-scale assessments of accessibility, ecological function, and vulnerability to promote sustainable and equitable urban design (Wu et al., 2025). Even though there have been numerous of studies conducted on GIS applications in related sectors, they demonstrate significant deficiencies when considering a UGI-specific perspective. Wu et al. (2025) conducted a comprehensive analysis of urban ecosystem services (UES) remote sensing research from 2010 to 2020. The review offered a thorough examination of the improvements to GIS; however, it did not address the specific planning, design, and management components of urban green infrastructure (UGI). In the past, research has mainly concentrated on hazard management, which involves the analysis of landscape fragmentation and the mitigation of emergency risk. Nevertheless, these studies frequently concentrate on ecological and biological outcomes, missing evaluations of aesthetic value, accessibility, and social justice (Wu et al., 2025). In the same vein, research on the restoration of urban vacant land has underscored the significance of GIS in ecological planning, even though it has not yet fully integrated transdisciplinary indicators or methodological advancements until 2020. The current evaluations' ability to provide exhaustive, contemporary, and practice-oriented UGI assessments is disrupted by these omissions. Knowledge Gap Over the past five years, there have been substantial methodological advancements in the assessment of UGI. Enhanced multi-criteria decision analysis (MCDA) methodologies, participatory GIS strategies that incorporate local feedback, automated ecological pathway modeling, and the application of artificial intelligence for image classification are all included in this. However, no systematic research has integrated these accomplishments to provide a comprehensive understanding of the ecological, social, and economic aspects of GIS-based urban green infrastructure evaluation. Inability to establish a consistent evidence basis renders urban planners, legislators, and academics ineffective of implementing GIS strategies that address both technological and social challenges in the absence of a synthesis. In this investigation, empirical studies that were published between January 1, 2020, and June 30, 2024, and employed GIS to investigate UGI are thoroughly examined. This results in the modification of existing deficiencies. The review incorporates evidence from environmental, social, and transdisciplinary perspectives, thereby incorporating methodological diversity and topic breadth. To ensure transparency and reproducibility, the research is conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Objectives and Research Questions The initial objective of the review is to determine the components of UGI that are evaluated using GIS. Secondly, it will provide a thorough examination and assessment of the methodological approaches that are implemented. Thirdly, it will examine the opportunities and innovations in GIS-based UGI assessment that are the consequence of interdisciplinary integration and new technologies. The review has three objectives. These objectives are consistent with the PECO framework: • Population (P)-designated urban areas that have been equipped with a diverse range of green infrastructure, such as wetlands, gardens, green roofs, parks, and natural corridors. • Exposure (E): The process of evaluating, simulating, or designing urban green infrastructure (UGI) using GIS technologies, tools, and data sources. • Comparison (C): Assessment methodologies that are based on geographic information systems (GIS) demonstrate variations in their spatial dimensions, indicators, and contexts. • Result (R): Assessment results for environmental performance, accessibility, social benefits, aesthetic value, resilience, and planning recommendations. This review examines a variety of specific research topics to accomplish these objectives: 1. Which categories of UGI and associated services are evaluated most frequently using GIS? 2. Which data sources, analytical techniques, and validation strategies are employed in modern UGI assessments that are based on GIS? 3. In the context of the implementation of artificial intelligence, participatory procedures, and transdisciplinary frameworks, how have methodological methods evolved since 2020? What are the remaining deficiencies in the evaluation of urban green infrastructure using GIS, and how can these issues be addressed in future research? By structuring the synthesis around these inquiries, the review offers a diverse range of stakeholder’s valuable information. The results may be used by urban planners and policymakers to determine the most suitable GIS methodologies for context-specific issues, such as spatial disparities and climate adaptation. Potential for multidisciplinary collaboration and methodological deficiencies may be identified by researchers. Environmental management professionals may capitalize on the technology and data integration strategies that have been prioritized to optimize their investments in green infrastructure. The urgency of exhaustive, precise, and timely UGI assessments is underscored by the growing urgency of public health concerns, biodiversity loss, and climate change. A method of ensuring that green infrastructure development follows the broad criteria of sustainability, resilience, and equity as urban populations expand and land-use demands increase is evaluated using geographic information systems (GIS). The successful implementation of geographic information systems (GIS) necessitates variables that surpass technical proficiency. It is essential to guarantee that UGI criteria are established, that reliable data is accessible, that community perspectives are included, and that institutional and policy frameworks are followed. The systematic synthesis of knowledge is essential for the global viability of effective UGI initiatives that satisfy these requirements. This research defines the most recent advancements in GIS-based UGI assessment within the broader context of sustainable urban development. Additionally, it contextualizes them. It is acknowledged that UGI is a multifaceted asset with value in the ecological, social, and economic spheres, and that GIS can provide the comprehensive understanding required for its preservation, enhancement, and equitable distribution when methodological precision and interdisciplinary insight are employed. Methods Protocol and Reporting Standards This systematic review followed the PRISMA 2020 principles, ensuring methodological transparency and reproducibility (Page et al., 2021). Prior to conducting the literature search, a protocol was developed that detailed the review objectives, inclusion and exclusion criteria, search approach, quality evaluation, and synthesis process. The review lacked proper PROSPERO registration since it focused exclusively on methodological synthesis and not clinical results. Eligibility Criteria The eligibility criteria were developed using the Population, Exposure, Comparator, and Outcome (PECO) architecture. The target population included metropolitan areas with green infrastructure components like parks, gardens, green roofs, wetlands, ecological corridors, and more vegetated spaces. The exposure included using Geographic Information Systems (GIS) to evaluate, model, and plan urban green infrastructure. The comparison included modifications to GIS procedures, data sources, analytical techniques, and spatial settings. The findings included measures for ecological performance, accessibility, social benefits, aesthetic characteristics, resilience, and policy and planning recommendations. Eligible study designs were empirical investigations with qualitative, quantitative, or mixed approaches that presented novel research. Review articles, conceptual papers, conference abstracts, and non-peer-reviewed documents were excluded. Only studies published in English between January 1, 2020, and June 30, 2024, were evaluated. Information Sources and Search Strategy The search strategy used controlled vocabulary and keyword terms such as "green infrastructure," "green space," "green roof," and "GIS" or "Geographic Information System." Two major bibliographic databases were searched: Web of Science Core Collection (Science Citation Index and Social Science Citation Index) and Scopus. To account for the most recent methodological improvements, the date range was limited to January 1, 2020, to June 30, 2024. The extra material contains the complete search strings for each database. These phrases have been modified to each database's indexing architecture to improve retrieval sensitivity. A divided process was used to choose the studies. Initially, two reviewers independently reviewed titles and abstracts using the eligibility criteria. In the second stage, complete texts of potentially relevant papers were received and screened for inclusion. Discrepancies among reviewers were resolved through debate, and in cases where consensus was not possible, a third reviewer made the ultimate judgment. Screening decisions were recorded, and reasons for exclusion at the full-text stage were documented. The inter-reviewer agreement during title and abstract screening was high, with a kappa coefficient greater than 0.85, indicating excellent agreement. Data extraction was done individually by two reviewers using a pretested extraction form in MS Excel. Extracted variables include bibliographic information, country and study location, GIS data sources, UGI classification, research objectives, analytical methodology, validation methods, and main findings. The extraction form was improved after pilot testing on three studies to ensure clarity and comprehensiveness. No specific systematic review software, such as Covidence, was used; nevertheless, consistent version control was maintained through shared cloud-based storage to avoid data loss or duplication. The quality assessment included four criteria derived from previous methodological studies (Wu et al., 2025): clarity of aims and research inquiries, explicit identification of GIS software and version, transparency and reproducibility of the research methodology, and provision of an optimization plan based on results. Each condition was assigned a value of one (fully met), 0.5 (partially met), or zero (not met). Two reviewers independently examined each manuscript, settling disagreements through consensus, with a third reviewer available for arbitration. The ratings were combined to provide a four-point overall quality score. The average quality score across the papers included was 3.2, indicating a high level of methodological rigor. Data Synthesis The data synthesis used a thematic narrative framework to categorize studies based on assessment focus (e.g., accessibility, ecological benefits, social benefits, visual aesthetics), methodological approach (e.g., spatial analysis, network analysis, multi-criteria decision analysis), and type of urban green infrastructure evaluated. Because of the heterogeneity in study designs, indices, and spatial contexts, quantitative meta-analysis was not feasible. Nonetheless, comparative qualitative synthesis enabled the identification of methodological trends, technological developments, and multidisciplinary integrations that have emerged after 2020. This evaluation follows PRISMA 2020 principles, with a PRISMA flow diagram outlining the selection process and a checklist included in the supplemental materials. The approach ensures that the search strategy, selection criteria, data extraction, quality assessment, and synthesis are all visible and reproducible. Results Study Selection A comprehensive examination of the Scopus and Web of Science Core Collection databases revealed 1,592 documents. The dataset was cleansed of duplicates, resulting in 1,141 records. For a variety of reasons, including retraction (n = 3), non-English language (n = 34), publication type (book section or conference proceedings; n = 174), review format (n = 27), and a lack of keywords in titles (n = 398) and abstracts (n = 164), 800 articles were eliminated during the preliminary evaluation of titles and abstracts. This led to the identification of 340 articles that necessitated a full-text review. The full-text screening process eliminated papers that were unrelated to UGI evaluation (n = 67), not focused on UGI (n = 67), inconsistent with the review objectives (n = 11), and lacked pertinent key terms (n = 112). The inclusion criteria were satisfied by twenty articles. T The 20 studies were published in 20 distinct journals, with MDPI outlets (n = 7) being the most prevalent (n = 7). Elsevier (n = 4), Taylor & Francis (n = 3), and individual instances from Springer, Pleiades Publishing Inc., Sciendo, World Scientific, Tehran Urban Research and Planning Center, and University of Kufa were also included. The most participants were found in China (n = 6), Saudi Arabia (n = 2), and Spain (n = 2), with the research sites dispersed across 13 nations. The proportion of research locations in Asia was the highest (n = 12), followed by Europe (n = 5), and Africa (n = 3). Government agencies, commercial corporations, academic institutions, international organizations, and other sources (including open-source repositories, satellite datasets, and web-based platforms) where the five categories into which GIS data sources were split. To enhance reliability, numerous studies integrated multiple data sources. The research in this domain was primarily concerned with the assessment of social value, environmental benefits, accessibility, and distribution. NDVI and land-cover classification were typically employed in distribution analyses to delineate green space areas and monitor changes in time (Buchavyi et al., 2023 ; Pouya & Aghlmand, 2022 ). Cost-distance metrics were employed to represent trip impedance, and network analysis was employed to evaluate accessibility by modeling walking and driving routes (Vîlcea & ǘoșea, 2020; Yang, 2024 ). Models such as InVEST, which quantify carbon storage and sequestration, were employed to investigate ecological benefits (Rachid et al., 2024 ; Hoeben & Posch, 2021 ). The social advantages were assessed by combining survey data with GIS outputs and analyzing perceived accessibility, utilization patterns, and health consequences (Mohammed & Hammo, 2023 ). Methodological Approaches The critical appraisal of numerous studies revealed a high degree of methodological integration, particularly when participatory data collection and remote sensing were combined. However, the insufficient coverage of informal or non-public green spaces, which could substantially enhance urban ecosystem services, remained a persistent issue. Assessments of potential efficacy focused on the intended design, connectivity, and suitability of the site. Before overlay analysis, suitability mapping frequently implements multi-criteria decision analysis (MCDA) methodologies, particularly the Analytical Hierarchy Process (AHP), to allocate weights to environmental and socioeconomic variables (Xu et al., 2020 ; Mobarak et al., 2022 ; Waheeb et al., 2023 ; Hailemariam, 2021 ). Connectivity assessments employed instruments such as GuidosToolBox to assess landscape fragmentation and identify biological corridors (Mironova, 2021 ; Wanghe et al., 2020 ). Priority zones for green space enhancement or reconfiguration were identified through scenario-based simulations, service demand mapping, and weighted overlay analysis (Huang et al., 2023 ; Rodríguez-Espinosa et al., 2020 ; Zhang et al., 2024 ). These works exhibited creativity by integrating GIS with specialized modeling tools to enable scenario testing and optimization. The validation methodologies were inconsistent; some studies included field verification, while others relied solely on model outputs without ground-truthing. Table 1 Methodological Approaches Method Type Tools/Software Advantages Limitations MCDA (AHP) ArcGIS, QGIS, Analytical Hierarchy Process modules Integrates multiple environmental & socio-economic criteria; adaptable to diverse contexts Subjectivity in weight assignment; requires expert input Network Analysis ArcGIS Network Analyst, GIS-based routing tools Models’ accessibility via walking/driving routes; supports equity-focused planning Data quality dependent on accurate road/transport layers; may overlook informal paths AI Image Classification Python (TensorFlow, Keras), ENVI, high-res imagery Automates land-cover classification; handles large datasets; increases spatial precision Requires substantial training data; sensitive to image quality By creating algorithms, decision-support systems, and data processing improvements, methodologically oriented research enhanced GIS applications. A few examples are the development of GIS algorithms for the selection of green roof and roadside tree locations using computational fluid dynamics (Kim et al., 2021 ) and GIS-based decision-support systems for the creation of ecological networks (Bai et al., 2022 ). The researchers underscored the importance of AI-driven image categorization, crowd-sourced geolocation information, and high-resolution remote sensing data to enhance the analytical depth and geographical precision thereof. Technical rigor and the potential for scalability are the strengths of these investigations. However, only a small number of studies specifically investigated the applicability of tools in situations with limited technical capabilities or user accessibility. Diversity of Sources and Data Integration: Most studies employed a diverse array of datasets, frequently incorporating governmental geographic data, satellite imagery, and in situ observations. This approach enhanced reliability, but it also led to inconsistencies in the quality and resolution of data. Procedures for Validation: Significant variation has been observed in validation methodologies. Field surveys and stakeholder consultations were instrumental in verifying the accuracy of GIS outputs; however, the lack of unambiguous validation in numerous studies restricted the confidence in their applicability. Interdisciplinary Methodologies: In recent years, there has been a gradual integration of ecological, social, and planning perspectives, enabling more thorough evaluations of UGI. These methods acknowledged green infrastructure as a socioeconomic resource and an ecological habitat. Visualization Improvements: The transition to more sophisticated GIS visualizations facilitated the communication of results to the public and policymakers in a more transparent manner. GIS was universally recognized as an effective platform for the integration of geographical and non-spatial variables to evaluate UGI. The efficacy of MCDA methodologies for appropriateness analysis and the significance of accessibility metrics in equity-centered planning were both agreed upon. There was a disagreement regarding the most effective indicators for quantifying ecological benefits, with some studies advocating for vegetation indices and others emphasizing modeled ecosystem service outputs. The effectiveness of GIS systems in evaluating artistic and cultural qualities was a topic of debate. Table 2 - Study Characteristics Author/Year Country UGI Type GIS Method Bai et al., 2022 China Green networks Decision-support system Zhang et al., 2024 China Historic landmarks Visual assessment Huang et al., 2023 China Green spaces Scenario simulation Rachid et al., 2024 Morocco Urban greenery Ecosystem services modeling Discussion Principal Findings The incorporation of 20 recent empirical studies illustrates that GIS-based evaluations of urban green infrastructure (UGI) have developed into projects that are highly transdisciplinary, data-intensive, and methodologically diverse. The review aimed to identify the most frequently investigated aspects of UGI, clarify and evaluate the methodological approaches used, and investigate the advances and opportunities that result from technology integration and interdisciplinary collaboration. From this context, three significant discoveries are made. Initially, GIS approaches broadened beyond ecological mapping to encompass social, economic, and aesthetic variables, indicating a transition to a more comprehensive assessment of sustainability. Secondly, the analytical profundity and relevance of decision-making have been enhanced by methodological advancements such as ecological corridor modeling, multi-criteria decision analysis (MCDA), and artificial intelligence for image classification. The analytical robustness has been enhanced by the incorporation of multiple data sources, including government cadastral data, high-resolution remote sensing, and crowd-sourced information. However, the quality and compatibility of the data have been a source of concern. In contrast to previous research that primarily concentrated on distant sensing of urban ecosystem services or hazard-specific applications, this synthesis distinguishes post-2020 research by emphasizing urban green infrastructure. Previous research has concentrated on UGI as an ecological resource, with an emphasis on the potential for environmental mitigation, biodiversity, and vegetation cover (Mironova, 2021 ; Wanghe et al., 2020 ). The examined studies expand this perspective to encompass aesthetic valuation, spatial justice, and accessibility, in accordance with contemporary urban sustainability frameworks that equally prioritize environmental resilience and social equity. This increase is in accordance with environmental planning trends that emphasize the importance of integrated socio-ecological systems thinking. The methodological innovation is the integration of participatory and perception-based data with spatial analysis, a practice that has been less prevalent in previous research. While ecological link modeling and NDVI mapping are significant methodologies, the incorporation of stakeholder surveys, crowd-sourced geolocation data, and urban morphology measurements introduces analytical features that were substantially absent in studies conducted prior to 2020. In other regions, the findings are consistent with the current consensus. Accessibility is a critical component of the utilization of urban green infrastructure and a matter of equity in urban planning, as evidenced by a multitude of studies (Vîlcea & ǘoșea, 2020; Yang, 2024 ). It is widely acknowledged that MCDA frameworks, particularly AHP, are beneficial for site suitability analysis due to their ability to incorporate a variety of socioeconomic, environmental, and geographical factors (Mobarak et al., 2022 ; Hailemariam, 2021 ). Nevertheless, there are debates regarding the quantification of ecological benefits. Some studies concentrate on biophysical measurements, such as NDVI and canopy cover, while others advocate for ecosystem service models, such as InVEST, which offer function-based metrics such as carbon sequestration (Rachid et al., 2024 ; Hoeben & Posch, 2021 ). The presentation underscores the substantial disciplinary distinctions between conventional ecological monitoring practices and service-oriented valuation methodologies. Additionally, there is a lack of consensus regarding the most effective method for capturing and integrating aesthetic and cultural values into GIS frameworks. While some academicians employ visual impact evaluation tools, others rely on subjective perception surveys. The evidentiary foundation possesses significant values. Clear delineations of data sources, analytical tools, and parameter configurations are frequently observed, indicating a high level of methodological transparency. To enhance the confidence of their findings, numerous studies apply validation procedures, including stakeholder feedback, satellite image comparison, and field verification. The applicability of GIS-based UGI assessment to various socioeconomic and ecological ecosystems is illuminated by the diverse contexts of Asia, Europe, and Africa. In addition, the capacity of evaluations to address the complex elements of urban sustainability is enhanced by the growing interdisciplinarity that integrates ecology, urban planning, sociology, and computer science. Strengths and Limitations However, it is evident that there are constraints. The geographical representation is inconsistent, with a significant preference for middle- and high-income countries, particularly in East Asia and specific regions of Europe. Urban green infrastructure has the potential to provide substantial resilience benefits to low-income communities; however, they are underrepresented. A potential linguistic bias may have been introduced by restricting the language to English, which could have resulted in the exclusion of pertinent studies published in other languages. Grey literature, which could include practical advancements that have not yet been addressed in academic journals, is disregarded in favor of peer-reviewed journal papers. Selection bias may be present since the databases Web of Science and Scopus lack every relevant regional or technical journals. Meta-analyses face challenges by the methodological heterogeneity among studies, which is characterized by disparities in spatial resolution, indicator definitions, and validation procedures, thereby limiting the comparability of results. The absence of specific reproducibility measures, such as publicly accessible datasets or code, precludes openness, even when methodological descriptions are extensive. Research Gaps and Future Directions The practical implications are immediate and actionable. Urban planners can utilize GIS-based MCDA frameworks to facilitate the equitable deployment of UGI, particularly in underserved neighborhoods with apparent accessibility issues. By incorporating ecological connectivity models into municipal planning procedures, it is guaranteed that new urban green infrastructure investments will benefit broader habitat networks. Therapies that are both ecologically effective and socially meaningful can be achieved by incorporating biophysical and perceptual data into the design process. Carbon sequestration and microclimate regulation models can be employed by environmental managers to select urban green infrastructure projects that offer substantial climate adaptation benefits. Research methodology has the potential to be enhanced. Improved study comparability and meta-analytical synthesis will be enabled by the establishment of agreements on critical variables, including ecological function, social benefit, and accessibility. By improving participatory GIS methods, it is possible to foster greater community engagement and guarantee that UGI designs are in accordance with local cultural concepts and requirements. Additional research on AI-assisted classification, automated suitability mapping, and real-time monitoring systems has the potential to enhance accuracy and reduce the time required for analysis. It is essential to conduct cross-continental comparison analyses to comprehend the ways in which contextual factors affect the performance and perception of UGI. Similarly, the policy implications are substantial. GIS-based UGI studies can be incorporated into the regulatory planning processes of municipal governments to improve zoning decisions, infrastructure allocations, and climate resilience efforts. The challenge of limited data accessibility in specific regions could be mitigated by open data initiatives, while capacity-building programs could enhance the technical skills necessary for complex GIS analysis. The legitimacy and community support of UGI initiatives will be enhanced by the integration of participatory mapping and validation into policy procedures. Numerous inquiries remain unresolved. The question of whether integrated indices can effectively encompass both without reductionism is underscored by the fact that the appropriate balance of ecological and social variables in urban green infrastructure evaluation remains unresolved. Ecosystem services and social benefits that are not completely documented are substantially influenced by informal green areas, including vacant lots and community-managed gardens. The evidence basis for claims regarding resilience and durability is restricted by a lack of longitudinal studies that evaluate the efficacy of UGI therapy over time. The potential of emergent data sources, such as high-frequency satellite constellations and Internet of Things (IoT) environmental sensors, for the continuous monitoring of urban green infrastructure (UGI) is still inadequately investigated. The monetization of ecological services in planning contexts is a topic of ongoing debate. Even though service value can assist in decision-making, critics claim that it may fail to consider non-monetizable cultural or environmental assets. The question of whether accessibility metrics should prioritize perceived quality, physical proximity, or a combination of the two is a topic of ongoing debate. The advantages of centralized versus decentralized systems are a topic of ongoing debate. Centralized UGI planning guarantees network connectivity, while decentralized, community-driven initiatives may more effectively address local requirements. In summary, the evaluation of urban green infrastructure (UGI) using geographic information systems (GIS) has evolved into a multifaceted, interdisciplinary instrument that can address complicated urban sustainability issues. The literature that was examined illustrates methodological advancements, expanded theme dimensions, and enhanced integration of social and environmental factors. However, the field's capacity to recommend global best practices is restricted by voids in regional coverage, methodological consistency, and longitudinal data. Inclusivity in the context of regional representation and stakeholder engagement, as well as the enhancement of technical capabilities and the standardization of key indicators, should be the primary focus of future research. Consequently, a UGI design that is equitable, resilient, and responsive to the demands of rapidly changing urban landscapes can be established using GIS to provide the empirical foundation. Table 2 Summary of Key Findings & Research Gaps Key Findings Research Gaps Accessibility and equity metrics are increasingly used in urban planning decisions Debate persists on whether to prioritize perceived quality vs. physical proximity in accessibility metrics GIS-based ecosystem service models quantify climate adaptation benefits like carbon sequestration Models require more validation in diverse geographic and socio-economic settings Visualization advances improve communication with policymakers and the public Few studies provide open data or reproducible workflows for transparency Conclusion A recent study synthesis shows that Geographic Information Systems (GIS) are critical for the assessment, planning, and management of urban green infrastructure (UGI). According to the research examined, GIS has evolved beyond ecological mapping to encompass social, economic, and aesthetic components, indicating a shift toward holistic sustainability assessment. The main findings of this study show that methodological diversity is essential for capturing the complexities of UGI, interdisciplinary methodologies enhance analytical and policy importance, and equitable data integration is critical for achieving inclusive urban planning outcomes. Three key findings emerge. Initially, GIS-based UGI assessments offer an empirical framework for data-driven decision-making at all levels, from localized neighborhood initiatives to citywide ecological network planning. Second, the combination of remote sensing, network analysis, and multi-criteria decision analysis allows for a thorough evaluation of both present and potential urban green infrastructure effectiveness. Recent methodological advances, such as AI-assisted categorization, participatory GIS, and transdisciplinary indicator frameworks, have improved the accuracy, inclusivity, and policy usefulness of UGI assessments. Practice recommendations highlight the need of incorporating GIS-based evaluation into institutional planning and management frameworks. To ensure equitable distribution of green space, urban planners must adopt uniform accessibility and connectivity standards, especially in disadvantaged districts. Environmental managers may use ecological service models, such as carbon sequestration and microclimate management, into project selection to maximize climate adaptation benefits. Policymakers must push for open data initiatives, provide funding for local capacity development in GIS applications, and institutionalize participatory mapping to include community perspectives into official planning processes. Researchers need to standardize essential measurements of urban green infrastructure performance in ecological, social, and aesthetic realms. This standardization would increase study comparability and enable meta-analyses, resulting in more generalizable findings. To increase the global applicability of GIS-based techniques, researchers should focus on underrepresented settings, particularly low-income and rapidly urbanizing places. Longitudinal designs may be used to monitor changes in UGI effectiveness over time, demonstrating resilience and adaptability. Policy suggestions center on incorporating GIS assessment findings into legal urban development frameworks. This integration must incorporate scenario-based modeling to forecast future urban development and climate change consequences, ensuring that UGI spending is sustainable and efficient over time. Governments must build legislative frameworks that encourage data interchange between public agencies, business sector organizations, and research institutions, reducing barriers to comprehensive geographical analysis. Future research should concentrate on four critical areas. Initially, methodological harmonization is required to synchronize definitions, indicators, and validation methods, allowing for robust comparisons across varied geographic and cultural contexts. Second, participatory GIS approaches must be expanded to integrate user viewpoints, cultural values, and informal contributions to green spaces, improving the alignment between planning objectives and lived experiences. Third, investigating the use of emerging technologies such as high-frequency satellite imagery, IoT-based environmental monitoring, and machine learning to increase real-time assessment capabilities. Fourth, research should look at the trade-offs between centralized and decentralized urban green infrastructure planning techniques, balancing ecological connectivity with community-driven design flexibility. To summarize, GIS-based UGI assessment offers a comprehensive and adaptable approach for addressing complex urban sustainability concerns. Stakeholders may utilize GIS to build, implement, and manage green infrastructure that is equitable, resilient, and adaptable to changing environmental and social requirements by stressing methodological rigor, inclusion, and cross-sector collaboration. The evidence base is sufficiently established to inform policy and practice; but realizing its full potential requires intentional attempts to standardize methodology, broaden geographic reach, and include multiple knowledge forms. References Bai, X., Li, Y., et al. (2022). Urban green space planning based on remote sensing and geographic information systems . International Journal of Modern Physics C , World Scientific. Buchavyi, O., Lovynska, M., et al. (2023). A GIS assessment of the green space percentage in a big industrial city (Dnipro, Ukraine) . Buildings , MDPI. Gorjian, M. (2024). A deep learning-based methodology to re-construct optimized re-structured mesh from architectural presentations (Doctoral dissertation, Texas A&M University). Texas A&M University. https://oaktrust.library.tamu.edu/items/0efc414a-f1a9-4ec3-bd19-f99d2a6e3392 Gorjian, M. (2025). Advances and challenges in GIS-based assessment of urban green infrastructure: A systematic review (2020–2024) . Preprints. https://doi.org/10.20944/preprints202508.0281.v1 Gorjian, M. (2025, July 15). Analyzing the relationship between urban greening and gentrification: Empirical findings from Denver, Colorado . SSRN. https://doi.org/10.2139/ssrn.5353201 Gorjian, M. (2025, July 10). Greening schoolyards and the spatial distribution of property values in Denver, Colorado [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.08894 Gorjian, M. (2025, July 26). Greening schoolyards and urban property values: A systematic review of geospatial and statistical evidence [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.19934 Gorjian, M. (2025). Green gentrification and community health in urban landscape: A scoping review of urban greening’s social impacts (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7225794/v1 Gorjian, M. (2025). Green schoolyard investments and urban equity: A systematic review of economic and social impacts using spatial-statistical methods [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7213563/v1 Gorjian, M. (2025). Green schoolyard investments influence local-level economic and equity outcomes through spatial-statistical modeling and geospatial analysis in urban contexts . arXiv. https://doi.org/10.48550/arXiv.2507.14232 Gorjian, M. (2025). Quantifying gentrification: A critical review of definitions, methods, and measurement in urban studies . Preprints. https://doi.org/10.20944/preprints202508.0150.v1 Gorjian, M. (2025). Schoolyard greening, child health, and neighborhood change: A comparative study of urban U.S. cities (arXiv:2507.08899) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.08899 Gorjian, M. (2025, July 11). The impact of greening schoolyards on residential property values [Working paper]. SSRN. https://doi.org/10.2139/ssrn.5348810 Gorjian, M. (2025). The impact of greening schoolyards on surrounding residential property values: A systematic review (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7235811/v1 Gorjian, M. (2025, July 29). Urban schoolyard greening: A systematic review of child health and neighborhood change [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7232642/v1 Gorjian, M., & Quek, F. (2024). Enhancing consistency in sensible mixed reality systems: A calibration approach integrating haptic and tracking systems [Preprint]. EasyChair. https://easychair.org/publications/preprint/KVSZ Gorjian, M., Caffey, S. M., & Luhan, G. A. (2024). Exploring architectural design 3D reconstruction approaches through deep learning methods: A comprehensive survey . Athens Journal of Sciences , 11(2), 1–29. https://www.athensjournals.gr/sciences/2024-6026-AJS-Gorjian-02.pdf Gorjian, M., Caffey, S. M., & Luhan, G. A. (2025). Analysis of design algorithms and fabrication of a graph-based double-curvature structure with planar hexagonal panels . arXiv. https://doi.org/10.48550/arXiv.2507.16171 Gorjian, M., Caffey, S. M., & Luhan, G. A. (2025). Exploring architectural design 3D reconstruction approaches through deep learning methods: A comprehensive survey . Athens Journal of Sciences , 12, 1–29. https://doi.org/10.30958/ajs.X-Y-Z Gorjian, M., Luhan, G. A., & Caffey, S. M. (2025). Analysis of design algorithms and fabrication of a graph-based double-curvature structure with planar hexagonal panels . arXiv preprint arXiv:2507.16171. https://doi.org/10.48550/arXiv.2507.16171 Raina, A. S., Mone, V., Gorjian, M., Quek, F., Sueda, S., & Krishnamurthy, V. R. (2024). Blended physical-digital kinesthetic feedback for mixed reality-based conceptual design-in-context . In Proceedings of the 50th Graphics Interface Conference (Article 6, pp. 1–16). ACM. https://doi.org/10.1145/3670947.3670967 Hailemariam, S. (2021). Suitable site selection for urban green space development using geographic information system and remote sensing based on multi criterion analysis . Environmental Monitoring and Assessment , Springer. Hoeben, R., & Posch, A. (2021). Green roof ecosystem services in various urban development types: A case study in Graz, Austria . Forests , MDPI. Huang, Y., Yu, S., et al. (2023). Analysis and optimized location selection of comprehensive green space supply in the central urban area of Hefei based on GIS . Sustainable Futures , Elsevier. Kim, J., Oh, S., et al. (2021). Establishment of a geographic information system-based algorithm to analyze suitable locations for green roofs and roadside trees . Applied Sciences , MDPI. Mironova, O. (2021). GIS modeling of green infrastructure of Mediterranean cities for the management of urbanized ecosystems . Arid Ecosystems , Pleiades Publishing Inc. Mobarak, M., Shrahily, R., et al. (2022). Assessing green infrastructures using GIS and the multi-criteria decision-making method: the case of the Al Baha region (Saudi Arabia) . Ekológia (Bratislava) , Sciendo. Mohammed, R., & Hammo, M. (2023). Evaluate of green space (parks) in Duhok city by use image satellite, Google Earth, GIS, NDVI, and field survey techniques . Kufa Journal for Agricultural Sciences , University of Kufa. Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews . BMJ , 372:n71. Pouya, S., & Aghlmand, S. (2022). Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic . Land , MDPI. Rachid, M., Elmostafa, M., et al. (2024). Assessing carbon storage and sequestration benefits of urban greening in Nador City, Morocco, utilizing GIS and the InVEST model . Ecological Indicators , Elsevier. Rodríguez-Espinosa, V., Aguilera-Benavente, F., et al. (2020). Green infrastructure design using GIS and spatial analysis: A proposal for the Henares Corridor (Madrid–Guadalajara, Spain) . Landscape Research , Taylor & Francis. Vîlcea, C., & Șoșea, E. (2020). A GIS-based analysis of the urban green space accessibility in Craiova city, Romania . Geografisk Tidsskrift-Danish Journal of Geography , Taylor & Francis. Waheeb, M., Zerouali, A., et al. (2023). Enhancing sustainable urban planning through GIS and multiple-criteria decision analysis: A case study of green space infrastructure in Taif Province, Saudi Arabia . Sustainability , MDPI. Wanghe, K., Guo, H., et al. (2020). Gravity model toolbox: An automated and open-source ArcGIS tool to build and prioritize ecological corridors in urban landscapes . Remote Sensing , MDPI. Wu, X., Liu, J., & Hou, Y. (2025). Data and methods for assessing urban green infrastructure using GIS: A systematic review . PLOS ONE , 20(6): e0324906. https://doi.org/10.1371/journal.pone.0324906 Xu, Z., Luo, J., et al. (2020). Accurate suitability evaluation of large-scale roof greening based on RS and GIS methods . Water , MDPI. Yang, F. (2024). Research on optimization strategies for urban park green space planning in Nanjing based on GIS from the perspectives of network analysis and Thiessen polygon theory . Journal of Asian Architecture and Building Engineering , Taylor & Francis. Zhang, X., Yan, Y., et al. (2024). Visual assessment of historic landmarks based on GIS and survey: A study of view and viewing of Tiger Hill in Suzhou, China . Sustainability , MDPI. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7349702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":499021591,"identity":"66f25585-a0a9-44dc-aff6-3ba077b8d317","order_by":0,"name":"Mahshid Gorjian","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-9135-0687","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Mahshid","middleName":"","lastName":"Gorjian","suffix":""}],"badges":[],"createdAt":"2025-08-11 21:38:17","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7349702/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7349702/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88944594,"identity":"df8ffcba-afca-4e57-91b1-08e789dad65c","added_by":"auto","created_at":"2025-08-13 04:34:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97778,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Introduction section.\u003c/p\u003e","description":"","filename":"Uf1.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/7d1a3a3810e737db919925d3.png"},{"id":88944505,"identity":"bdbc26be-dc85-4d47-a296-12d6d70baed2","added_by":"auto","created_at":"2025-08-13 04:26:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56635,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methods section.\u003c/p\u003e","description":"","filename":"Uf2.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/33e52050ddb350e566f9e8e8.png"},{"id":88944592,"identity":"4616dacb-b136-402f-9c2b-b8ed15a8624e","added_by":"auto","created_at":"2025-08-13 04:34:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76084,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methods section.\u003c/p\u003e","description":"","filename":"Uf3.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/108bd00f4e3abdbfe1c9f3be.png"},{"id":88944593,"identity":"5ed7958e-3c6d-4bd6-b57a-81ba67bace1e","added_by":"auto","created_at":"2025-08-13 04:34:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48153,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"Uf4.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/845f9722dc4eebc0c1d25c18.png"},{"id":88944510,"identity":"f833ddb8-d118-4698-88df-178d5f0fd92f","added_by":"auto","created_at":"2025-08-13 04:26:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57378,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"Uf5.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/cc7e065aab65ece1aea46f45.png"},{"id":88944511,"identity":"849ade9e-be6f-4b79-af82-13a71728e76a","added_by":"auto","created_at":"2025-08-13 04:26:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":132985,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"Uf6.png","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/bf480b34b9a43aa18264db65.png"},{"id":88945064,"identity":"7c826d34-d674-4530-b1a2-0dad3bb140fc","added_by":"auto","created_at":"2025-08-13 04:42:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1226212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7349702/v1/a41faba3-bc69-467c-ab32-0ab073573cc4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAdvances in GIS-Based Assessment of Urban Green Infrastructure: A Systematic Review (2020–2024)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eBackground and Rationale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increased speed of urbanization has added to the pressures on ecological systems, resource consumption, and urban living standards. The United Nations Sustainable Growth Goal 11 emphasizes the connection between urban growth, human well-being, and environmental sustainability (Wu et al., 2025). As a result of the persistent contradictions between the integrity of natural ecosystems and socioeconomic development, green infrastructure (GI) has emerged as a strategic approach that balances environmental, social, and economic interests. GI provides a variety of environmental services, including the regulation of climate, the enhancement of biodiversity, and the provision of cultural and recreational benefits (Wu et al., 2025). The guiding themes underscore the significance of interdisciplinary collaboration, the integration of urban and rural habitats, the further development of the connection between ecological structure and function, and the importance of multifunctionality. The absence of a globally accepted definition or systematic classification of GI, despite its significance, results in inconsistencies in planning, regulation, and implementation (Wu et al., 2025).\u003c/p\u003e\n\u003cp\u003eUrban Natural Infrastructure (UGI), a subset of Green Infrastructure (GI), is primarily concerned with the natural assets that exist in urban environments. These assets consist of biological corridors, gardens, parks, wetlands, and green roofs. UGI strengthens environmental resilience, promotes social cohesion, elevates aesthetic and cultural values, and enhances public health. To effectively manage UGI, it is crucial to have instruments that can incorporate environmental and socioeconomic factors, evidence-based evaluation procedures, and accurate geographical data. Geographic Information Systems (GIS) have emerged as an essential tool in this field. \u0026nbsp; \u0026nbsp; \u0026nbsp;GIS utilizes geographical data from remote sensing, government records, and in situ surveys to conduct multi-scale assessments of accessibility, ecological function, and vulnerability to promote sustainable and equitable urban design (Wu et al., 2025).\u003c/p\u003e\n\u003cp\u003eEven though there have been numerous of studies conducted on GIS applications in related sectors, they demonstrate significant deficiencies when considering a UGI-specific perspective. Wu et al. (2025) conducted a comprehensive analysis of urban ecosystem services (UES) remote sensing research from 2010 to 2020. The review offered a thorough examination of the improvements to GIS; however, it did not address the specific planning, design, and management components of urban green infrastructure (UGI). In the past, research has mainly concentrated on hazard management, which involves the analysis of landscape fragmentation and the mitigation of emergency risk. Nevertheless, these studies frequently concentrate on ecological and biological outcomes, missing evaluations of aesthetic value, accessibility, and social justice (Wu et al., 2025). In the same vein, research on the restoration of urban vacant land has underscored the significance of GIS in ecological planning, even though it has not yet fully integrated transdisciplinary indicators or methodological advancements until 2020. The current evaluations\u0026apos; ability to provide exhaustive, contemporary, and practice-oriented UGI assessments is disrupted by these omissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver the past five years, there have been substantial methodological advancements in the assessment of UGI. Enhanced multi-criteria decision analysis (MCDA) methodologies, participatory GIS strategies that incorporate local feedback, automated ecological pathway modeling, and the application of artificial intelligence for image classification are all included in this. However, no systematic research has integrated these accomplishments to provide a comprehensive understanding of the ecological, social, and economic aspects of GIS-based urban green infrastructure evaluation. Inability to establish a consistent evidence basis renders urban planners, legislators, and academics ineffective of implementing GIS strategies that address both technological and social challenges in the absence of a synthesis.\u003c/p\u003e\n\u003cp\u003eIn this investigation, empirical studies that were published between January 1, 2020, and June 30, 2024, and employed GIS to investigate UGI are thoroughly examined. This results in the modification of existing deficiencies. The review incorporates evidence from environmental, social, and transdisciplinary perspectives, thereby incorporating methodological diversity and topic breadth. To ensure transparency and reproducibility, the research is conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives and Research Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial objective of the review is to determine the components of UGI that are evaluated using GIS. Secondly, it will provide a thorough examination and assessment of the methodological approaches that are implemented. Thirdly, it will examine the opportunities and innovations in GIS-based UGI assessment that are the consequence of interdisciplinary integration and new technologies. The review has three objectives. These objectives are consistent with the PECO framework:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Population (P)-designated urban areas that have been equipped with a diverse range of green infrastructure, such as wetlands, gardens, green roofs, parks, and natural corridors.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Exposure (E): The process of evaluating, simulating, or designing urban green infrastructure (UGI) using GIS technologies, tools, and data sources.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Comparison (C): Assessment methodologies that are based on geographic information systems (GIS) demonstrate variations in their spatial dimensions, indicators, and contexts.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Result (R): Assessment results for environmental performance, accessibility, social benefits, aesthetic value, resilience, and planning recommendations.\u003c/p\u003e\n\u003cp\u003eThis review examines a variety of specific research topics to accomplish these objectives:\u003c/p\u003e\n\u003cp\u003e1. Which categories of UGI and associated services are evaluated most frequently using GIS?\u003c/p\u003e\n\u003cp\u003e2. Which data sources, analytical techniques, and validation strategies are employed in modern UGI assessments that are based on GIS?\u003c/p\u003e\n\u003cp\u003e3. In the context of the implementation of artificial intelligence, participatory procedures, and transdisciplinary frameworks, how have methodological methods evolved since 2020?\u003c/p\u003e\n\u003cp\u003eWhat are the remaining deficiencies in the evaluation of urban green infrastructure using GIS, and how can these issues be addressed in future research?\u003c/p\u003e\n\u003cp\u003eBy structuring the synthesis around these inquiries, the review offers a diverse range of stakeholder\u0026rsquo;s valuable information. The results may be used by urban planners and policymakers to determine the most suitable GIS methodologies for context-specific issues, such as spatial disparities and climate adaptation. Potential for multidisciplinary collaboration and methodological deficiencies may be identified by researchers. Environmental management professionals may capitalize on the technology and data integration strategies that have been prioritized to optimize their investments in green infrastructure.\u003c/p\u003e\n\u003cp\u003eThe urgency of exhaustive, precise, and timely UGI assessments is underscored by the growing urgency of public health concerns, biodiversity loss, and climate change. A method of ensuring that green infrastructure development follows the broad criteria of sustainability, resilience, and equity as urban populations expand and land-use demands increase is evaluated using geographic information systems (GIS). The successful implementation of geographic information systems (GIS) necessitates variables that surpass technical proficiency. It is essential to guarantee that UGI criteria are established, that reliable data is accessible, that community perspectives are included, and that institutional and policy frameworks are followed. The systematic synthesis of knowledge is essential for the global viability of effective UGI initiatives that satisfy these requirements.\u003c/p\u003e\n\u003cp\u003eThis research defines the most recent advancements in GIS-based UGI assessment within the broader context of sustainable urban development. Additionally, it contextualizes them. It is acknowledged that UGI is a multifaceted asset with value in the ecological, social, and economic spheres, and that GIS can provide the comprehensive understanding required for its preservation, enhancement, and equitable distribution when methodological precision and interdisciplinary insight are employed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eProtocol and Reporting Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review followed the PRISMA 2020 principles, ensuring methodological transparency and reproducibility (Page et al., 2021). Prior to conducting the literature search, a protocol was developed that detailed the review objectives, inclusion and exclusion criteria, search approach, quality evaluation, and synthesis process. The review lacked proper PROSPERO registration since it focused exclusively on methodological synthesis and not clinical results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe eligibility criteria were developed using the Population, Exposure, Comparator, and Outcome (PECO) architecture. The target population included metropolitan areas with green infrastructure components like parks, gardens, green roofs, wetlands, ecological corridors, and more vegetated spaces. The exposure included using Geographic Information Systems (GIS) to evaluate, model, and plan urban green infrastructure. The comparison included modifications to GIS procedures, data sources, analytical techniques, and spatial settings. The findings included measures for ecological performance, accessibility, social benefits, aesthetic characteristics, resilience, and policy and planning recommendations. Eligible study designs were empirical investigations with qualitative, quantitative, or mixed approaches that presented novel research. Review articles, conceptual papers, conference abstracts, and non-peer-reviewed documents were excluded. Only studies published in English between January 1, 2020, and June 30, 2024, were evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformation Sources and Search Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search strategy used controlled vocabulary and keyword terms such as \u0026quot;green infrastructure,\u0026quot; \u0026quot;green space,\u0026quot; \u0026quot;green roof,\u0026quot; and \u0026quot;GIS\u0026quot; or \u0026quot;Geographic Information System.\u0026quot; Two major bibliographic databases were searched: Web of Science Core Collection (Science Citation Index and Social Science Citation Index) and Scopus. To account for the most recent methodological improvements, the date range was limited to January 1, 2020, to June 30, 2024. The extra material contains the complete search strings for each database. These phrases have been modified to each database\u0026apos;s indexing architecture to improve retrieval sensitivity.\u003c/p\u003e\n\u003cp\u003eA divided process was used to choose the studies. Initially, two reviewers independently reviewed titles and abstracts using the eligibility criteria. In the second stage, complete texts of potentially relevant papers were received and screened for inclusion. Discrepancies among reviewers were resolved through debate, and in cases where consensus was not possible, a third reviewer made the ultimate judgment. Screening decisions were recorded, and reasons for exclusion at the full-text stage were documented. The inter-reviewer agreement during title and abstract screening was high, with a kappa coefficient greater than 0.85, indicating excellent agreement.\u003c/p\u003e\n\u003cp\u003eData extraction was done individually by two reviewers using a pretested extraction form in MS Excel. Extracted variables include bibliographic information, country and study location, GIS data sources, UGI classification, research objectives, analytical methodology, validation methods, and main findings. The extraction form was improved after pilot testing on three studies to ensure clarity and comprehensiveness. No specific systematic review software, such as Covidence, was used; nevertheless, consistent version control was maintained through shared cloud-based storage to avoid data loss or duplication.\u003c/p\u003e\n\u003cp\u003eThe quality assessment included four criteria derived from previous methodological studies (Wu et al., 2025): clarity of aims and research inquiries, explicit identification of GIS software and version, transparency and reproducibility of the research methodology, and provision of an optimization plan based on results. Each condition was assigned a value of one (fully met), 0.5 (partially met), or zero (not met). Two reviewers independently examined each manuscript, settling disagreements through consensus, with a third reviewer available for arbitration. The ratings were combined to provide a four-point overall quality score. The average quality score across the papers included was 3.2, indicating a high level of methodological rigor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data synthesis used a thematic narrative framework to categorize studies based on assessment focus (e.g., accessibility, ecological benefits, social benefits, visual aesthetics), methodological approach (e.g., spatial analysis, network analysis, multi-criteria decision analysis), and type of urban green infrastructure evaluated. Because of the heterogeneity in study designs, indices, and spatial contexts, quantitative meta-analysis was not feasible. Nonetheless, comparative qualitative synthesis enabled the identification of methodological trends, technological developments, and multidisciplinary integrations that have emerged after 2020.\u003c/p\u003e\n\u003cp\u003eThis evaluation follows PRISMA 2020 principles, with a PRISMA flow diagram outlining the selection process and a checklist included in the supplemental materials. The approach ensures that the search strategy, selection criteria, data extraction, quality assessment, and synthesis are all visible and reproducible.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStudy Selection\u003c/h2\u003e\u003cp\u003eA comprehensive examination of the Scopus and Web of Science Core Collection databases revealed 1,592 documents. The dataset was cleansed of duplicates, resulting in 1,141 records. For a variety of reasons, including retraction (n\u0026thinsp;=\u0026thinsp;3), non-English language (n\u0026thinsp;=\u0026thinsp;34), publication type (book section or conference proceedings; n\u0026thinsp;=\u0026thinsp;174), review format (n\u0026thinsp;=\u0026thinsp;27), and a lack of keywords in titles (n\u0026thinsp;=\u0026thinsp;398) and abstracts (n\u0026thinsp;=\u0026thinsp;164), 800 articles were eliminated during the preliminary evaluation of titles and abstracts. This led to the identification of 340 articles that necessitated a full-text review. The full-text screening process eliminated papers that were unrelated to UGI evaluation (n\u0026thinsp;=\u0026thinsp;67), not focused on UGI (n\u0026thinsp;=\u0026thinsp;67), inconsistent with the review objectives (n\u0026thinsp;=\u0026thinsp;11), and lacked pertinent key terms (n\u0026thinsp;=\u0026thinsp;112). The inclusion criteria were satisfied by twenty articles. T\u003c/p\u003e\u003cp\u003eThe 20 studies were published in 20 distinct journals, with MDPI outlets (n\u0026thinsp;=\u0026thinsp;7) being the most prevalent (n\u0026thinsp;=\u0026thinsp;7). Elsevier (n\u0026thinsp;=\u0026thinsp;4), Taylor \u0026amp; Francis (n\u0026thinsp;=\u0026thinsp;3), and individual instances from Springer, Pleiades Publishing Inc., Sciendo, World Scientific, Tehran Urban Research and Planning Center, and University of Kufa were also included. The most participants were found in China (n\u0026thinsp;=\u0026thinsp;6), Saudi Arabia (n\u0026thinsp;=\u0026thinsp;2), and Spain (n\u0026thinsp;=\u0026thinsp;2), with the research sites dispersed across 13 nations. The proportion of research locations in Asia was the highest (n\u0026thinsp;=\u0026thinsp;12), followed by Europe (n\u0026thinsp;=\u0026thinsp;5), and Africa (n\u0026thinsp;=\u0026thinsp;3). Government agencies, commercial corporations, academic institutions, international organizations, and other sources (including open-source repositories, satellite datasets, and web-based platforms) where the five categories into which GIS data sources were split. To enhance reliability, numerous studies integrated multiple data sources.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe research in this domain was primarily concerned with the assessment of social value, environmental benefits, accessibility, and distribution. NDVI and land-cover classification were typically employed in distribution analyses to delineate green space areas and monitor changes in time (Buchavyi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pouya \u0026amp; Aghlmand, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cost-distance metrics were employed to represent trip impedance, and network analysis was employed to evaluate accessibility by modeling walking and driving routes (V\u0026icirc;lcea \u0026amp; ǘoșea, 2020; Yang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Models such as InVEST, which quantify carbon storage and sequestration, were employed to investigate ecological benefits (Rachid et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hoeben \u0026amp; Posch, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The social advantages were assessed by combining survey data with GIS outputs and analyzing perceived accessibility, utilization patterns, and health consequences (Mohammed \u0026amp; Hammo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMethodological Approaches\u003c/h2\u003e\u003cp\u003eThe critical appraisal of numerous studies revealed a high degree of methodological integration, particularly when participatory data collection and remote sensing were combined. However, the insufficient coverage of informal or non-public green spaces, which could substantially enhance urban ecosystem services, remained a persistent issue.\u003c/p\u003e\u003cp\u003eAssessments of potential efficacy focused on the intended design, connectivity, and suitability of the site. Before overlay analysis, suitability mapping frequently implements multi-criteria decision analysis (MCDA) methodologies, particularly the Analytical Hierarchy Process (AHP), to allocate weights to environmental and socioeconomic variables (Xu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mobarak et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Waheeb et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hailemariam, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Connectivity assessments employed instruments such as GuidosToolBox to assess landscape fragmentation and identify biological corridors (Mironova, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wanghe et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Priority zones for green space enhancement or reconfiguration were identified through scenario-based simulations, service demand mapping, and weighted overlay analysis (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rodr\u0026iacute;guez-Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese works exhibited creativity by integrating GIS with specialized modeling tools to enable scenario testing and optimization. The validation methodologies were inconsistent; some studies included field verification, while others relied solely on model outputs without ground-truthing.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMethodological Approaches\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTools/Software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdvantages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCDA (AHP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArcGIS, QGIS, Analytical Hierarchy Process modules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntegrates multiple environmental \u0026amp; socio-economic criteria; adaptable to diverse contexts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSubjectivity in weight assignment; requires expert input\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNetwork Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArcGIS Network Analyst, GIS-based routing tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModels\u0026rsquo; accessibility via walking/driving routes; supports equity-focused planning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData quality dependent on accurate road/transport layers; may overlook informal paths\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Image Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePython (TensorFlow, Keras), ENVI, high-res imagery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAutomates land-cover classification; handles large datasets; increases spatial precision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRequires substantial training data; sensitive to image quality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBy creating algorithms, decision-support systems, and data processing improvements, methodologically oriented research enhanced GIS applications. A few examples are the development of GIS algorithms for the selection of green roof and roadside tree locations using computational fluid dynamics (Kim et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and GIS-based decision-support systems for the creation of ecological networks (Bai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The researchers underscored the importance of AI-driven image categorization, crowd-sourced geolocation information, and high-resolution remote sensing data to enhance the analytical depth and geographical precision thereof.\u003c/p\u003e\u003cp\u003eTechnical rigor and the potential for scalability are the strengths of these investigations. However, only a small number of studies specifically investigated the applicability of tools in situations with limited technical capabilities or user accessibility.\u003c/p\u003e\u003cp\u003eDiversity of Sources and Data Integration: Most studies employed a diverse array of datasets, frequently incorporating governmental geographic data, satellite imagery, and in situ observations. This approach enhanced reliability, but it also led to inconsistencies in the quality and resolution of data.\u003c/p\u003e\u003cp\u003eProcedures for Validation: Significant variation has been observed in validation methodologies. Field surveys and stakeholder consultations were instrumental in verifying the accuracy of GIS outputs; however, the lack of unambiguous validation in numerous studies restricted the confidence in their applicability.\u003c/p\u003e\u003cp\u003eInterdisciplinary Methodologies: In recent years, there has been a gradual integration of ecological, social, and planning perspectives, enabling more thorough evaluations of UGI. These methods acknowledged green infrastructure as a socioeconomic resource and an ecological habitat.\u003c/p\u003e\u003cp\u003eVisualization Improvements: The transition to more sophisticated GIS visualizations facilitated the communication of results to the public and policymakers in a more transparent manner.\u003c/p\u003e\u003cp\u003eGIS was universally recognized as an effective platform for the integration of geographical and non-spatial variables to evaluate UGI. The efficacy of MCDA methodologies for appropriateness analysis and the significance of accessibility metrics in equity-centered planning were both agreed upon. There was a disagreement regarding the most effective indicators for quantifying ecological benefits, with some studies advocating for vegetation indices and others emphasizing modeled ecosystem service outputs. The effectiveness of GIS systems in evaluating artistic and cultural qualities was a topic of debate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 - Study Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor/Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUGI Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGIS Method\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBai et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision-support system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistoric landmarks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisual assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScenario simulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRachid et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorocco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban greenery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcosystem services modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal Findings\u003c/h2\u003e\u003cp\u003eThe incorporation of 20 recent empirical studies illustrates that GIS-based evaluations of urban green infrastructure (UGI) have developed into projects that are highly transdisciplinary, data-intensive, and methodologically diverse. The review aimed to identify the most frequently investigated aspects of UGI, clarify and evaluate the methodological approaches used, and investigate the advances and opportunities that result from technology integration and interdisciplinary collaboration. From this context, three significant discoveries are made. Initially, GIS approaches broadened beyond ecological mapping to encompass social, economic, and aesthetic variables, indicating a transition to a more comprehensive assessment of sustainability. Secondly, the analytical profundity and relevance of decision-making have been enhanced by methodological advancements such as ecological corridor modeling, multi-criteria decision analysis (MCDA), and artificial intelligence for image classification. The analytical robustness has been enhanced by the incorporation of multiple data sources, including government cadastral data, high-resolution remote sensing, and crowd-sourced information. However, the quality and compatibility of the data have been a source of concern.\u003c/p\u003e\u003cp\u003eIn contrast to previous research that primarily concentrated on distant sensing of urban ecosystem services or hazard-specific applications, this synthesis distinguishes post-2020 research by emphasizing urban green infrastructure. Previous research has concentrated on UGI as an ecological resource, with an emphasis on the potential for environmental mitigation, biodiversity, and vegetation cover (Mironova, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wanghe et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The examined studies expand this perspective to encompass aesthetic valuation, spatial justice, and accessibility, in accordance with contemporary urban sustainability frameworks that equally prioritize environmental resilience and social equity. This increase is in accordance with environmental planning trends that emphasize the importance of integrated socio-ecological systems thinking. The methodological innovation is the integration of participatory and perception-based data with spatial analysis, a practice that has been less prevalent in previous research. While ecological link modeling and NDVI mapping are significant methodologies, the incorporation of stakeholder surveys, crowd-sourced geolocation data, and urban morphology measurements introduces analytical features that were substantially absent in studies conducted prior to 2020.\u003c/p\u003e\u003cp\u003eIn other regions, the findings are consistent with the current consensus. Accessibility is a critical component of the utilization of urban green infrastructure and a matter of equity in urban planning, as evidenced by a multitude of studies (V\u0026icirc;lcea \u0026amp; ǘoșea, 2020; Yang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is widely acknowledged that MCDA frameworks, particularly AHP, are beneficial for site suitability analysis due to their ability to incorporate a variety of socioeconomic, environmental, and geographical factors (Mobarak et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hailemariam, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, there are debates regarding the quantification of ecological benefits. Some studies concentrate on biophysical measurements, such as NDVI and canopy cover, while others advocate for ecosystem service models, such as InVEST, which offer function-based metrics such as carbon sequestration (Rachid et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hoeben \u0026amp; Posch, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The presentation underscores the substantial disciplinary distinctions between conventional ecological monitoring practices and service-oriented valuation methodologies. Additionally, there is a lack of consensus regarding the most effective method for capturing and integrating aesthetic and cultural values into GIS frameworks. While some academicians employ visual impact evaluation tools, others rely on subjective perception surveys.\u003c/p\u003e\u003cp\u003eThe evidentiary foundation possesses significant values. Clear delineations of data sources, analytical tools, and parameter configurations are frequently observed, indicating a high level of methodological transparency. To enhance the confidence of their findings, numerous studies apply validation procedures, including stakeholder feedback, satellite image comparison, and field verification. The applicability of GIS-based UGI assessment to various socioeconomic and ecological ecosystems is illuminated by the diverse contexts of Asia, Europe, and Africa. In addition, the capacity of evaluations to address the complex elements of urban sustainability is enhanced by the growing interdisciplinarity that integrates ecology, urban planning, sociology, and computer science.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eHowever, it is evident that there are constraints. The geographical representation is inconsistent, with a significant preference for middle- and high-income countries, particularly in East Asia and specific regions of Europe. Urban green infrastructure has the potential to provide substantial resilience benefits to low-income communities; however, they are underrepresented. A potential linguistic bias may have been introduced by restricting the language to English, which could have resulted in the exclusion of pertinent studies published in other languages. Grey literature, which could include practical advancements that have not yet been addressed in academic journals, is disregarded in favor of peer-reviewed journal papers. Selection bias may be present since the databases Web of Science and Scopus lack every relevant regional or technical journals. Meta-analyses face challenges by the methodological heterogeneity among studies, which is characterized by disparities in spatial resolution, indicator definitions, and validation procedures, thereby limiting the comparability of results. The absence of specific reproducibility measures, such as publicly accessible datasets or code, precludes openness, even when methodological descriptions are extensive.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eResearch Gaps and Future Directions\u003c/h2\u003e\u003cp\u003eThe practical implications are immediate and actionable. Urban planners can utilize GIS-based MCDA frameworks to facilitate the equitable deployment of UGI, particularly in underserved neighborhoods with apparent accessibility issues. By incorporating ecological connectivity models into municipal planning procedures, it is guaranteed that new urban green infrastructure investments will benefit broader habitat networks. Therapies that are both ecologically effective and socially meaningful can be achieved by incorporating biophysical and perceptual data into the design process. Carbon sequestration and microclimate regulation models can be employed by environmental managers to select urban green infrastructure projects that offer substantial climate adaptation benefits.\u003c/p\u003e\u003cp\u003eResearch methodology has the potential to be enhanced. Improved study comparability and meta-analytical synthesis will be enabled by the establishment of agreements on critical variables, including ecological function, social benefit, and accessibility. By improving participatory GIS methods, it is possible to foster greater community engagement and guarantee that UGI designs are in accordance with local cultural concepts and requirements. Additional research on AI-assisted classification, automated suitability mapping, and real-time monitoring systems has the potential to enhance accuracy and reduce the time required for analysis. It is essential to conduct cross-continental comparison analyses to comprehend the ways in which contextual factors affect the performance and perception of UGI.\u003c/p\u003e\u003cp\u003eSimilarly, the policy implications are substantial. GIS-based UGI studies can be incorporated into the regulatory planning processes of municipal governments to improve zoning decisions, infrastructure allocations, and climate resilience efforts. The challenge of limited data accessibility in specific regions could be mitigated by open data initiatives, while capacity-building programs could enhance the technical skills necessary for complex GIS analysis. The legitimacy and community support of UGI initiatives will be enhanced by the integration of participatory mapping and validation into policy procedures.\u003c/p\u003e\u003cp\u003eNumerous inquiries remain unresolved. The question of whether integrated indices can effectively encompass both without reductionism is underscored by the fact that the appropriate balance of ecological and social variables in urban green infrastructure evaluation remains unresolved. Ecosystem services and social benefits that are not completely documented are substantially influenced by informal green areas, including vacant lots and community-managed gardens. The evidence basis for claims regarding resilience and durability is restricted by a lack of longitudinal studies that evaluate the efficacy of UGI therapy over time. The potential of emergent data sources, such as high-frequency satellite constellations and Internet of Things (IoT) environmental sensors, for the continuous monitoring of urban green infrastructure (UGI) is still inadequately investigated.\u003c/p\u003e\u003cp\u003eThe monetization of ecological services in planning contexts is a topic of ongoing debate. Even though service value can assist in decision-making, critics claim that it may fail to consider non-monetizable cultural or environmental assets. The question of whether accessibility metrics should prioritize perceived quality, physical proximity, or a combination of the two is a topic of ongoing debate. The advantages of centralized versus decentralized systems are a topic of ongoing debate. Centralized UGI planning guarantees network connectivity, while decentralized, community-driven initiatives may more effectively address local requirements.\u003c/p\u003e\u003cp\u003eIn summary, the evaluation of urban green infrastructure (UGI) using geographic information systems (GIS) has evolved into a multifaceted, interdisciplinary instrument that can address complicated urban sustainability issues. The literature that was examined illustrates methodological advancements, expanded theme dimensions, and enhanced integration of social and environmental factors. However, the field's capacity to recommend global best practices is restricted by voids in regional coverage, methodological consistency, and longitudinal data. Inclusivity in the context of regional representation and stakeholder engagement, as well as the enhancement of technical capabilities and the standardization of key indicators, should be the primary focus of future research. Consequently, a UGI design that is equitable, resilient, and responsive to the demands of rapidly changing urban landscapes can be established using GIS to provide the empirical foundation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Key Findings \u0026amp; Research Gaps\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch Gaps\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccessibility and equity metrics are increasingly used in urban planning decisions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDebate persists on whether to prioritize perceived quality vs. physical proximity in accessibility metrics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGIS-based ecosystem service models quantify climate adaptation benefits like carbon sequestration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModels require more validation in diverse geographic and socio-economic settings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisualization advances improve communication with policymakers and the public\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFew studies provide open data or reproducible workflows for transparency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA recent study synthesis shows that Geographic Information Systems (GIS) are critical for the assessment, planning, and management of urban green infrastructure (UGI). According to the research examined, GIS has evolved beyond ecological mapping to encompass social, economic, and aesthetic components, indicating a shift toward holistic sustainability assessment. The main findings of this study show that methodological diversity is essential for capturing the complexities of UGI, interdisciplinary methodologies enhance analytical and policy importance, and equitable data integration is critical for achieving inclusive urban planning outcomes.\u003c/p\u003e\u003cp\u003eThree key findings emerge. Initially, GIS-based UGI assessments offer an empirical framework for data-driven decision-making at all levels, from localized neighborhood initiatives to citywide ecological network planning. Second, the combination of remote sensing, network analysis, and multi-criteria decision analysis allows for a thorough evaluation of both present and potential urban green infrastructure effectiveness. Recent methodological advances, such as AI-assisted categorization, participatory GIS, and transdisciplinary indicator frameworks, have improved the accuracy, inclusivity, and policy usefulness of UGI assessments.\u003c/p\u003e\u003cp\u003ePractice recommendations highlight the need of incorporating GIS-based evaluation into institutional planning and management frameworks. To ensure equitable distribution of green space, urban planners must adopt uniform accessibility and connectivity standards, especially in disadvantaged districts. Environmental managers may use ecological service models, such as carbon sequestration and microclimate management, into project selection to maximize climate adaptation benefits. Policymakers must push for open data initiatives, provide funding for local capacity development in GIS applications, and institutionalize participatory mapping to include community perspectives into official planning processes.\u003c/p\u003e\u003cp\u003eResearchers need to standardize essential measurements of urban green infrastructure performance in ecological, social, and aesthetic realms. This standardization would increase study comparability and enable meta-analyses, resulting in more generalizable findings. To increase the global applicability of GIS-based techniques, researchers should focus on underrepresented settings, particularly low-income and rapidly urbanizing places. Longitudinal designs may be used to monitor changes in UGI effectiveness over time, demonstrating resilience and adaptability.\u003c/p\u003e\u003cp\u003ePolicy suggestions center on incorporating GIS assessment findings into legal urban development frameworks. This integration must incorporate scenario-based modeling to forecast future urban development and climate change consequences, ensuring that UGI spending is sustainable and efficient over time. Governments must build legislative frameworks that encourage data interchange between public agencies, business sector organizations, and research institutions, reducing barriers to comprehensive geographical analysis.\u003c/p\u003e\u003cp\u003eFuture research should concentrate on four critical areas. Initially, methodological harmonization is required to synchronize definitions, indicators, and validation methods, allowing for robust comparisons across varied geographic and cultural contexts. Second, participatory GIS approaches must be expanded to integrate user viewpoints, cultural values, and informal contributions to green spaces, improving the alignment between planning objectives and lived experiences. Third, investigating the use of emerging technologies such as high-frequency satellite imagery, IoT-based environmental monitoring, and machine learning to increase real-time assessment capabilities. Fourth, research should look at the trade-offs between centralized and decentralized urban green infrastructure planning techniques, balancing ecological connectivity with community-driven design flexibility.\u003c/p\u003e\u003cp\u003eTo summarize, GIS-based UGI assessment offers a comprehensive and adaptable approach for addressing complex urban sustainability concerns. Stakeholders may utilize GIS to build, implement, and manage green infrastructure that is equitable, resilient, and adaptable to changing environmental and social requirements by stressing methodological rigor, inclusion, and cross-sector collaboration. The evidence base is sufficiently established to inform policy and practice; but realizing its full potential requires intentional attempts to standardize methodology, broaden geographic reach, and include multiple knowledge forms.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBai, X., Li, Y., et al. (2022). \u003cem\u003eUrban green space planning based on remote sensing and geographic information systems\u003c/em\u003e. \u003cstrong\u003eInternational Journal of Modern Physics C\u003c/strong\u003e, World Scientific.\u003c/li\u003e\n\u003cli\u003eBuchavyi, O., Lovynska, M., et al. (2023). \u003cem\u003eA GIS assessment of the green space percentage in a big industrial city (Dnipro, Ukraine)\u003c/em\u003e. \u003cstrong\u003eBuildings\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2024). \u003cem\u003eA deep learning-based methodology to re-construct optimized re-structured mesh from architectural presentations\u003c/em\u003e (Doctoral dissertation, Texas A\u0026amp;M University). Texas A\u0026amp;M University. https://oaktrust.library.tamu.edu/items/0efc414a-f1a9-4ec3-bd19-f99d2a6e3392\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eAdvances and challenges in GIS-based assessment of urban green infrastructure: A systematic review (2020\u0026ndash;2024)\u003c/em\u003e. Preprints. https://doi.org/10.20944/preprints202508.0281.v1\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025, July 15). \u003cem\u003eAnalyzing the relationship between urban greening and gentrification: Empirical findings from Denver, Colorado\u003c/em\u003e. SSRN. https://doi.org/10.2139/ssrn.5353201\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025, July 10). \u003cem\u003eGreening schoolyards and the spatial distribution of property values in Denver, Colorado\u003c/em\u003e[Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.08894\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025, July 26). \u003cem\u003eGreening schoolyards and urban property values: A systematic review of geospatial and statistical evidence\u003c/em\u003e [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.19934\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eGreen gentrification and community health in urban landscape: A scoping review of urban greening\u0026rsquo;s social impacts\u003c/em\u003e (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7225794/v1\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eGreen schoolyard investments and urban equity: A systematic review of economic and social impacts using spatial-statistical methods\u003c/em\u003e [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7213563/v1\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eGreen schoolyard investments influence local-level economic and equity outcomes through spatial-statistical modeling and geospatial analysis in urban contexts\u003c/em\u003e. arXiv. https://doi.org/10.48550/arXiv.2507.14232\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eQuantifying gentrification: A critical review of definitions, methods, and measurement in urban studies\u003c/em\u003e. Preprints. https://doi.org/10.20944/preprints202508.0150.v1\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eSchoolyard greening, child health, and neighborhood change: A comparative study of urban U.S. cities\u003c/em\u003e (arXiv:2507.08899) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.08899\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025, July 11). \u003cem\u003eThe impact of greening schoolyards on residential property values\u003c/em\u003e [Working paper]. SSRN. https://doi.org/10.2139/ssrn.5348810\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025). \u003cem\u003eThe impact of greening schoolyards on surrounding residential property values: A systematic review\u003c/em\u003e (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7235811/v1\u003c/li\u003e\n\u003cli\u003eGorjian, M. (2025, July 29). \u003cem\u003eUrban schoolyard greening: A systematic review of child health and neighborhood change\u003c/em\u003e[Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7232642/v1\u003c/li\u003e\n\u003cli\u003eGorjian, M., \u0026amp; Quek, F. (2024). \u003cem\u003eEnhancing consistency in sensible mixed reality systems: A calibration approach integrating haptic and tracking systems\u003c/em\u003e [Preprint]. EasyChair. https://easychair.org/publications/preprint/KVSZ\u003c/li\u003e\n\u003cli\u003eGorjian, M., Caffey, S. M., \u0026amp; Luhan, G. A. (2024). \u003cem\u003eExploring architectural design 3D reconstruction approaches through deep learning methods: A comprehensive survey\u003c/em\u003e. \u003cem\u003eAthens Journal of Sciences\u003c/em\u003e, 11(2), 1\u0026ndash;29. https://www.athensjournals.gr/sciences/2024-6026-AJS-Gorjian-02.pdf\u003c/li\u003e\n\u003cli\u003eGorjian, M., Caffey, S. M., \u0026amp; Luhan, G. A. (2025). \u003cem\u003eAnalysis of design algorithms and fabrication of a graph-based double-curvature structure with planar hexagonal panels\u003c/em\u003e. arXiv. https://doi.org/10.48550/arXiv.2507.16171\u003c/li\u003e\n\u003cli\u003eGorjian, M., Caffey, S. M., \u0026amp; Luhan, G. A. (2025). \u003cem\u003eExploring architectural design 3D reconstruction approaches through deep learning methods: A comprehensive survey\u003c/em\u003e. \u003cem\u003eAthens Journal of Sciences\u003c/em\u003e, 12, 1\u0026ndash;29. https://doi.org/10.30958/ajs.X-Y-Z\u003c/li\u003e\n\u003cli\u003eGorjian, M., Luhan, G. A., \u0026amp; Caffey, S. M. (2025). \u003cem\u003eAnalysis of design algorithms and fabrication of a graph-based double-curvature structure with planar hexagonal panels\u003c/em\u003e. \u003cem\u003earXiv preprint\u003c/em\u003e arXiv:2507.16171. https://doi.org/10.48550/arXiv.2507.16171\u003c/li\u003e\n\u003cli\u003eRaina, A. S., Mone, V., Gorjian, M., Quek, F., Sueda, S., \u0026amp; Krishnamurthy, V. R. (2024). \u003cem\u003eBlended physical-digital kinesthetic feedback for mixed reality-based conceptual design-in-context\u003c/em\u003e. In \u003cem\u003eProceedings of the 50th Graphics Interface Conference\u003c/em\u003e (Article 6, pp. 1\u0026ndash;16). ACM. https://doi.org/10.1145/3670947.3670967\u003c/li\u003e\n\u003cli\u003eHailemariam, S. (2021). \u003cem\u003eSuitable site selection for urban green space development using geographic information system and remote sensing based on multi criterion analysis\u003c/em\u003e. \u003cstrong\u003eEnvironmental Monitoring and Assessment\u003c/strong\u003e, Springer.\u003c/li\u003e\n\u003cli\u003eHoeben, R., \u0026amp; Posch, A. (2021). \u003cem\u003eGreen roof ecosystem services in various urban development types: A case study in Graz, Austria\u003c/em\u003e. \u003cstrong\u003eForests\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eHuang, Y., Yu, S., et al. (2023). \u003cem\u003eAnalysis and optimized location selection of comprehensive green space supply in the central urban area of Hefei based on GIS\u003c/em\u003e. \u003cstrong\u003eSustainable Futures\u003c/strong\u003e, Elsevier.\u003c/li\u003e\n\u003cli\u003eKim, J., Oh, S., et al. (2021). \u003cem\u003eEstablishment of a geographic information system-based algorithm to analyze suitable locations for green roofs and roadside trees\u003c/em\u003e. \u003cstrong\u003eApplied Sciences\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eMironova, O. (2021). \u003cem\u003eGIS modeling of green infrastructure of Mediterranean cities for the management of urbanized ecosystems\u003c/em\u003e. \u003cstrong\u003eArid Ecosystems\u003c/strong\u003e, Pleiades Publishing Inc.\u003c/li\u003e\n\u003cli\u003eMobarak, M., Shrahily, R., et al. (2022). \u003cem\u003eAssessing green infrastructures using GIS and the multi-criteria decision-making method: the case of the Al Baha region (Saudi Arabia)\u003c/em\u003e. \u003cstrong\u003eEkol\u0026oacute;gia (Bratislava)\u003c/strong\u003e, Sciendo.\u003c/li\u003e\n\u003cli\u003eMohammed, R., \u0026amp; Hammo, M. (2023). \u003cem\u003eEvaluate of green space (parks) in Duhok city by use image satellite, Google Earth, GIS, NDVI, and field survey techniques\u003c/em\u003e. \u003cstrong\u003eKufa Journal for Agricultural Sciences\u003c/strong\u003e, University of Kufa.\u003c/li\u003e\n\u003cli\u003ePage, M. J., et al. (2021). \u003cem\u003eThe PRISMA 2020 statement: An updated guideline for reporting systematic reviews\u003c/em\u003e. \u003cstrong\u003eBMJ\u003c/strong\u003e, 372:n71.\u003c/li\u003e\n\u003cli\u003ePouya, S., \u0026amp; Aghlmand, S. (2022). \u003cem\u003eEvaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic\u003c/em\u003e. \u003cstrong\u003eLand\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eRachid, M., Elmostafa, M., et al. (2024). \u003cem\u003eAssessing carbon storage and sequestration benefits of urban greening in Nador City, Morocco, utilizing GIS and the InVEST model\u003c/em\u003e. \u003cstrong\u003eEcological Indicators\u003c/strong\u003e, Elsevier.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Espinosa, V., Aguilera-Benavente, F., et al. (2020). \u003cem\u003eGreen infrastructure design using GIS and spatial analysis: A proposal for the Henares Corridor (Madrid\u0026ndash;Guadalajara, Spain)\u003c/em\u003e. \u003cstrong\u003eLandscape Research\u003c/strong\u003e, Taylor \u0026amp; Francis.\u003c/li\u003e\n\u003cli\u003eV\u0026icirc;lcea, C., \u0026amp; Șoșea, E. (2020). \u003cem\u003eA GIS-based analysis of the urban green space accessibility in Craiova city, Romania\u003c/em\u003e. \u003cstrong\u003eGeografisk Tidsskrift-Danish Journal of Geography\u003c/strong\u003e, Taylor \u0026amp; Francis.\u003c/li\u003e\n\u003cli\u003eWaheeb, M., Zerouali, A., et al. (2023). \u003cem\u003eEnhancing sustainable urban planning through GIS and multiple-criteria decision analysis: A case study of green space infrastructure in Taif Province, Saudi Arabia\u003c/em\u003e. \u003cstrong\u003eSustainability\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eWanghe, K., Guo, H., et al. (2020). \u003cem\u003eGravity model toolbox: An automated and open-source ArcGIS tool to build and prioritize ecological corridors in urban landscapes\u003c/em\u003e. \u003cstrong\u003eRemote Sensing\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eWu, X., Liu, J., \u0026amp; Hou, Y. (2025). \u003cem\u003eData and methods for assessing urban green infrastructure using GIS: A systematic review\u003c/em\u003e. \u003cstrong\u003ePLOS ONE\u003c/strong\u003e, 20(6): e0324906. https://doi.org/10.1371/journal.pone.0324906\u003c/li\u003e\n\u003cli\u003eXu, Z., Luo, J., et al. (2020). \u003cem\u003eAccurate suitability evaluation of large-scale roof greening based on RS and GIS methods\u003c/em\u003e. \u003cstrong\u003eWater\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003cli\u003eYang, F. (2024). \u003cem\u003eResearch on optimization strategies for urban park green space planning in Nanjing based on GIS from the perspectives of network analysis and Thiessen polygon theory\u003c/em\u003e. \u003cstrong\u003eJournal of Asian Architecture and Building Engineering\u003c/strong\u003e, Taylor \u0026amp; Francis.\u003c/li\u003e\n\u003cli\u003eZhang, X., Yan, Y., et al. (2024). \u003cem\u003eVisual assessment of historic landmarks based on GIS and survey: A study of view and viewing of Tiger Hill in Suzhou, China\u003c/em\u003e. \u003cstrong\u003eSustainability\u003c/strong\u003e, MDPI.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Colorado Denver","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geographic Information Systems, Urban Green Infrastructure, systematic review, spatial analysis, ecosystem services, PRISMA, multi-criteria decision analysis","lastPublishedDoi":"10.21203/rs.3.rs-7349702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7349702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeographic Information System (GIS) assessments that are comprehensive provide factual foundations for the design, construction, and enhancement of Urban Green Infrastructure (UGI) to foster sustainable development. Research methodology, application sectors, trends, and issues in GIS-based urban green infrastructure assessments are integrated in this study. The literature from the Scopus and Web of Science databases was carefully reviewed from January 1, 2020, to June 30, 2024. The initial dataset includes 640 items from Web of Science and 952 items from Scopus. The inclusion criteria were satisfied by 20 articles after 1,572 duplicates and irrelevant studies were eliminated. Accessibility, ecological services, resilience, environmental justice, social benefits, and aesthetics comprised the scope. Artificial intelligence improved data analysis, while the integration of GIS with multiple datasets and indicators increased comprehensiveness. However, it was important to establish connections with communities in the field to facilitate inclusion. The investigation recognized a growing interdisciplinarity that covered aesthetics, sociology, urban planning, and ecology. These methods demonstrate the necessity of sustainable urban development that is integrated with ecological, social, economic, and cultural factors.\u003c/p\u003e","manuscriptTitle":"Advances in GIS-Based Assessment of Urban Green Infrastructure: A Systematic Review (2020–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 04:26:52","doi":"10.21203/rs.3.rs-7349702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53ca9c69-6493-4787-b674-ddacc20da6fb","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53003725,"name":"Urban Studies"}],"tags":[],"updatedAt":"2025-08-13T04:26:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 04:26:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7349702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7349702","identity":"rs-7349702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}