Citizen science contributions to sustainable urban transformation, urban sustainability and urban planning: A systematic literature review

Rapid population growth in the town of Mila, in northeastern Algeria, has made urban expansion essential. However, unfavorable soil conditions pose major challenges to urban development. This study aims to characterize subsurface conditions, assess soil mechanical behavior, and establish a geotechnical zoning framework together with an Engineering Ground Model to support safe urban development. An integrated site investigation program was conducted, including 15 Core Drillings, 49 dynamic penetration tests, laboratory analyses, and hydrogeological monitoring. The subsurface stratigraphy consists of clays and marls containing limestone blocks at depths exceeding 20m, together with two slip surfaces identified at depths of - 4m and - 15m. The results of the dynamic penetration tests divide the study area into two zones: one characterized by low peak resistance (Pr = 4.38 MPa) and shallow bedrock (BR = - 1.6m), and the other by moderate conditions (BR = - 6 m and Pr = 18.48MPa). The soils show low chemical aggressiveness (SO42⁻ < 5.5mg/kg), very high clay content (> 75%), high plasticity (21 < Pi < 41.94%), and significant compressibility (Cr = 27.80%, 15.18% < w < 24.54%). X-ray diffraction analysis revealed clay and interstratified minerals dominated by illite (10-30%) and montmorillonite/smectite (≈12.5%). Most of the site is characterized by moderate to low admissible bearing capacity (0.164 < qad(max) < 0.842MPa) for shallow foundations and is susceptible to significant volumetric changes, with settlements exceeding 5cm across large areas. Based on the combined analysis of bearing capacity, settlement potential, groundwater depth, and soil heterogeneity, three geotechnical zones were identified, ranging from highly unfavorable to relatively competent foundation conditions. The spatial distribution of areas with low bearing capacity and high settlement potential closely correlates with the observed patterns of structural damage. The results demonstrate that shallow foundations are largely unsuitable throughout the study area unless ground improvement measures or deep foundation systems are adopted. This study provides a robust Engineering Ground Model and geotechnical zoning framework to guide sustainable urban planning and foundation design in similar clay-dominated, hydro-mechanically sensitive environments.

ABSTRACT Transportation Mode Identification (TMI) based on GPS trajectory data is vital for data-driven urban mobility analysis and sustainable transportation planning. Despite recent advances in deep learning-based spatiotemporal modeling, effectively integrating heterogeneous contextual information into TMI remains a challenge. This paper proposes CALRCN, a Context-aware Attentive Long-term Recurrent Convolutional Network that jointly models spatial, temporal, and contextual characteristics of human mobility. The framework employs a structured input representation that separates GPS-based motion features from GIS-based contextual features. Spatial patterns are extracted using a convolutional module with channel-wise attention, while long-term temporal dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) with additive attention. Weather information is further incorporated to enhance contextual awareness. Experiments on the Geolife dataset demonstrate that CALRCN achieves an accuracy of 95.7%, outperforming existing deep learning baselines. Further evaluation supports the framework’s generalizability, while ablation studies validate the effectiveness of context fusion and attention mechanisms.

ABSTRACT Green spaces offer valuable and essential services to the urban population, ranging from leisure to environmental benefits. The differences in knowledge, attitude, and perception towards urban green spaces vary from one gender to another, and they have the potential to influence the urban environmental discourse. Previous studies and scholarly works have indicated that women tend to be more active and closely associated with urban green spaces and the environment compared to men. This study aims to investigate the disparities in knowledge and attitude towards urban green spaces between men and women in Chennai. This perpetual knowledge gap between these genders could provide ramifications for urban environmental discussions based on gender. This study employs a cross-sectional quantitative methodology, utilizing a structured questionnaire and a convenience sampling technique to collect data from 438 participants from the proximities of urban green spaces in Chennai. Data analysis was conducted through descriptive statistics using SPSS version 21, and a 5-point Likert scale was employed to comprehend their perceptions. Findings revealed that there is no significant difference in the knowledge about urban green spaces between men and women; however, their attitudes towards urban green spaces exhibit variations among them. Despite men’s greater engagement in urban environments, their attitude towards the appropriate usage of this urban environment lags behind that of women. These findings imply the intersection of gender and urban environments, highlighting the gender-based disparities in urban environmental engagement. Furthermore, this study underscores the need for sustainable and gender-inclusive urban planning to foster the urban environment.

ABSTRACT Urban forests are critical nature-based solutions for climate change mitigation, yet accurately quantifying their carbon sequestration potential at fine scales remains a major challenge. In this study, an integrated framework for individual-tree-level species classification and carbon stock estimation in urban forests is proposed, leveraging UAV-based LiDAR and hyperspectral data fusion. To address the challenges of high feature dimensionality and class imbalance, an enhanced feature selection strategy termed adaptive cross-validation with dynamic correlation constraints (ACV-DCC) was introduced. A total of 14,376 individual trees were automatically segmented using a seed-growing algorithm across three types of urban green spaces on the Yuzhong Campus of Lanzhou University. The ACV-DCC method significantly improved classification performance, increasing the accuracy for 18 tree species to 85.67%. Among the tested classifiers, the RF outperformed the SVM and XGBoost algorithms in terms of the accuracy (85%–86%) and robustness. Its nonlinear modeling capacity also enabled accurate prediction of tree structural attributes, supporting a carbon stock estimation of approximately 1.85 × 106 kg on campus. The results demonstrate the scalability and effectiveness of the proposed framework in small- to medium-scale heterogeneous urban green environments. This reproducible and scalable workflow provides a high-precision technical pathway that can be adapted to heterogeneous urban environments worldwide, thereby contributing to global urban carbon monitoring efforts and sustainable ecosystem planning.

Climate change can intensify mosquito-borne disease risks through rising temperatures and more frequent extreme weather events. To mitigate effects of climate change, cities are adopting nature-based solutions, such as urban greening and rainwater management, yet their implications for vector-borne diseases and host community composition remain poorly understood. West Nile virus (WNV), an emerging mosquito-borne human pathogen in Europe, is primarily transmitted between birds and mosquitoes. Using mosquito sampling at five sites within a one-square-kilometre area in Berlin, Germany, we examined how urban land cover, including climate-resilient infrastructure, influences local WNV amplification over two mosquito seasons in 2023 and 2024. We found seasonal WNV infection rates of up to 4.8% in mosquitoes and identified fine-scale heterogeneity in infection risk. Residential areas and cemeteries exhibited the highest minimum infection rates per month (up to 15 and 21, respectively), whereas natural conversation and sponge city sites showed significantly lower rates (up to 4 and 13, respectively). These patterns were not explained by mosquito abundance or species composition but by habitat characteristics and avian host community structure. Our findings reveal that urban land cover shapes WNV infection risk and suggest that incorporating biodiversity restoration into nature-based solutions may serve as strategy for sustainable climate-resilient urban planning.

ABSTRACT Although social sustainability is widely recognised as a vague concept, it has become an important aspect of urban planning. However, there is limited knowledge of how urban planners and designers (practitioners) interpret and operationalise it in practice. This paper explores how the concept of urban social sustainability (USS), formulated in planning policy, is conceptualised and translated into practice through operationalisation. The inherent vagueness of USS can allow misalignments between goals and outcomes, questioning the legitimacy of the planning process. The results of 15 interviews with practitioners from Sweden, the Netherlands and Denmark reveal that strategies for conceptualising and operationalising USS are interdependent and include an intermediate process of re-conceptualisation. For conceptualisation, practitioners use six strategies, and for operationalisation, they rely on tools like municipal policies, public participation and digital resources. The results show that the translation of USS from conceptualisation to operationalisation is shaped by dimensions of time and scale to re-conceptualise USS. By adopting a semiotic perspective of translation, the study highlights the interpretive agency of practitioners in shaping the meaning of USS, contributing to a deeper understanding of how USS is made actionable in practice and offers insights for policy and tool development in socially sustainable urban planning.

Multiday, multimodal, time-dependent origin-destination (TD-OD) flows describe when, where, and how urban travel occurs. However, existing approaches are typically single-mode or rely on dense multimodal observations that are rarely available at scale. We show that multimodal TD-OD flows can be recovered by integrating household travel surveys with smart-card transit data. The proposed framework estimates cross-modal flow ratios from survey data and applies them to time-varying transit flows to recover private-vehicle and walking demand at hourly and day-of-week resolution. Validation against independent datasets in Singapore and Seoul shows strong agreement (common part of commuters > 0.70; R-squared > 0.60). The recovered flows support policy-relevant analyses, showing that transit is most competitive for intermediate distances (11-16 km) and transit-only data can underestimate peak epidemic infections by up to 50%. These findings demonstrate the importance of a scalable data fusion for multimodal mobility analysis in sustainable and resilient urban planning.

Analyzing urban sprawl in response to land use land cover change dynamics in Areka town and surrounding area: Wolaita Zone, Ethiopia

Climate change is causing temperatures to rise across much of the earth, and this warming is amplified in urban areas due to the urban heat island effect. One of the most effective methods of reducing urban heat is expanding the urban forest. However, rising temperatures coupled with increasing drought intensity create challenges for the establishment of newly planted trees in cities. Obstacles to urban tree establishment are exacerbated in legacy cities, which have limited financial resources to plant and manage trees. In summer 2024, we conducted a large-scale field study in Dayton, Ohio, USA to address the challenges of expanding the urban forest in a legacy city. To determine low-cost solutions for urban forest expansion, we supplied 640 newly-planted native saplings with varying levels of irrigation investments across 20 parks in Dayton. We then monitored sapling survival, growth, and health in response to our irrigation investments and surrounding impervious surfaces within a 500-m radius of each planting site as a proxy for heat. We found that the effects of both irrigation treatment and surrounding heat varied among tree species. Overall, approximately 50% of planted saplings survived to the end of the growing season, and many saplings went missing due to anthropogenic disturbance. Therefore, we recommend a tailored approach to urban forest expansion which takes species, resources available for irrigation, and surrounding imperviousness into consideration to inform sustainable urban reforestation plans.

Remote sensing based assessment of urban microclimate extremes for advancing sustainable urban planning

The COVID-19 pandemic provided a unique opportunity to evaluate the impact of reduced anthropogenic activities on air quality. This study assesses variations in atmospheric concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂) across selected regions of South India during three phases: pre-COVID (2019), during lockdown (2020), and post-COVID (2021). Satellite observations from Sentinel-5P and NASA Giovanni datasets were used to analyse spatial and temporal trends. The results indicate a clear reduction in pollutant concentrations during the lockdown period, particularly for NO₂, followed by a noticeable rebound as restrictions were relaxed. These trends highlight the strong influence of transportation, industrial activity, and energy consumption on regional air quality. Although variations exist between datasets due to differences in retrieval approaches and resolution, the overall patterns consistently demonstrate improved air quality during reduced human activity. The findings emphasise the potential for achieving significant air quality improvements through effective emission control strategies. This study provides insights for sustainable urban planning and air pollution management, underscoring the need for long-term policies that balance economic development with environmental protection.

A hybrid MDBN-CA framework for simulating urban growth and optimizing multidimensional zoning in sustainable urban planning

Accurate temperature prediction is essential for climate adaptation, environmental monitoring, and sustainable urban planning. This study evaluates the performance of two machine learning techniques Random Forest (RF) and Gradient Boosting Regression (GBR) for predicting near-surface air temperature in the Greater Accra Region of Ghana. Daily temperature observations obtained from the Ghana Meteorological Agency covering the period 1960–2018 were used for model development. The average daily air temperature was computed from minimum and maximum temperature observations. The predictive performance of the models was compared with a classical statistical time-series model, Autoregressive Integrated Moving Average (ARIMA). Model evaluation was performed using five-fold cross-validation to improve the robustness of the results. Performance metrics included Mean Squared Error (MSE) and the coefficient of determination (R²). The results show that the Random Forest model achieved the highest predictive accuracy with MSE = 0.0010 °C and R² = 0.9996, while the Gradient Boosting Regression model produced MSE = 0.0015 °C and R² = 0.9994. The ARIMA model showed significantly lower performance with MSE ≈ 0.598 °C and R² ≈ 0.30. The high predictive performance of the machine learning models is partly attributed to the deterministic relationship between the input variables and the computed target temperature. The study demonstrates the potential of machine learning approaches for climate-related prediction tasks and provides insights for environmental planning and climate resilience strategies in rapidly urbanizing regions such as Greater Accra.

Abstract. Urbanization and climate change are rapidly transforming metropolitan environments, posing serious challenges to sustainable development, climate resilience, and disaster risk management, Among these challenges, concern, where built up areas experiences higher temperatures than surrounding rural regions due to increased impervious surfaces, vegetation loss and altered urban morphology. As UHI intensifies heatwave impacts and public health risks, advanced tools are required to monitor, simulate and mitigate urban heat dynamics. This Study analyses the spatio-temporal evolution of Land Surface temperature (LST) and UHI intensity in Bengaluru for 2004 to 2024 using multi-temporal Landsat data. Key surface indicators include, NDVI, NDBI, NDWI, albedo ad LULC were derived to assess thermal behaviour. Results indicate rapid built-up expansion and declining vegetation, leading to the regression predicts 1-2 °C increases in LST by 2023 in highly urbanized areas.To support urban climate decision making, a 3D Digital Twin platform was developed using CesiumJS, integrating geospatial analysis, remote sensing, and predictive simulations. The framework demonstrates the potential of Digital Twins as an effective decision-supportive tool for climate-adaptive and sustainable urban planning.

Background: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban service accessibility. Methods: This paper presents, for the first time, a systematic selection of medical aesthetics clinic location as a multi-criteria decision-making (MCDM) problem. The Simple Multi-Attribute Rating Technique (SMART) and Single-Valued Neutrosophic TOPSIS (SVN-TOPSIS) are combined to solve the location selection problem. SMART determines criterion weights, whereas SVN-TOPSIS evaluates alternatives using linguistic terms understandable to non-technical decision makers. Results: The proposed SMART SVN-TOPSIS is applied to a real investment problem in which two investors select the best clinic location from five alternatives with nine criteria. Siam Square—the heart of shopping, fashion, and youth culture in Bangkok—is recommended as the top location. Conclusions: The results indicate that the proposed method is capable of generating a consistent ranking of alternatives and differentiating between locations that exhibit similar evaluation characteristics. The findings may also support sustainable urban service planning and healthcare-related facility location decisions.

Abstract Rapid urbanization has resulted in eco-environmental degradation, leading to the global implementation of green infrastructure strategies, such as the Green Heart, to balance urban development with nature conservation. The Changsha-Zhuzhou-Xiangtan Green Heart in China represents an important example of addressing eco-environmental issues within urban agglomerations. However, current research lacks empirical assessments of its multifunctional effectiveness. This research develops a multidimensional framework that integrates 14 eco-environmental and socio-economic indicators spanning the years 2014 to 2022 to address this gap. Employing the CRITIC-entropy weighting and coupling-coordination degree model, the findings indicate that, despite a substantial increase in socio-economic effectiveness driven by growth in tourism-related infrastructure and economic output, eco-environmental effectiveness remained limited, as evidenced by a marked decline in water conservation volume and ecological corridor connectivity. The coupling-coordination degree improved from slightly uncoordinated to slightly coordinated, showing gradual but incomplete harmony in socio-economic development and eco-environmental conservation. The Changsha-Zhuzhou-Xiangtan Green Heart planning presents potential for advancing a green development model; however, its eco-environmental challenges impede the realization of key eco-environmental objectives, thereby questioning its viability as a sustainability model and similar risks for other projects in rapidly urbanizing regions. This study offers a replicable framework for evaluating Green Heart planning and governance, delivering practical insights for sustainable urban planning in China and contributing to global discourse on green infrastructure in rapidly urbanizing areas.

• Resilience of urban vitality during the COVID-19 pandemic was measured in Seoul. • Three resilience dimensions were resistance, recovery capacity, and adaptability. • Neighborhood characteristics shaped resilience differently across dimensions. • Context-specific effects revealed heterogeneous and dynamic resilience patterns. • Findings provide insights for sustainable urban planning and tailored strategies. Although urban vitality and resilience are often examined as distinct phenomena, little is known about how vitality evolves across different phases of resilience under external shocks. This study investigates the resilience of urban vitality in Seoul during the COVID-19 pandemic by distinguishing three dimensions: resistance, recovery capacity, and adaptability. Using de facto population data (2018–2024), we trace resilience trajectories with a dynamic measurement framework that operationalizes these dimensions. Structural equation modeling is then applied to identify how neighborhood-level indicators for 424 administrative units shaped these three dimensions. The results show that resilience varied across neighborhoods: resistance declined in office-dominated areas but was partially supported by lodging and school facilities, recovery capacity was constrained by industrial land use but strengthened by favorable demographic composition and green space, and adaptability was reinforced by high levels of public transport accessibility. By contrast, several land-use and facility variables demonstrated inconsistent or marginal effects, indicating that resilience was shaped more by social composition, spatial openness, and connectivity than by static density measures. These findings highlight the heterogeneous and dynamic nature of resilience and underscore the need for differentiated strategies in sustainable urban planning.

Pattern recognition is critical for obtaining useful informationfromremotesensingdataandimprovingland-use and landcover (LULC) classification. With the increased availability of multispectral, hyperspectral, LiDAR, and high-resolution data, sophisticated algorithms are needed to address issues such spectral similarity, geographical heterogeneity, at-mospheric distortions, and mixed pixels. Recent research has shown that machine-learning models such as SVM, Random Forest, Ensemble Learning, and ELM, as well as deep-learning architectures such as CNNs, U-Net, and hybrid spectral-spatial networks, improve classification accuracy and environmental monitoring. Semantic alignment, open-vocabulary mapping, spatial point pattern analysis, and novel-class discovery are all emerging technologies that promote adaptability in dynamic contexts. The use of GIS, Monte Carlo simulations, and mul-timodal data fusion improves the modelling of environmental processesandlong-termchanges.Overall,thereviewedresearch demonstrate that sophisticated pattern-recognition approaches offerdependable,scalable,anddata-drivensolutionsforremote-sensing applications, thereby promoting sustainable resource management, urban planning, ecological conservation, and climate-resilient development.

Increased urbanization and climate change have led to environmental problems, including the rising temperature, frequent flooding, and plastic wastes in large quantities in the urban areas. These problems need to be resolved with the help of data-driven tools in order to analyze the environmental trends and facilitate sustainable urban planning. The current paper will introduce EcoVision, which is an artificial intelligence-based environmental risk forecasting and sustainability dashboard that uses urban data to detect areas at risk regarding Urban Heat Island (UHI) intensity, flood threat, and plastic waste hotspots. The system employs machine learning models to act on the environmental data and urban data such as temperature, rainfall, population density, and land cover data, as well as air quality indicators. In order to enhance transparency and interpretability, Explainable Artificial Intelligence models like SHAP are incorporated to point out the major determinants of model predictions. The results forecasted are presented in the form of an interactive dashboard that gives maps, graphs, and analytical insights to facilitate an understanding of the environmental risks. EcoVision will help planners, environmental agencies and policymakers in the better understanding of the city development with sustainable development process through the combination of predictive analytics, explainable AI, and visual decision support. The proposed system is a contribution to the implementation of Sustainable Development Goals of climate action, sustainable cities, and careful management of resources