Rapid urban expansion in developing countries has intensified pressure on peri-urban wetlands, leading to significant ecological transformation. The East Kolkata Wetlands (EKW), a designated Ramsar site on the eastern fringe of Kolkata, serves as a globally recognised model of wastewater-fed aquaculture and resource recovery. However, accelerating urbanisation and infrastructure growth have increasingly altered its landscape structure and ecological integrity. This study investigates the spatio-temporal dynamics of land-use and land-cover (LULC) change in the East Kolkata Wetlands over the past three decades using multi-temporal satellite imagery and geospatial techniques. Supervised classification methods were applied to map major land categories, including wetlands, water bodies, agricultural land, vegetation, and built-up areas. Landscape metrics, including patch density, fragmentation index, and connectivity measures, were computed to assess structural transformations and ecological stability. The results indicate a substantial increase in built-up areas, accompanied by declines in wetland extent and agricultural land. Water bodies exhibit increasing fragmentation and reduced spatial connectivity, reflecting the conversion of ecologically functional zones into urban land uses. These transformations compromise key ecosystem services, including wastewater treatment, flood regulation, biodiversity conservation, and livelihood support for local communities dependent on wetland-based activities. The study highlights the urgent need for integrated urban planning, stricter enforcement of wetland protection policies, and continuous spatial monitoring to ensure sustainable coexistence between urban growth and wetland conservation in rapidly expanding metropolitan regions.

Introduction: SDG 11 mandates inclusive, safe, resilient, and sustainable cities, yet realisation requires distinguishing equality, uniform resource distribution from equity, calibrating support to differentiated needs. Kitwe’s urban fabric includes twenty-four formal and nineteen informal settlements that coexist within one municipal boundary, yet residents experience divergent access to healthcare, education, water, and social protection. With 66% of the population under 25, Copperbelt poverty at 35.9%, and the informal sector absorbing 80–90% of employment, the city constitutes a case for equity-oriented social development in Sub-Saharan Africa.
Methods: A stratified cross-sectional survey across six selected settlements (three formal, three informal) measures healthcare accessibility using a two-step floating catchment area method, education enrolment and completion rates, water and sanitation service levels, and social protection coverage, including the Social Cash Transfer Programme and NHIMA health insurance. A Spatial Equity Index weights indicators by vulnerability prevalence across the formal–informal continuum. Focus group discussions with women, youth, and persons with disabilities capture qualitative exclusion dimensions.
Results: Informal settlement residents travel 2.5–4 times further to access primary healthcare than their formal settlement counterparts, with the national 1:12,000 doctor-patient ratio masking far worse localised ratios in peri-urban zones. Cash transfer coverage, expanded to 880,000 households nationally, shows significant enrolment gaps among mobile urban populations who are ineligible under community-targeting criteria designed for rural contexts. Child stunting in low-income settlements reaches 48% versus 25% in planned areas. The Spatial Equity Index reveals consistent disadvantage gradients correlating with distance from the formal core and proximity to mine waste sites.
Conclusions: Equity-oriented urban social development in Kitwe necessitates spatially targeted, vulnerability-weighted resource allocation that transcends uniform municipal service delivery models. Priority interventions include extending NHIMA coverage to informal populations, establishing satellite health facilities within compound areas, and reformulating cash transfer targeting criteria for mobile urban households to close the persistent equity gap.

This study contributes to the interdisciplinary debate on the local implementation of the Sustainable Development Goals (SDGs) by analysing public policies in the municipality of São Cristóvão, Sergipe, between 2019 and 2025. The selected period corresponds to the formal incorporation of the 2030 Agenda into local planning instruments, including the revision of the Master Plan and its integration into multiannual plans and annual budgetary legislation. Although the municipality has gained recognition for sustainability-oriented initiatives and has been referred to as a “Sustainable City,” a systematic assessment of policy coherence and social development impacts remains limited. The research aims to: (i) map municipal programs and actions across the social, environmental, and economic dimensions of sustainable development; (ii) classify these initiatives according to their alignment with the 17 SDGs, with particular emphasis on SDG 11 (Sustainable Cities and Communities); and (iii) evaluate the coherence between implemented policies and municipal sustainability and social development indicators. Special attention is given to how local governance addresses inclusion, equitable access to urban services, and the protection of vulnerable populations. Methodologically, the study adopts a qualitative documentary approach, analyzing official municipal documents produced between 2019 and 2025, including planning instruments, fiscal legislation, and management reports, complemented by a structured literature review on sustainable urban governance. The findings contribute to the debate on the effectiveness of municipal public policies in advancing inclusive, resilient, and sustainable urban development, highlighting both institutional advances and structural constraints in implementing the 2030 Agenda at the local level.

Rapid and unplanned infrastructure development has tremendous impacts on land use and land cover dynamics; in particular, large-scale infrastructure projects reshape the peri-urban and rural landscapes in rapidly urbanizing regions of Bangladesh. The construction of the Karnafuli Tunnel bisected Bairag, a peri-urban union of Bangladesh, into two segments, potentially triggering a drastic impact on the LULC modifications and economic shifts. This study intends to investigate the nexus between LULC change, land price dynamics, and urban densification in Bairag over time, showcasing the impact of the crucial changes. At two-year intervals, Landsat 7 SR (2008–2012), Landsat 8 (2014), and Sentinel-2 (2016–2024) data were extracted to prepare supervised LULC maps of Bairag, which were classified into seven categories over 200 training signatures per class using Google Earth Engine, and a Kappa value >0.85 was achieved, denoting strong classification accuracy. The finding reveals an increase of more than three times in built-up areas, while the vegetation and agricultural lands decreased by 50% and 18%, respectively, in 2024 compared to 2008. Moreover, change detection maps were generated through pixel-by-pixel comparison of consecutive LULC maps, which eventually produced transition matrices to quantify land cover conversions, indicating stable pre-tunnel land dynamics and accelerated urban expansion and abrupt changes in the later years. Later, built-up zones extracted from supervised LULC maps were investigated through a sector-based buffer ring method, showing 500 m concentric rings and directional sectors from a predefined center to determine urban expansion in Bairag over the years. The urban densification toward the east and southeast directions was predominant, depicting the increase in areas by 123 and 111 times, respectively, indicating radial urban growth. Even the land price dynamics and built-up expansion indicated significant positive correlations. This study will contribute to sustainable land use planning, infrastructure governance, and equitable development strategies in rapidly transforming cities like Chattogram.

The pedestrian streets of the Historic Centre of Mexico City are spaces that promote alternative modes of transportation and offer a different way of experiencing the city. It is in these spaces that local and tourist pedestrians interact with the history of the city, perceiving, experiencing, and, in some cases, reinterpreting the landscape. This reinterpretation could be associated with the loss of identity of the local inhabitants due to mass tourism. Our research examines the semiotic landscape of five pedestrianized streets in the Historic Centre of Mexico City, aiming to identify how the landscape has been reinterpreted through language. During 2022, a total of 470 surveys were conducted among residents and pedestrians, revealing a duality between the historical symbols observed and the current predominance of a commercial context. Our findings offer an overview of how symbols and written language on buildings and businesses reflect the impact of mass tourism on historic areas and the powerful way in which it affects residents by modifying their surroundings and interfering with their daily lives, forcing them to reinterpret their landscape. We argue that although tourism is an important part of the economy, it can be managed in a way that respects the identity of inhabitants and historic places.

Urban Heat Islands (UHIs) intensify thermal stress in rapidly expanding campuses across hot desert regions, yet empirical micro-scale data from Saudi university environments remain limited. This study investigates the cooling performance of green spaces and surface treatments at Najran University, located in the arid southern region of Saudi Arabia. To enhance the reliability of satellite-derived land surface temperature analysis and support microclimate simulation inputs, field measurements were undertaken under existing campus conditions. Environmental sensors and handheld instruments were deployed at selected sites representing diverse surface types, including conventional paved areas, reflective-coated pavements, cemented zones, and shaded vegetated areas. Temperature and humidity sensors were elevated to ensure unobstructed ambient data capture, while infrared thermometers recorded surface temperatures through targeted
spot readings. Data loggers continuously collected ambient temperature and relative humidity during representative daytime periods. The results demonstrate clear thermal gradients across the assessed surface categories. Vegetated zones exhibited the lowest temperatures, ranging from 6.5 °C in the morning to 29.1 °C at noon, attributable to shading and evapotranspiration. Unpainted pavements showed the highest heat retention, with temperatures between 9.5 °C and 42.7 °C, whereas reflective-painted pavements consistently reduced surface temperatures relative to untreated surfaces.
Spatial analysis further confirmed an inverse relationship between vegetation density and
surface temperature distribution across the campus. Overall, the findings highlight that the strategic expansion of green infrastructure, combined with the targeted application of reflective materials, can significantly mitigate localized UHI intensity in arid institutional settings. Such measures support climate-sensitive campus design and advance broader sustainability objectives.

Citizen-generated spatial data offer new opportunities to understand how residents perceive the energy and socioeconomic conditions of their living environments. However, translating subjective perceptions into interpretable urban patterns remains methodologically challenging. This study explores how bivariate spatial mapping can be used to reveal alignments and mismatches between perceived conditions and measurable characteristics of the built and social environment.

The analysis is based on citizen-generated geodata collected through a neighborhood crowd-mapping platform developed within the oPEN Lab project, applied to the Rochapea district in Pamplona, Spain. The study area comprises 14 census sections, which led to a combined cartographic and descriptive analytical approach. Two independent bivariate maps were constructed: (1) perceived thermal comfort × energy consumption and (2) housing tenure × income level. Each map followed a 3×3 classification matrix, producing nine perception classes arranged along a theoretical gradient. For each class, mean values of available demographic, socioeconomic, and residential indicators were aggregated from cadastral and official statistical sources. The analysis focused on internal comparisons between classes to detect gradients, discontinuities, and inconsistencies between perception-based categories and contextual indicators.

Energy perception patterns showed a clear association with post-regulatory housing stock and the absence of interior dwellings, suggesting a link between construction standards and perceived comfort. Conversely, socioeconomic perception displayed counterintuitive spatial configurations, where favorable perceived classes did not consistently align with higher official income or newer housing. These discontinuities highlight complex socio-spatial dynamics beyond conventional indicators.

Despite a reduced spatial sample and the participatory nature of the data, including a participation bias (59% women and 53% minors due to school-based engagement activities), bivariate spatial mapping of citizen perceptions provides an exploratory framework to identify concordances and tensions between lived experience and urban structure. The approach supports evidence-informed neighborhood diagnostics and demonstrates the analytical value of participatory geodata for urban energy planning.

The Historic Urban Landscape (HUL) approach promoted by UNESCO calls for integrating natural and cultural heritage, yet its empirical application in urban planning remains limited, especially in Latin American cities. This study addresses that gap by proposing a multidimensional mathematical model to quantify environmental sustainability in the Ingachaca Urban Action Unit in Cuenca, Ecuador. The area is a transitional landscape combining vernacular architecture, the Tomebamba River riparian corridor, and the Pumapungo archaeological complex.

A Composite Environmental Sustainability Index was developed using the Analytic Hierarchy Process (AHP) based on expert consensus. Seven indicators were standardized on a 0–1 scale using satellite imagery and in situ sensors: soil permeability, green area per capita, vegetation index (NDVI), biological diversity (Shannon Index), water quality (WQI), air quality (settleable dust and combustion gases), and acoustic pollution.

The resulting global index reached 0.593, indicating moderate environmental sustainability while revealing strong ecological polarization. On the positive side, the landscape structure shows high biophysical resilience, supported by 78.15 m² of green space per capita, 71.3% permeable soil, robust vegetation coverage, and acceptable river water quality (WQI = 67.55). These conditions provide a solid ecological base consistent with HUL principles.

However, this potential is significantly undermined by intense external urban pressures. The most critical impacts are extreme acoustic pollution, with average daily noise levels of 118 dB and peaks above 130 dB, and sustained atmospheric degradation driven by heavy vehicular traffic.

The proposed AHP-based model supports strategic planning by integrating Nature-based Solutions (NbS) with heritage conservation. It quantitatively demonstrates how blue–green infrastructure can mitigate environmental stressors while protecting vernacular and archaeological values. Replicable across historic urban centers, the methodology reframes heritage landscapes as resilient socio-ecological systems where ecological restoration and cultural preservation jointly enable sustainable, healthy urban living.

This study applies Public Participatory GIS (PPGIS) to explore students’ spatial experiences and perceptions of sustainability on the campus of the University of Pannonia (Veszprém, Hungary), ranked by the UI GreenMetric World University Ranking system. Using the free Map-Me platform, an online participatory mapping survey was conducted in November 2025 among 235 students of the Faculty of Business and Economics. Due to the nature of the free platform, the number of responses to certain questions varied. Respondents identified locations associated with positive experiences and places where sustainability principles are perceived on campus. Spatial data were processed and analysed in QGIS following the exploration–explanation–modelling framework of Fagerholm et al. (2021). Methods included point clustering, heatmap visualisation, buffer and proximity analyses, overlay techniques, and logistic regression modelling to examine the factors influencing positive experiences. Results reveal spatial concentrations of positive experiences primarily near green and communal spaces, while perceived sustainability hotspots only partially overlap with these areas. Proximity to parks and natural features may increase the probability of positive experiences. The findings highlight the importance of green infrastructure and accessible communal areas in shaping students’ campus experiences. The study demonstrates the applicability of PPGIS in campus-scale sustainability assessment and provides spatially explicit insights to support evidence-based campus planning and green university development.

Accurate prediction of urban wind fields is critical for applications such as Unmanned Aerial System (UAS) trajectory planning, pedestrian comfort analysis, and urban heat island mitigation. However, traditional high-fidelity Computational Fluid Dynamics (CFD) simulations are computationally prohibitive for extensive parametric analyses, while low-fidelity empirical models lack the necessary localized accuracy within complex urban morphologies.

This work presents the development of a Machine Learning Surrogate Model designed to bridge the gap between low-fidelity approximations and high-fidelity simulations. Using generative diffusion architectures, the proposed data-driven surrogate serves as a lightweight, data-fusion-capable tool for rapid wind-flow prediction. The model is trained on a comprehensive dataset generated through parametric simulations of a cityscape, combining high-fidelity solvers with low-fidelity kinematic approximations. The training space systematically covers the range of wind magnitudes and directions derived from annual observed wind roses.

To ensure broad applicability, the surrogate model is designed to generalize across varying building densities, enabling accurate interpolation of flow fields transitioning from dense, high-rise downtown centers to sparse suburban layouts. Furthermore, the model aims to incorporate a robust data-fusion methodology to assimilate sparse, real-world meteorological measurements, dynamically enhancing predictive accuracy.

Ultimately, this generative framework demonstrates substantial downstream utility, offering near-high-fidelity accuracy at a fraction of the computational cost, thereby enabling rapid, reliable environmental modeling for advanced urban infrastructure and autonomous aerial navigation.