Search Results (2,921)

Urban parks are vital for enhancing residents’ quality of life. With ongoing urban expansion, park visitation and quality expectations have risen, yet research on ecosystem disservices—i.e., the negative impacts of parks—remains limited compared with studies on ecosystem services. This study addresses this gap by investigating why residents may avoid parks, focusing on how negative perceptions vary across different park types. Through questionnaire surveys conducted in 68 parks across five categories in central Beijing, residents’ concerns were analyzed. Findings show significant differences among park type: users of small neighborhood parks emphasized internal noise, infrastructure, and cleanliness; while users of medium and large regional park users prioritized safety risks, followed by landscape and infrastructure issues. Users of historically significant parks raised concerns related to overcrowding and plant emissions, while those of special theme parks highlighted issues related to the cultural or natural atmosphere. Accordingly, park renovation should adopt type-specific strategies rather than uniform approaches. Drawing on successful Beijing cases, targeted improvements and a sustainable business operation model are proposed to address funding limitations. These results align with China’s park classification framework and offer insights for international urban park management, ultimately contributing to improved resident well-being and reduced urban inequality. Full article

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Rapid urbanization in the Global South has intensified the formation of mega-urban regions, where conventional urban–rural classifications often fail to capture the complexity of peri-urban systems. In the Jakarta–Bandung Mega-Urban Region (JBMUR), rapid land-use change and socio-economic transformation have produced hybrid landscapes that challenge binary zoning approaches. This study aims to delineate urban, peri-urban, and rural spatial structures using a spatially constrained clustering framework and to evaluate the performance of the Rustiadi Quantitative Zoning Method-2 (RQZM-2) compared with conventional non-spatial clustering (Non-RQZM). Built-environment, accessibility, environmental, and socio-economic indicators derived from remote sensing and spatially disaggregated statistical data were analyzed using grid-based K-Means clustering. Comparative validation using internal metrics, stability analysis, spatial coherence diagnostics, and statistical differentiation tests indicates that RQZM-2 produces more stable, spatially coherent, and interpretable clusters than conventional clustering. The validated four-cluster solution identifies compact urban cores, extensive peri-urban transition belts, and two distinct rural sub-types, revealing a functionally differentiated regional structure across the JBMUR. These findings demonstrate that incorporating spatial contextualization into clustering improves the empirical representation of peri-urban spatial continuity and provides a robust analytical basis for spatial zoning and regional planning in rapidly urbanizing mega-urban regions. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

Accurate building roof classification from airborne LiDAR point clouds is fundamental to reliable three-dimensional (3D) urban reconstruction. While supervoxel-based methods offer efficiency and resilience to uneven point density, their performance is critically undermined by cross-boundary segmentation errors—a direct consequence of random seed initialization that merges geometrically similar yet semantically distinct objects. To address this root cause, this study proposes Interior-Anchored Mean-Shift (IAMS), a novel supervoxel segmentation framework that rethinks seed placement as a geometry-aware interior localization problem. By integrating local geometric consistency point density, and spatial correlation into a unified kernel density estimator, supplemented by density-adaptive voxel weighting and a semi-variogram-driven bandwidth, IAMS reliably anchors seeds within object interiors, yielding highly homogeneous supervoxels without post-processing. Extensive experiments on three diverse airborne LiDAR datasets demonstrated that IAMS consistently outperformed state-of-the-art baselines. On the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark, our approach improved roof classification completeness, correctness, and quality by up to 7.1% (per-object) over the conventional Voxel Cloud Connectivity Segmentation (VCCS) algorithm while being significantly faster than recent boundary-preserving alternatives. Critically, IAMS maintains robust performance under challenging conditions, including sparse sampling and dense vegetation occlusion, making it a practical solution for real-world urban remote sensing. Full article

The dynamics of the processes shaping the quality of rainwater discharged by sewer systems is very complex. The use of hydrodynamic models to simulate surface runoff and the dynamics of changes in pollutants, including heavy metal (HM) concentrations, requires the collection of a lot of data that is difficult to obtain, and model calibration is complex and time-consuming. This paper presents a machine learning model and investigates the possibility of applying data mining methods to simulate changes in the concentrations of selected heavy metals (Ni, Cu, Cr, Zn and Pb) based on rainwater quality studies conducted in three urban catchments located in Kielce, southern Poland, with the aim of developing a model with broader applicability. Simulations of HM content in rainwater were performed using regression and classification trees (RF), neural networks (MLP) and support vector machines (SVMs). The MLP (MAPE ≤ 21.6) and SVM (MAPE ≤ 23.5) methods were shown to have the highest accuracy in simulating HM content. These models produced satisfactory simulation results based on rainfall amount and meteorological conditions, and they had relatively simple model structures and short simulation time. The study demonstrated that the proposed approach provides a transferable tool for estimating HM content in rainwater based on air quality, expressed in terms of visibility, and the type of catchment development. Full article

Social media platforms have been established as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on semi-synthetic data streams constructed from historical geolocated seeds for controlled validation, utilizing real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil. The proposed framework demonstrated robust quantitative performance, achieving a prequential accuracy of up to 86.4% and a weighted F1-score of 0.864 in the Panama scenario, maintaining high stability against semantic noise. The results suggest that this hybrid architecture is a highly viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals. Full article

This research is interdisciplinary in nature and supports the process of selecting individual plants to achieve sustainable visual and ecological effects in the urban landscape. The importance of this study is further emphasised by climate change, which necessitates modifications to the existing selection of ornamental plants. These individuals must be capable of adapting to urban ecosystems in order to mitigate the impacts of climate change on humans and other organisms and to maintain a high level of biodiversity. Accordingly, this paper highlights, at the individual level, the significance of Japanese quince (Chaenomeles japonica (Thunb.) Lindl. ex Spach) as an element of urban green infrastructure in the Balkan Peninsula. Based on a real case study conducted over the period 2007–2025 and through an integrative approach involving 3841 phenological observations and climate parameters over 19 consecutive years, local phenological flowering patterns were identified, upon which the species’ functional potential depends. The key patterns and abundance of flowering are the result of interactions with daily maximum and minimum air temperatures and precipitation levels, as confirmed by correlations with percentile-based classifications of climatic variables for the study years. The statistical non-significance of the trends points to the influence of extreme climatic events but also to the adaptability of the selected genotype compared with other Japanese quince genotypes in the vicinity. Regression analysis determined the optimal daily air temperatures for continuous flowering during 2024 and 2025. The results confirm that the selected individual is sustainable, and it is, therefore, proposed for inclusion in the assortment of ornamental plants important for preserving ecosystem services in urban landscape design, particularly in view of its demonstrated utilitarian benefits. Full article

Accurate urban expansion mapping in dryland environments is essential for sustainable planning, infrastructure management, and heritage-sensitive development, yet it remains methodologically challenging because built-up surfaces often exhibit strong spectral similarity to bright bare soils. This study comparatively evaluates three widely used urban mapping approaches in Diriyah, Saudi Arabia, a rapidly transforming heritage district of high relevance to Saudi Vision 2030: the Global Human Settlement Layer (GHSL), the Normalized Difference Built-up Index (NDBI), and unsupervised k-means classification. Built-up extent was mapped for 2015, 2020, and 2025, and method performance was assessed using 150 stratified reference points interpreted from high-resolution imagery. The results reveal substantial quantitative differences among methods. GHSL produced the most conservative estimates of urban extent (2.80, 4.94, and 5.31 km²), while NDBI and unsupervised classification generated much larger and less realistic built-up areas due to spectral confusion with bright bare soil. Accuracy assessment confirmed the superiority of GHSL, which achieved the highest overall accuracy (0.88) and Kappa coefficient (0.83), compared with NDBI (0.53; 0.41) and unsupervised classification (0.61; 0.50). To support integrative interpretation, the study also developed a Hybrid Built-up Detection Model (HBDM), which combines the three outputs into a continuous urban intensity layer that helps distinguish persistent urban cores from uncertain transition zones. The findings demonstrate that conservative global built-up products provide a more reliable baseline than index-based or unsupervised methods in bright-soil dryland settings. More broadly, the study offers practical methodological guidance for urban monitoring and sustainable land management in desert cities undergoing rapid transformation under large-scale development agendas such as Saudi Vision 2030. Full article

In response to land-use and land-cover (LULC) changes in South Africa, which have varied effects on biodiversity, several studies have characterized LULC changes using remote sensing data due to its cost-effectiveness, repetitiveness, spatial coverage and flexibility. However, the geotemporal and methodological characteristics of these studies remain relatively unknown. In this regard, we review remote sensing-based studies conducted in South Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 343 articles retrieved from Web of Science, Google Scholar, and Scopus databases, 103 studies were eligible for analysis. The analysis showed that (a) various remote sensing datasets were increasingly and effectively used to characterize LULC in South Africa over the period 2001–2024, primarily Landsat data with integration of various advanced classification algorithms; (b) most studies were conducted in the eastern seaboard, particularly in the Maputaland–Pondoland–Albany hotspot and highveld to the north, and (c) much research dealt with issues pertaining to “pristine class” conversion to urban area and other human-induced activities, mainly in biodiversity-rich landscapes. Overall, LULC studies achieved consistently reliable accuracies, largely using publicly available geospatial datasets, thereby creating an accessible foundation for all researchers. LULC research is expected to increase as conservation efforts strengthen amid ongoing developments in South Africa. Full article

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

China’s rapid urbanization has accelerated the transition of residential development toward high-density models. As a critical interface between architecture and the urban environment, residential facades reflect evolving design strategies, living demands, and technological conditions. However, due to the complexity and diversity of facade components, the underlying influencing factors of facade evolution remain insufficiently explored. This study focuses on Shenzhen, a typical high-density city in southern China, and quantitatively analyzes 225 residential facades from 1980 to 2024 using HCA (Hierarchical Cluster Analysis). The results show that the development of residential facades in Shenzhen presents continuous and staged evolutionary characteristics, with a transition from simplified, function-oriented configurations to diversified and technology-integrated forms. Six clusters of facade types are identified, and the analysis reveals that this evolution is driven by the combined effects of policies and design standards (external factors), resident demand (internal factors), and technological development (technical support), rather than merely stylistic changes. This study establishes a quantitative classification framework to identify the evolutionary patterns and influencing factors of residential facades, enriches the research system of high-density residential facades, provides methodological support for facade analysis, and offers both theoretical and practical guidance for facade design in subtropical high-density cities. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article