Search Results (369)

This review seeks to understand what urban planning and management can do to reduce disaster risk and help cities adapt to the impacts of climate change. To achieve this, it examines various streams of the literature, as the topic sits at the intersection of several distinct but relevant disciplinary fields. These include urban planning in hazardous areas, recovery planning, disaster risk reduction (an umbrella term encompassing disciplines from engineering to geography and sociology), and, more recently, climate change adaptation. To navigate this vast body of knowledge, a conceptual framework is proposed to guide the selection of the relevant literature, and the strategy for this selection is detailed in the methodological section. This review adopts elements of both critical and theoretical approaches: it does not aim to be comprehensive or to systematically search each disciplinary domain addressed. While acknowledging the limitations and potential biases in the selection of articles and books, the review reflects an evolution in the discourse on urban planning for resilience. The discussion explores how the concept of resilience has emerged as a valuable bridge between disaster risk reduction, sustainability, and climate change adaptation—especially as cities face increasing exposure and vulnerability to stresses that are now more frequently compounded, multi-hazard, and cascading. The conclusion outlines the gaps and challenges that researchers, practitioners, and policy makers need to address moving forward. Full article

Understanding how atmospheric pollutants interact with soil pollution is essential for assessing long-term environmental and human health risks. This study compares concentrations of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) in PM₁₀ and surface soil near air quality monitoring stations in Zagreb, Croatia. While previous work identified primary emission sources affecting PM₁₀ composition in the area, this study extends the analysis to investigate potential pollutant transfer and accumulation in soils. Multivariate statistical tools, including correlation analysis and principal component analysis (PCA), were employed to gain a deeper understanding of the sources and behavior of pollutants. Results reveal significant correlations between air and soil concentrations for several PTEs and PAHs, particularly when air pollutant data are averaged over extended periods (up to 6 months), indicating cumulative deposition effects. Σ11PAH concentrations in soils ranged from 1.2 to 524 µg/g, while mean BaP in PM₁₀ was 2.2 ng/m³ at traffic-affected stations. Strong positive air–soil correlations were found for Pb and Cu, whereas PAH associations strengthened at longer averaging windows (3–6 months), especially at 10 cm depth. Seasonal variations were observed, with stronger associations in autumn, reflecting intensified emissions and atmospheric conditions that facilitate pollutant transfer. PCA identified similar pollutant groupings in both air and soil matrices, suggesting familiar sources such as traffic emissions, industrial activities, and residential heating. The integrated PCA approach, which jointly analyzed air and soil pollutants, showed coherent behaviour for heavier PAHs and several PTEs (e.g., Pb, Cu), as well as divergence in more volatile or mobile species (e.g., Flu, Zn). Spatial differences among monitoring sites show localized influences on pollutant accumulation. Furthermore, this work demonstrates the value of coordinated air–soil monitoring in urban environments and provides an understanding of pollutant distributions across different components of the environment. Full article

Co-design plays a pivotal role in architectural design and urban planning for the green transition, facilitating collaboration among designers and stakeholders to create contextually appropriate solutions. This study examines the balance between stakeholder input and designer agency within co-design practices aimed at addressing the complex challenges posed by the green transition. Looking at how designers’ mindsets and methods are influenced by co-design, this study is carried out by analysing two contrasting case studies from the Future Island-Island project: Field Operations, an immersive residential on Rathlin Island, and DesignLink, a structured design sprint with organisational partners. Employing the terminologies of autogenic (designer-led) and allogenic design (stakeholder-led), the research critically explores how these modalities influence design outcomes and designers themselves. Field Operations exemplifies a more allogenic approach characterised by collaborative brief development through local immersion, while DesignLink primarily illustrates an autogenic process where predefined objectives guided creative synthesis. The study reveals that effective co-design requires oscillation between these approaches, underscoring the necessity for designers to harness both community insights while ensuring their own creative agency. The findings in this study advocate for a refined co-design framework that optimally integrates stakeholder contributions without compromising the integrity and coherence of the design process, emphasising the importance of contextual sensitivity, innovation, and timely decision-making in addressing complex societal challenges such as the green transition. Full article

Spatial density, as a key indicator of the quality of the urban residential environment, comprises both physical and perceived dimensions. Physical density refers to objective spatial characteristics (e.g., building density and population density), whereas perceived density denotes residents’ perceptual evaluations (e.g., perceived crowding, visual openness, and overall environmental quality). Clarifying the relationship between physical and perceived density is therefore critical for advancing livability-oriented urban planning and design. This study examines the relationship through an empirical analysis of 50 representative high-density communities in Guangzhou. Using morphological classification, descriptive statistics, and multiple linear regression, the analysis compares objective density indicators with residents’ perceptual evaluations and identifies key environmental factors that shape perceived density. Findings indicate that physical and perceived density are not fully aligned: compact but coherent spatial forms can enhance residents’ perceptual evaluations, whereas overcrowded and deteriorating environments intensify negative perceptions. The identified community typologies—for example, urban villages, traditional walk-up estates, and modern high-rise complexes—exhibit distinct perceptual patterns and influencing factors. These results highlight the need for density regulation to move beyond conventional physical indicators and to incorporate perceptual dimensions into planning frameworks. Overall, the study provides theoretical insights and practical guidance for tailored strategies in the renewal and management of high-density communities. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

Historic and cultural towns in China are crucial carriers of vernacular heritage, yet many unlisted historic buildings remain highly vulnerable to urbanization and fragmented governance. This study takes Xiangzhu Town in Zhejiang Province as a case study and develops a multidimensional evaluation framework—integrating value, morphology, and risk—to identify conservation priorities and guide adaptive reuse. The results highlight three key findings: (1) a spatial pattern of “core preservation and peripheral renewal,” with historical and artistic values concentrated in the core, scientific value declining outward, and functional diversity emerging at the periphery; (2) a morphological structure characterized by “macro-coherence and micro-diversity,” as revealed by balanced global connectivity and localized hotspots in space syntax analysis; and (3) differentiated building risks, where most assets are low to medium risk, but some high-value ancestral halls show accelerated deterioration requiring urgent action. Based on these insights, a collaborative framework of “graded management–classified guidance–zoned response” is proposed to align systematic restoration with community-driven revitalization. This study demonstrates the effectiveness of the value–morphology–risk approach for small historic towns, offering a replicable tool for differentiated heritage conservation and sustainable urban–rural transition. Full article

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence. Full article

Accurate and reliable vehicle localization is essential for autonomous driving in complex outdoor environments. Traditional feature-based visual–inertial odometry (VIO) suffers from sparse features and sensitivity to illumination, limiting robustness in outdoor scenes. Deep learning-based optical flow offers dense and illumination-robust motion cues. However, existing methods rely on simple bidirectional consistency checks that yield unreliable flow in low-texture or ambiguous regions. Global navigation satellite system (GNSS) measurements can complement VIO, but often degrade in urban areas due to multipath interference. This paper proposes a multi-sensor fusion system that integrates monocular VIO with GNSS measurements to achieve robust and drift-free localization. The proposed approach employs a hybrid VIO framework that utilizes a deep learning-based optical flow network, with an enhanced consistency constraint that incorporates local structure and motion coherence to extract robust flow measurements. The extracted optical flow serves as visual measurements, which are then fused with inertial measurements to improve localization accuracy. GNSS updates further enhance global localization stability by mitigating long-term drift. The proposed method is evaluated on the publicly available KITTI dataset. Extensive experiments demonstrate its superior localization performance compared to previous similar methods. The results show that the filter-based multi-sensor fusion framework with optical flow refined by the enhanced consistency constraint ensures accurate and reliable localization in large-scale outdoor environments. Full article

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature ($T_{H_{2}O}^{°}$), air temperature ($T_{air}^{°}$), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ($T_{H_{2}O}^{°}$), and ($T_{air}^{°}$) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

Remote sensing visual question answering (RSVQA) involves interpreting complex geospatial information captured by satellite imagery to answer natural language questions, making it a vital tool for observing and analyzing Earth’s surface without direct contact. Although numerous studies have addressed RSVQA, most have focused primarily on answer accuracy, often overlooking the underlying reasoning capabilities required to interpret spatial and contextual cues in satellite imagery. To address this gap, this study presents a comprehensive evaluation of four large multimodal models (LMMs) as follows: GPT-4o, Grok 3, Gemini 2.5 Pro, and Claude 3.7 Sonnet. We used a curated subset of the EarthVQA dataset consisting of 100 rural images with 29 question–answer pairs each and 100 urban images with 42 pairs each. We developed the following three task-specific frameworks: (1) Zero-GeoVision, which employs zero-shot prompting with problem-specific prompts that elicit direct answers from the pretrained knowledge base without fine-tuning; (2) CoT-GeoReason, which enhances the knowledge base with chain-of-thought prompting, guiding it through explicit steps of feature detection, spatial analysis, and answer synthesis; and (3) Self-GeoSense, which extends this approach by stochastically decoding five independent reasoning chains for each remote sensing question. Rather than merging these chains, it counts the final answers, selects the majority choice, and returns a single complete reasoning chain whose conclusion aligns with that majority. Additionally, we designed the Geo-Judge framework to employ a two-stage evaluation process. In Stage 1, a GPT-4o-mini-based LMM judge assesses reasoning coherence and answer correctness using the input image, task type, reasoning steps, generated model answer, and ground truth. In Stage 2, blinded human experts independently review the LMM’s reasoning and answer, providing unbiased validation through careful reassessment. Focusing on Self-GeoSense with Grok 3, this framework achieves superior performance with 94.69% accuracy in Basic Judging, 93.18% in Basic Counting, 89.42% in Reasoning-Based Judging, 83.29% in Reasoning-Based Counting, 77.64% in Object Situation Analysis, and 65.29% in Comprehensive Analysis, alongside RMSE values of 0.9102 in Basic Counting and 1.0551 in Reasoning-Based Counting. Full article

In response to escalating climate change and ecological degradation, nature-based solutions (NBSs) have emerged as a critical paradigm for sustainable environmental governance. Simultaneously, artificial intelligence (AI) offers powerful capabilities for addressing the complexity and uncertainty inherent in natural systems. This study investigates the integration of AI within NBS through a hybrid bibliometric and semantic-enhancement framework. Drawing on 535 peer-reviewed articles from the Web of Science Core Collection (2011–2024), we employ keyword co-occurrence analysis via CiteSpace and semantic refinement using ChatGPT-4.0 to identify 15 key thematic clusters. Results reveal that AI is widely applied in ecological monitoring, carbon emission reduction, urban climate adaptation, and green infrastructure optimization—substantially improving the responsiveness, precision, and scalability of NBS interventions. The proposed methodology enhances both structural insight and semantic coherence in bibliometric review, offering a robust foundation for future interdisciplinary research. This study contributes to the theoretical development and practical implementation of AI-enhanced NBS, supporting data-driven, adaptive strategies for climate resilience and sustainable development. Full article

Urban areas increasingly face heat-related climate risks, necessitating targeted, nature-based interventions such as tree planting to improve resilience, livability, and public health. This study presents a data-driven workflow to identify urban tree planting potential (TPP) in the city of Dortmund, Germany. The approach integrates high-resolution spatial datasets capturing land cover, shading, thermal comfort, population density, and critical infrastructure. All variables were harmonized within a 50 m hexagonal grid, normalized, and combined into a composite TPP score using weighting schemes informed by expert judgment and sensitivity testing. Spatial and non-spatial clustering were applied to group urban areas by shared characteristics, and a connectivity analysis evaluated the spatial coherence of high-potential cells and their relationship to existing green infrastructure. The findings demonstrate the potential to strengthen urban green infrastructure and guide coordinated planting strategies while addressing both ecological and social priorities. The presented workflow offers a flexible, transferable tool to support municipalities in prioritizing effective greening interventions and integrating climate adaptation objectives into urban development planning. Full article

Autonomous vehicles (AVs) are rapidly becoming a reality, with a series of cities in the world currently testing applications. Despite these developments, the existing analyses in the literature concerning the impacts of such developments on urban transportation systems have yielded a body of evidence marked by significant divergence and contradictory conclusions. Such conflicting findings critically hamper the synthesis of a coherent understanding and the formulation of evidence-based strategies, a challenge exacerbated by the potentially multifaceted nature of these impacts. The potential disruptive technology and the game-changing force of automated vehicles make this lack of congruence in analytical outcomes severely complicate efforts to derive clear insights or actionable conclusions. The purpose of the paper is to explore and define the optimal strategies for implementing autonomous vehicle technologies, to predict their effects on anomalies, in the Kuhnian sense, of urban transportation, and to propose a desirable urban vision and a paradigm shift consisting of a decline of car ownership dependence and the rise of shared AVs. This study is undertaken to address the escalating crisis in urban transportation globally. Cities are facing unprecedented strain due to rapid urbanization, leading to severe traffic congestion, pervasive air and noise pollution, significant safety risks, and persistent accessibility gaps, all of which profoundly diminish urban quality of life and impede economic vitality. The new vision has been assessed based on a literature selection, some qualitative and quantitative analyses, and applications and projects currently in testing. The results are largely positive and promise to change urban transportation radically, as well as to resolve the mismatches between the vision, what the paradigm predicts, and what is revealed in the implementation. The success of the vision ultimately depends on policy and regulation to manage the way in which AVs are implemented in urban areas, if they are not to lead to a worsening of the urban environment, accessibility, and health. This thoughtful implementation should address all potential challenges through integrated planning of transportation, land use, and digital systems. Full article

Historic districts face persistent challenges balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study develops a multi-source data-driven evaluation system coupling spatial quality and urban vitality, focusing on China’s Republican-era historic districts with Nanjing’s Yihe Road as a case study. Integrating field surveys and big data (street view imagery, POI data, heatmaps), we quantitatively assess environmental quality and vitality. Key findings reveal a distinct spatial pattern: “high-quality concentration internally” and “high-vitality concentration externally,” where core areas exhibit functional homogenization and low vitality, while peripheries show high pedestrian activity but lack spatial coherence. Clustering analysis categorizes streets into four types based on quality and vitality levels, highlighting contradictions between static conservation and adaptive reuse. The study deepens understanding of spatial differentiation mechanisms and reveals universal patterns for sustainable development strategies. A multi-tiered governance strategy is proposed: urban-level flexible governance harmonizes cross-departmental policies via adaptive planning, district-level differentiated governance activates spatial value through functional reorganization, and street-level fine-grained management prioritizes incremental micro-renewal. The research underscores the critical need to balance heritage preservation with contemporary functional demands during urban renewal, offering a practical framework to resolve spatial conflicts and reconcile conservation with regeneration. Full article