Search Results (48)

The construction of post-industrial landscapes is increasingly regarded as an important pathway for promoting urban sustainability. However, limited attention has been given to the interconnections between post-industrial landscapes and local villagers in rural contexts. From the perspective of environmental justice, the ecological and cultural-tourism goals of post-industrial landscapes may be mismatched with villagers’ place-based needs. This study examines a typical rural post-industrial waterfront area in China to analyze villagers’ environmental justice. Representative project photographs were collected, and villagers’ perceptions were obtained through questionnaires and semi-structured interviews, yielding 98 valid responses (95% response rate). Quantitative measurements of landscape characteristics were combined with pairwise preference evaluations, and the analysis applied the framework of recognition, participatory, and distributive justice. A discrete choice model (DCM) and spatial analysis were then employed to explore the relationships. Quantitative analysis showed that natural vegetation, plazas, industrial heritage, and pedestrian paths had negative effects on villagers’ recognition (β = −0.36 to −0.18), whereas hardscape had a strong positive effect (β = 0.94). Moreover, spatial analysis indicated localized patterns of environmental injustice, highlighting uneven distribution of landscape benefits across the site. Semi-structured interviews revealed villagers’ priorities across landscape design, amenities, local livelihoods, and project implementation, highlighting the importance of safer, more functional, and well-managed spaces. Collectively, these findings underscore the importance of inclusive planning and design strategies that integrate ecological, cultural, and recreational considerations, thereby supporting the sustainable renewal of rural post-industrial waterfronts. Full article

In the second half of the twentieth century, the concept of indeterminacy in architecture emerged to address the realities of chance and change, with the megastructure representing a critical point of this ambition. As the aims of indeterminate architectural approaches align with current sustainable development goals, this study hypothesises this design concept as the basis for the sustainability of structures built within its framework. Through a case study of Spens, a socialist megastructure in Novi Sad, Serbia, the paper explores the potentials of megastructures in relation to requirements for more sustainable cities. Firstly, it evaluates Spens’ current social sustainability through focus group discussions analysing sense of community, place, and wellbeing. Findings demonstrate a clear recognition of Spens’ spatial qualities among users. Secondly, the paper examines the future environmental sustainability of Spens, focusing on strategies that enhance user wellbeing and urban life as a point of overlap between social and environmental sustainability, using the Green Space Factor (GSF) and Urban Greening Factor (UFG). Results reveal the significant yet underutilised potential for greening. The paper highlights the long-term societal value of such structures and strategies for leveraging their concepts for their sustainable adaptation rather than replacement amid ongoing retrofit or replacement debates. Full article

Point cloud segmentation underpins various applications in geospatial analysis, such as autonomous navigation, urban planning, and management. Kernel Point Convolution (KPConv) has become a de facto standard for such tasks, yet its fixed geometric kernel limits the modeling of fine-grained contextual relationships—particularly in heterogeneous, real-world point cloud data. In this paper, we introduce the adaptation of a Local Contextual Attention (LCA) mechanism for the KPConv network, with reweighting kernel coefficients based on local feature similarity in the spatial proximity domain. Crucially, our lightweight design preserves KPConv’s distance-based weighting while embedding adaptive context aggregation, improving boundary delineation and small-object recognition without incurring significant computational or memory overhead. Our comprehensive experiments validate the efficacy of the proposed LCA block across multiple challenging benchmarks. Specifically, our method significantly improves segmentation performance by achieving a 20% increase in mean Intersection over Union (mIoU) on the STPLS3D dataset. Furthermore, we observe a 16% enhancement in mean F1 score (mF1) on the Hessigheim3D benchmark and a notable 15% improvement in mIoU on the Toronto3D dataset. These performance gains place LCA-KPConv among the top-performing methods reported in these benchmark evaluations. Trained models, prediction results, and the code of LCA are available in a GitHub opensource repository. Full article

The increasing popularity of long-distance running in urban areas reflects a convergence of personal health goals and sustainable urban living practices. However, understanding the psychological drivers behind such behaviors remains essential for designing effective health promotion strategies. This study investigated the motivations of 155 participants of the Poznan Marathon and Half Marathon using the validated Polish version of the Motivations of Marathoners Scale (MOMS). Data were collected via an online survey and analyzed using descriptive statistics, t-tests, ANOVA, and MANOVA to assess differences across gender, education, place of residence, and BMI. The highest-rated motivations were personal goal achievement and health orientation, aligning with the values of sustainable urban living. The least important were recognition and affiliation. Women reported significantly higher motivations related to health and weight control, while men showed a greater tendency toward competition. Education level and place of residence did not significantly affect motivational profiles. BMI was positively correlated only with weight-related motives. The findings highlight the importance of tailoring physical activity promotion to demographic differences, particularly gender and BMI. Supporting long-distance running through inclusive, personalized strategies may enhance its role in fostering healthier and more sustainable urban communities. Full article

Urban scene understanding for inclusive smart cities remains challenged by the scarcity of training data capturing people with mobility impairments. We propose SYNTHUA-DT, a novel methodological framework that integrates unmanned aerial vehicle (UAV) photogrammetry, 3D digital twin modeling, and high-fidelity simulation in Unreal Engine to generate annotated synthetic datasets for urban accessibility applications. This framework produces photo-realistic images with automatic pixel-perfect segmentation labels, dramatically reducing the need for manual annotation. Focusing on the detection of individuals using mobility aids (e.g., wheelchairs) in complex urban environments, SYNTHUA-DT is designed as a generalized, replicable pipeline adaptable to different cities and scenarios. The novelty lies in combining real-city digital twins with procedurally placed virtual agents, enabling diverse viewpoints and scenarios that are impractical to capture in real life. The computational efficiency and scale of this synthetic data generation offer significant advantages over conventional datasets (such as Cityscapes or KITTI), which are limited in accessibility-related content and costly to annotate. A case study using a digital twin of Curitiba, Brazil, validates the framework’s real-world applicability: 22,412 labeled images were synthesized to train and evaluate vision models for mobility aids user detection. The results demonstrate improved recognition performance and robustness, highlighting SYNTHUA-DT’s potential to advance urban accessibility by providing abundant, bias-mitigating training data. This work paves the way for inclusive computer vision systems in smart cities through a rigorously engineered synthetic data pipeline. Full article

The rapid urbanization and technological advancements in the United Arab Emirates (UAE) have placed its modern architectural heritage from the 1970s and 1980s at increasing risk of being unrecognized and lost, particularly in Dubai following the discovery of oil. This research addresses the critical need for the documentation and heritage representation of Dubai’s modern heritage, a city undergoing rapid transformation within a globalized urban landscape. Focusing on the Nasser Rashid Lootah Building (Toyota Building), an iconic early 1970s residential high-rise representing the modern architecture of Dubai and a significant milestone in its architectural history, this study explores a replicable and cost-effective approach to digitally document and conserve urban heritage under threat. The existing building was meticulously documented and analyzed to highlight its enduring value within the fast-changing urban fabric. Through the innovative combination of drone photography, ground-based photography, and HBIM, a high-resolution 3D model and a semantically organized HBIM prototype were generated. This research demonstrates a replicable measure for identifying architectural values, understanding modernist design typologies, and raising local community awareness about Dubai’s modern heritage. Ultimately, this study contributes toward developing recognition criteria and guiding efforts in documenting modern high-rise buildings as vital heritage worthy of recognition, documentation, and future conservation in the UAE. Full article

Urban space is the social field in which religious diversity in contemporary Italy becomes most evident and where religious groups compete for visibility, recognition, and places of worship. The sites of so-called minorities can be observed as indicators of a plural religious geography. Peaceful and conflictual dynamics are both expressed precisely through external recognition, which may be horizontal—religious and social—when between peers or vertical therefore juridical. This study presents the findings of research conducted in the city of Turin, an emblematic case within the Italian religious landscape for the management of religious diversity and interreligious dialogue initiatives. The analysis focuses on the Romanian Orthodox Church located in the historic center, which we interpret as a shared religious place. This case shows how a spatial and material perspective can offer an innovative approach to the field of interreligious dialogue. Places of worship are crucial spaces for interreligious dialogue: they serve as laboratories of local peace-building and experiments in coexistence, mutual respect, encounter, and conflict mediation. However, in a frame of multiple secularities, the ambiguity of both the national and regional legal systems contribute to marginality of the religious dimension in the city’s urban planning policies, ignoring the important role these places play as spaces of cohesion, identity, inclusion. Full article

Cyclical analyses of dynamic changes in urban areas are critical and necessary for policymakers and societies. Remote sensing data processing methods are currently in place to determine the distribution of built-up and sealed areas on global and continental scales. However, there is a lack of research on distinguishing among urban classes at a larger scale for a city or its district. SAR sensors register features of urban areas that, when further processed, such as textures, can help in automatic recognition. We present a novel dataset for urban classification focusing on density analysis. Machine learning methods, including a selection of artificial neural networks and other classifiers, have been used to distinguish among different classes of built-up areas, as defined according to the Urban Atlas database. This dataset was used to establish benchmarks for classification, conduct verification tests, and evaluate accuracy. The C-band of Sentinel-1 images, for the same study areas as ICEYE X-band images and their texture derivatives, were used to classify variants. Better results were obtained using the CNN-based Unet model. The best overall accuracy was 79% for the X-band and 73% for the C-band datasets. The results indicate that the single-polarization X-band is more suitable for this classification despite the presence of more SAR features in the C-band with dual polarization. Full article

Being both a driver and a manifestation of the current ecological, climate, and social crises, urban sustainability has become a major contemporary issue. Rather than framing the challenges that populations are confronted to as external factors, especially in deprived and segregated neighborhoods, we collected narratives about their experience of their living environments. Our work assumed an innovative interdisciplinary perspective in response to the complex interconnexions of the issues at stake. We aimed to highlight the significance of a situated perspective and an experience-based approach to fully embrace the idea of a research engaged with and for the communities, especially those suffering from marginalization and social deprivation. Our empirical results, rooted in expressions of place attachment (or not), in four disadvantaged neighborhoods in France, are presented in the form of a heuristic device, a non-normative framework that iteratively produced a representation with six dimensions that we called feelings. Together, they can be used to explore the manifestations of well-being, through place attachment related to one’s living environment, in a relational and open way, as people make sense of their place and possibly engage in its defense. We suggest further attention should be directed to concepts such as agency, freedom, and social recognition, as major conditions of the possibility of well-being or leading a good life. These dimensions could be major targets for policies trying to respond to the current sustainability challenges, such as social and environmental justice in the face of an unequal and changing world. Full article

Robust autonomous navigation systems rely on mapping, locomotion, path planning, and localization factors. Localization, one of the most essential factors of navigation, is a crucial requirement for a mobile robot because it needs the capability to localize itself in the environment. Global Positioning Systems (GPSs) are commonly used for outdoor mobile robot localization tasks. However, various environmental circumstances, such as high-rise buildings and trees, affect GPS signal quality, which leads to reduced precision or complete signal blockage. This study proposes a visual-based localization system for outdoor mobile robots in crowded urban environments. The proposed system comprises three steps. The first step is to detect the text in urban areas using the “Efficient and Accurate Scene Text Detector (EAST)” algorithm. Then, EasyOCR was applied to the detected text for the recognition phase to extract text from images that were obtained from EAST. The results from text detection and recognition algorithms were enhanced by applying post-processing and word similarity algorithms. In the second step, once the text detection and recognition process is completed, the recognized word (label/tag) is sent to the Places API in order to return the recognized word’s coordinates that are passed within the specified radius. Parallely, points of interest (POI) data are collected for a defined area by a certain radius while the robot has an accurate internet connection. The proposed system was tested in three distinct urban areas by creating five scenarios under different lighting conditions, such as morning and evening, using the outdoor delivery robot utilized in this study. In the case studies, it has been shown that the proposed system provides a low error of around 4 m for localization tasks. Compared to existing works, the proposed system consistently outperforms all other approaches using just one sensor. The results indicate the efficacy of the proposed system for localization tasks in environments where GPS signals are limited or completely blocked. Full article

The incorporation of environmental variables into policies, programs, plans and projects has been achieved through the use of an Environmental Impact Assessment (EIA). However, the recognition by scholars of several limitations of the EIA has prompted the consideration of Strategic Environmental Assessment (SEA) as the appropriate instrument for achieving this objective. Studies on SEA have concentrated in phases prior to the decision-making, despite the fact that, after the strategic decision has been made, it is also necessary to follow up on the environmental impacts or effects produced by the plan, as well as the possibility of adopting measures to correct them when they cause adverse or unforeseen effects. The way in which this following-up takes place will vary from country to country, based on the respective legal system. Therefore, this study aims to understand these forms of follow-up in urban land planning instruments, at the local level which are legally binding, comparing regulations in France, Portugal and Chile, through three research questions focused on determining whether this phase exists, whether it is possible to modify the local planning instrument in the event of adverse effects and whether there are offset measures for those effects. This study employs a mixed methodology based on the law and content analysis, enabling the identification of pertinent aspects for investigation, the compilation of material for this study, and the answering of research questions through the comparative analysis of the laws of the selected countries. Results show differences and similarities between the regulations of the countries analyzed, regarding the ability to reverse undesired, negative or different effects from those originally considered in urban plans. It will shed light on the possibility for other countries to take follow-up action in the face of undesirable scenarios in the application of planning instruments. The gaps found in our research may also exist in the legislation of other countries. Full article

The present study reflects on spontaneous nature’s agency to reclaim abandoned urban areas in Italian urban brownfields, providing a focused analysis of the Metropolitan Area of Milan. These spaces are the products of phenomena, such as deindustrialization, demilitarization, and uncontrolled urban expansion, which have produced a compromised heritage and challenges to regeneration. Such abandonment sometimes produces new forms of urban nature, which suggests a possible path for ecological regeneration and coexistence, as affirmed by the multidisciplinary literature. The related informal urban biodiversity grows regardless of future planning provisions, triggering unexpected transformations of the urban environment and producing socio-ecological value, as demonstrated by citizens’ recognition of these places. The present study maps informal urban biodiversity in the Milan territory, identifying the presence of large contaminated sites, relevant urban voids, vacant lots, and former agricultural spaces. This study also reflects on possible paths for urban planning and policies to integrate informal urban biodiversity within the urban ecological structure by analyzing the main features and challenges of the corresponding regeneration processes. Full article

“Rejoice, Texandria, for Oda!” Thus begins the series of chants and readings commemorating the virgin St Oda, patron of the village that took her name—Sint-Oedenrode—in the late medieval liturgy of the town of ’s-Hertogenbosch. Overt praise for the surrounding region, Texandria, extending across the northern limits of the duchy of Brabant and diocese of Liège, is a recurring theme in the liturgy inspired by the saint’s legend. Yet how did Oda, of Scottish origin, become so closely associated with this remote region? And what was the significance of her liturgical veneration in ’s-Hertogenbosch, to which Sint-Oedenrode was enfranchised? Exemplifying interactions between central and secondary places within a specific region, this study argues for the relevance of the historical approach to urban–rural dynamics in medieval hagiography and its related liturgy. Recognition that smaller towns and villages played important roles in regional networks prompts more focused attention to regional priorities in the legends and liturgies of local saints. That Oda’s cult is attested by a diversity of extant documentary evidence—historical, hagiographic, and liturgical, including newly discovered liturgical readings—facilitates interpretation of her veneration in ’s-Hertogenbosch and of the intertextual connections between her legend and those of other saints, notably Lambert, associated with the duchy and diocese. As suggested by this example, regionalism merits greater scrutiny as an integral component of civic religion. Full article

In recent years, semantic segmentation has made significant progress in visual place recognition (VPR) by using semantic information that is relatively invariant to appearance and viewpoint, demonstrating great potential. However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. Therefore, this paper proposes a novel VPR framework that employs a coarse-to-fine image matching strategy, combining semantic and appearance information to improve algorithm performance. First, we construct SemLook global descriptors using semantic contours, which can preliminarily screen images to enhance the accuracy and real-time performance of the algorithm. Based on this, we introduce SemLook local descriptors for fine screening, combining robust appearance information extracted by deep learning with semantic information. These local descriptors can address issues such as semantic overlap and sparsity in urban environments, further improving the accuracy of the algorithm. Through this refined screening process, we can effectively handle the challenges of complex image matching in urban environments and obtain more accurate results. The performance of SemLook descriptors is evaluated on three public datasets (Extended-CMU Season, Robot-Car Seasons v2, and SYNTHIA) and compared with six state-of-the-art VPR algorithms (HOG, CoHOG, AlexNet_VPR, Region VLAD, Patch-NetVLAD, Forest). In the experimental comparison, considering both real-time performance and evaluation metrics, the SemLook descriptors are found to outperform the other six algorithms. Evaluation metrics include the area under the curve (AUC) based on the precision–recall curve, Recall@100%Precision, and Precision@100%Recall. On the Extended-CMU Season dataset, SemLook descriptors achieve a 100% AUC value, and on the SYNTHIA dataset, they achieve a 99% AUC value, demonstrating outstanding performance. The experimental results indicate that introducing global descriptors for initial screening and utilizing local descriptors combining both semantic and appearance information for precise matching can effectively address the issue of location recognition in scenarios with semantic ambiguity or sparsity. This algorithm enhances descriptor performance, making it more accurate and robust in scenes with variations in appearance and viewpoint. Full article

Remote sensing (RS) data can be attained from different sources, such as drones, satellites, aerial platforms, or street-level cameras. Each source has its own characteristics, including the spectral bands, spatial resolution, and temporal coverage, which may affect the performance of the vehicle detection algorithm. Vehicle detection for urban applications using remote sensing imagery (RSI) is a difficult but significant task with many real-time applications. Due to its potential in different sectors, including traffic management, urban planning, environmental monitoring, and defense, the detection of vehicles from RS data, such as aerial or satellite imagery, has received greater emphasis. Machine learning (ML), especially deep learning (DL), has proven to be effective in vehicle detection tasks. A convolutional neural network (CNN) is widely utilized to detect vehicles and automatically learn features from the input images. This study develops the Improved Deep Learning-Based Vehicle Detection for Urban Applications using Remote Sensing Imagery (IDLVD-UARSI) technique. The major aim of the IDLVD-UARSI method emphasizes the recognition and classification of vehicle targets on RSI using a hyperparameter-tuned DL model. To achieve this, the IDLVD-UARSI algorithm utilizes an improved RefineDet model for the vehicle detection and classification process. Once the vehicles are detected, the classification process takes place using the convolutional autoencoder (CAE) model. Finally, a Quantum-Based Dwarf Mongoose Optimization (QDMO) algorithm is applied to ensure an optimal hyperparameter tuning process, demonstrating the novelty of the work. The simulation results of the IDLVD-UARSI technique are obtained on a benchmark vehicle database. The simulation values indicate that the IDLVD-UARSI technique outperforms the other recent DL models, with maximum accuracy of 97.89% and 98.69% on the VEDAI and ISPRS Potsdam databases, respectively. Full article