Search Results (126)

Sóller, a highly touristic town in Mallorca, has been affected by gentrification problems related to the tourism industry. Recently, another gentrification process has appeared, affecting the retail fabric and leading to the disappearance of traditional locally owned shops and their substitution with tourist-focused stores. Using data from different sources, such as the City Hall documentary data, the Commerce Association archives and Google Street View images, this research highlights the gentrification process affecting two of the main commercial areas of the town. The results confirm that a commercial gentrification process, already identified in large cities such as Barcelona or Venice, can also affect medium-sized towns, creating a retail mutation that impacts local residents and their shopping capabilities. Full article

Mapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning. Full article

The 2023 Kahramanmaraş earthquakes caused unprecedented structural damage across South-Eastern Türkiye, highlighting the critical need for rapid post-disaster assessment and understanding the root causes of failure in reinforced concrete (RC) structures. This study provides a comprehensive comparative analysis of 207 RC buildings located in Adıyaman, Hatay, and Kahramanmaraş. A novel methodological approach was employed by integrating post-earthquake field observations with pre-earthquake digital data obtained via Google Street View to identify structural irregularities and damage patterns. The investigated buildings were classified based on their damage levels, with 11.1% categorized as heavily damaged, 34.3% as to-be-demolished, and 54.6% as collapsed. Significant structural irregularities, including soft stories (ranging from 64.9% to 82.7%), heavy overhangs, and vertical discontinuities, were found to be the primary drivers of severe damage. Furthermore, pounding and short-column effects were identified as the most prevalent damage types across all three provinces. The results demonstrate that pre-existing structural irregularities significantly exacerbated the seismic vulnerability of the RC building stock. This research emphasizes the importance of stringent adherence to design codes and suggests that integrating digital imagery into post-disaster surveys can significantly enhance the accuracy of damage classification for future earthquake resilience. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

Semantic segmentation of crowdsourced street-level imagery plays a critical role in urban analytics by enabling pixel-wise understanding of urban scenes for applications such as walkability scoring, environmental comfort evaluation, and urban planning, where robustness to geometric transformations and projection-induced symmetry variations is essential. This study presents a comparative evaluation of two primary families of semantic segmentation models: transformer-based models (SegFormer and Mask2Former) and prompt-based models (CLIPSeg, LangSAM, and SAM+CLIP). The evaluation is conducted on images with varying geometric properties, including normal perspective, fisheye distortion, and panoramic format, representing different forms of projection symmetry and symmetry-breaking transformations, using data from Google Street View and Mapillary. Each model is evaluated on a unified benchmark with pixel-level annotations for key urban classes, including road, building, sky, vegetation, and additional elements grouped under the “Other” class. Segmentation performance is assessed through metric-based, statistical, and visual evaluations, with mean Intersection over Union (mIoU) and pixel accuracy serving as the primary metrics. Results show that LangSAM demonstrates strong robustness across different image formats, with mIoU scores of 64.48% on fisheye images, 85.78% on normal perspective images, and 96.07% on panoramic images, indicating strong semantic consistency under projection-induced symmetry variations. Among transformer-based models, SegFormer proves to be the most reliable, attains higher accuracy on fisheye and normal perspective images among all models, with mean IoU scores of 72.21%, 94.92%, and 75.13% on fisheye, normal, and panoramic imagery, respectively. LangSAM not only demonstrates robustness across different projection geometries but also delivers the lowest segmentation error, consistently identifying the correct class for corresponding objects. In contrast, CLIPSeg remains the weakest prompt-based model, with mIoU scores of 77.60% on normal images, 59.33% on panoramic images, and a substantial drop to 59.33% on fisheye imagery, reflecting sensitivity to projection-related symmetry distortions. Full article

Urban Green Spaces (UGSs) are essential for ecological sustainability and public health, offering benefits such as air pollution reduction, urban cooling, and recreational opportunities. However, existing evaluation tools remain inconsistent, often assessing isolated dimensions like accessibility or aesthetics without fully integrating health considerations. A systematic approach is needed to understand how these tools measure UGS quality and their relevance to health outcomes. This study employs a literature review (PRISMA framework) to analyze UGS evaluation tools with a focus on quality and health implications. A search in Scopus and Web of Science identified 14 relevant studies. Data extraction examined tool structure, assessed dimensions, data collection methods, geographic applications, and integration of health indicators. The review identified 13 distinct tools varying in complexity and methodology, from standardized checklists to GIS-based analyses. While key dimensions included accessibility, safety, aesthetics, and biodiversity, health-related factors were inconsistently integrated. Few tools explicitly assessed physical, mental, or social health outcomes. Technological innovations, such as Google Street View and AI-based analysis, emerged as enhancements for UGS evaluation. Despite methodological advances, gaps remain in linking UGS quality assessments to health outcomes. The lack of standardized health metrics limits applicability in urban planning. Future research should focus on interdisciplinary frameworks integrating environmental and health indicators to support the creation of sustainable and health-promoting UGS. Full article

In recent decades, urban residents’ perceptions of their surrounding environment have been widely studied, especially pertaining to the association between environmental settings and humans’ psychological wellbeing. Many studies have used aerial imagery to derive environmental properties through image classification to approximate humans’ perceived environment, while a growing number of studies use street view imagery to achieve the same with image segmentation. There is limited research comparing the two approaches. This study aims to examine how the environmental properties derived from aerial and street view images correspond with each other. We utilized two study sites in urban communities in New Jersey, United States. High-resolution aerial images were acquired and classified to derive environmental properties within set buffer zones around sample points where Google Street View images were collected for image segmentation to derive corresponding environmental properties. Several buffer sizes were experimented with. The results show that the amount of greenness and individual environmental elements derived from street view versus aerial images can be quite different at the same locations. The amount of trees derived has a greater concordance between aerial and street views than the amount of buildings derived. The amounts of grass and roads are not in agreement between the two views. Trees derived from street view images correspond with those derived from aerial better when using a small, 30 m buffer. Low-rise buildings and grass agree better when using larger buffer sizes such as 60 m and 100 m. Roads correspond better when larger buffers are employed in green environments, but smaller buffers in environments with limited greenness. Our findings indicate that the choice of buffer size used when combining environmental properties derived from both aerial and street view images together should consider both the environmental elements involved and the type of environmental settings. Full article

This study proposed a methodology for obtaining a valid database of food retail establishments and characterized the community food environment, understood as the distribution and type of food outlets, in a Brazilian medium-sized municipality after the collapse of a mining tailings dam. An ecological study was conducted with establishments selling food for home consumption (butcher shops, fish markets; fruit and vegetable specialty markets; large- and small-chain supermarkets; bakeries and local markets) and immediate consumption (bars, snack bars, and restaurants). For home-consumption establishments, data were requested from governments and completed with website/app searches, virtual audits (Google Street View), and on-site audits. For immediate-consumption establishments, only on-site audit was used due to the low quality of the secondary databases. Agreement between databases was assessed with the Kappa statistic. Density (d) was calculated by the area (in km²) of the sampling stratum. Public databases presented low validity (23.0%; Kappa −0.388; p = 1.000), even after virtual auditing (31.4%; Kappa 0.37; p < 0.001). 96 establishments for home consumption and 261 for immediate consumption were identified, with predominance of local markets (35.4%), bars (35.2%), and snack bars (29.1%). The region with the highest density of establishments was the “Other Areas” stratum (d = 4.7 for home-consumption establishments and d = 13.2 for immediate-consumption establishments). Audit proved most effective, especially for small establishments. The lack of governmental databases and the identified food environment should inform municipal policies to promote food and nutrition security and reduce inequalities after the disaster. Full article

Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding these elements is essential for accurately estimating property values, whether for commercial or tax purposes. Recently developed methods based on web scraping and automatic detection using artificial intelligence have proven effective but require substantial human and financial resources, often unavailable in small cities. As a solution, this study proposes and evaluates a simpler mechanism for assessing neighborhood quality using Google Street View images and a scoring system in a human-centered approach. Based on image interpretation, a set of weights is assigned to each point, resulting in a micro-neighborhood quality assessment. This study was conducted in three Latin American cities, and the resulting variable was integrated into hedonic price models. The findings demonstrate the feasibility and effectiveness of the proposed approach. The novelty of this study lies in applying a method based on quasi-objective criteria and adapted to cities with limited technological resources. Full article

This paper examines how 360-degree panoramic tours can enhance heritage promotion, accessibility, and engagement, illustrated through two case studies: the Understory Art and Nature Trail in Northcliffe and the Subiaco Hotel in Perth, Western Australia. The Understory Art Trail was deployed in Google Street View to deliver an interactive, virtual walkthrough of outdoor art installations. This made the site accessible to a geographically diverse global audience, including those unable to visit in person. In contrast, the Subiaco Hotel tour was created with 3DVista. It integrated multimedia features such as historical photographs, architectural drawings, and narrative audio, offering users a layered exploration of built heritage. The two studies were designed so that frameworks like Technology Acceptance Model (TAM) could be applied to them to evaluate visitor experience. However, this paper focuses on the workflow for providing 360-degree panoramic tours, the integration of AR, low-cost digital twins, and the testing of interactive web platforms. Google Street View demonstrates ease of use through familiar navigation, while 3DVista reflects usefulness through its richer interpretive features. By analyzing workflows and digital strategies on both platforms, the study evaluates their effectiveness in increasing online visitor engagement, supporting heritage tourism, and communicating cultural significance. Challenges related to technical limitations, geolocation accuracy, audience targeting, and resource constraints are critically discussed. The findings demonstrate that context-sensitive applications of 360-degree tours are valuable for visibility, education, and long-term preservation. The paper concludes with targeted recommendations to guide future heritage projects in leveraging immersive digital technologies to expand audience engagement and support sustainable heritage management. Full article

Urban greening initiatives, including the incorporation of street trees, have been widely recognized for a variety of environmental benefits. However, their economic impact on retail, in particular, the impact of street trees on the visibility of signs, has been underexplored. Street trees can obscure retail signs, potentially reducing customer engagement and discouraging retailers from paying higher rents for such locations. This paper investigates how the blocking of retail signage by street trees affects monthly rent in developed commercial districts in Seoul. It identifies, through Google Street View and state-of-the-art deep-learning-based semantic segmentation methods, environmental elements such as street trees, sidewalks, and buildings; quantifies their proportions; and analyzes their impact on rent using OLS regression, controlling for socio-economic variables. The results reveal that rents significantly diminish when street trees blocking views of retail signs increase. Our findings require more nuanced consideration by planners and policymakers in balancing both environmental and economic demands toward sustainable street design and planning. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Urban growth has intensified the generation of solid waste, particularly in densely populated and vulnerable neighborhoods, leading to environmental degradation and public health risks. This study presents a multidisciplinary methodology to estimate the mass of macroplastic litter mobilized from urban surfaces into nearby watercourses during storm events. Focusing on the Villa Páez neighborhood in Cordoba, Argentina—a data-scarce and flood-prone urban basin—the approach integrates socio-environmental surveys, field observations, Google Street View analysis, and hydrologic modeling using EPA SWMM 5.2. Macroplastic accumulation on streets was estimated based on observed waste density, and its transport under varying garbage collection intervals and rainfall intensities was simulated using a conceptual pollutant model. Results indicate that plastic mobilization increases substantially with storm intensity and accumulation duration, with the majority of macroplastic mass transported during high-return-period rainfall events. The study highlights the need for frequent waste collection, improved monitoring in vulnerable urban areas, and scenario-based modeling tools to support more effective waste and stormwater management. Full article

The world is characterised by the growing volumes and flow of goods, which, amid benefits to economic development, result in negative externalities affecting the sustainability of cities. Although numerous studies have analysed the locational patterns of logistics facilities in cities, further research is required to examine their real estate patterns and trends. The aim of the paper is, therefore, to analyse the value of land accommodating logistics facilities in the City of Cape Town municipality, South Africa. Given the lack of dedicated geo-spatial data, logistics firms were searched on Google Maps, utilising a combination of aerial photography and street view imagery. Three main attributes of land parcels hosting logistics facilities were thereafter captured from the municipal cadastral information: property extent, street address, and property number. The latter two were used to extract the 2018 and 2022 property market values from the valuation rolls on the municipal website, followed by statistical, spatial, and geographically weighted regression (GWR) analyses. Zones near the central business district and seaport, as well as areas with prime road-based accessibility, had high market values, while those near the railway stations did not stand out. However, GWR yielded weak relationships between market values and the locational variables analysed, arguably showing a disconnect between spatial planning and logistics planning. Towards augmenting sustainable logistics, it is recommended that relevant stakeholders strategically integrate logistics into spatial planning, and particularly revitalise freight rail to attract investment to logistics hubs with direct railway access. Full article

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article