Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 742 - 742

Опубликована: Янв. 13, 2025

The rapid increase in urban population density driven by development has intensified inequity green space distribution. Identifying the causes of changes equity and developing strategies to improve greening are crucial for optimizing resource allocation alleviating social inequalities. However, long-term spatio-temporal evolution visibility remains underexplored. This study utilized “Time Machine” feature capture street view images from 2014, 2017, 2021, analyzing its across residential communities Wuhan. Deep learning techniques statistical methods, including Gini coefficient location quotient (LQ), were employed assess distribution spatial street-level greenery. results showed that overall Wuhan increased 4.18% between 2014 2021. this improvement did not translate into better equity, as consistently ranged 0.4 0.5. Among seven municipal districts, only Jiang’an District demonstrated relatively equitable 2017 Despite a gradual reduction disparities visibility, mismatch persisted UGS growth distribution, leading uneven patterns equity. explores factors driving inequities proposes enhance greening. Key recommendations include integrating evaluation framework planning guide fair allocation, prioritizing greenery low-income neighborhoods, reducing hardscapes support planting maintenance tall canopy trees. These measures aim accessible visible resources promote access communities.

Язык: Английский

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Fine-grained building function recognition with street-view images and GIS map data via geometry-aware semi-supervised learning

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 137, С. 104386 - 104386

Опубликована: Фев. 24, 2025

Язык: Английский

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Modelling sunlight and shading distribution on 3D trees and buildings: Deep learning augmented geospatial data construction from street view images

Building and Environment, Год журнала: 2025, Номер unknown, С. 112816 - 112816

Опубликована: Март 1, 2025

Язык: Английский

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A Non-Contact Method for Detecting and Evaluating the Non-Motor Use of Sidewalks Based on Three-Dimensional Pavement Morphology Analysis

Sensors, Год журнала: 2025, Номер 25(6), С. 1721 - 1721

Опубликована: Март 10, 2025

This study proposes a non-contact framework for evaluating the skid resistance of shared roadside pavements to improve cyclist and pedestrian safety. By integrating friction tester laser scanner, we synchronize high-resolution three-dimensional (3D) surface texture characterization with coefficient measurements under dry wet conditions. Key metrics—including fractal dimension (FD), macro/micro-texture depth density (HLTX WLTX), mean (MTD), joint dimensions—were derived from 3D scans. A hierarchical regression analysis was employed prioritize influence parameters on across environmental Combined material types (brick, tile, stone) drainage performance, these metrics are systematically analyzed quantify their correlations resistance. Results indicate that raised macro-textures high FD (>2.5) significantly enhance dry-condition resistance, whereas recessed textures degrade performance. The model further reveals MTD dominate (β = 0.61 −0.53, respectively), while micro-texture (WLTX) seam critical predictors −0.76 0.31). In environments, is dominated by (WLTX < 3500) macro-texture-driven water displacement, higher WLTX values indicating denser micro-textures impede drainage. validates scanning enables efficient mapping data (e.g., pore connectivity, ≥0.25 mm) properties, supporting rapid large-scale pavement assessments. These findings establish data-driven linkage between measurable indicators (texture, morphometry, drainage) offering practical foundation proactive sidewalk safety management, especially in high-risk areas. Future work should focus refining predictive models through multi-sensor fusion standardized design guidelines.

Язык: Английский

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Toward Automated and Comprehensive Walkability Audits with Street View Images: Leveraging Virtual Reality for Enhanced Semantic Segmentation

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2025, Номер 223, С. 78 - 90

Опубликована: Март 13, 2025

Язык: Английский

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ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science

Computers Environment and Urban Systems, Год журнала: 2025, Номер 119, С. 102283 - 102283

Опубликована: Март 20, 2025

Язык: Английский

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Paved or unpaved? A deep learning derived road surface global dataset from mapillary street-view imagery

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2025, Номер 223, С. 362 - 374

Опубликована: Март 27, 2025

Язык: Английский

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Physical urban change and its socio-environmental impact: Insights from street view imagery

Computers Environment and Urban Systems, Год журнала: 2025, Номер 119, С. 102284 - 102284

Опубликована: Март 29, 2025

Язык: Английский

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Restoring nature, enhancing active mobility: The role of street greenery in the EU’s 2024 restoration law

AMBIO, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Abstract This article argues for the importance of integrating a mobility perspective into urban greenspace planning and practice related to 2024 EU Nature Restoration Law. Street greenery can play an important multifunctional role in promoting ecosystem services functions, sustainable mobility, human health well-being. However, planners need more evidence on how street vegetation affects well-being during everyday active as well what type, where whom enhance vegetation. We discuss current advancements gaps literature these topics, identify key research priorities support restoration policy practice. These include: moving beyond dominant scientific thinking being place through space understanding exposure experience; use multiple metrics with attention temporal dynamics; integration objective subjective assessments; investigating further reducing environmental injustices.

Язык: Английский

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Validating Pedestrian Infrastructure Data: How Well Do Street-View Imagery Audits Compare to Government Field Data?

Urban Science, Год журнала: 2025, Номер 9(4), С. 130 - 130

Опубликована: Апрель 17, 2025

Data on pedestrian infrastructure is essential for improving the mobility environment and planning efficiency. Although governmental agencies are responsible capturing data mostly by field audits, most have not completed such audits. In recent years, virtual auditing based street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to movement planning, but concerns about quality reliability of opensource geospatial pose barriers use governments. Limited research has compared in relation traditional government approaches. this study, we compare from an sidewalk audit platform (Project Sidewalk) with data. We focus neighborhoods diverse walkability income levels city Seattle, Washington DuPage County, Illinois. Our analysis shows that Project Sidewalk can be reliable alternative features. The agreement different features ranges 75% signals complete (100%) missing sidewalks. However, variations measuring severity challenges dataset comparisons.

Язык: Английский

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