Perceived cycling safety remains a critical determinant of bicycle use among adolescents. Previous studies have highlighted the role of street environments in shaping safety perceptions, but most rely on spatial attributes (e.g., road infrastructure, land-use indices) and rarely incorporate the cyclists’ visual perspective. This study proposes a multidimensional framework that integrates visual and spatial representations of urban streets to model perceived cycling safety. By embedding fine-grained visual indicators derived from street view imagery into the road network, this novel framework captures 31 features across six environmental dimensions. Existing studies typically model perceived cycling safety using only a road’s own attributes, neglecting the influence of nearby roads. To address this limitation, we develop an improved Graph Convolutional Network that incorporates geographic context. It integrates layer-wise attention and an adaptive loss function to handle class imbalance and capture spatial dependencies. Explainable artificial intelligence (XAI) techniques are applied to interpret feature importance within the spatial context, moving beyond linear assumptions of traditional models. The framework is applied to a perception survey focusing on adolescents in Ghent, Belgium. The proposed model achieves an overall accuracy of 83.1%, outperforming all baselines and presenting a major advancement in this domain. XAI analysis reveals that both texture complexity and color monotony of the built environment tend to reduce perceived cycling safety, while tree coverage has a positive effect. Overall, the framework offers an interpretable and scalable approach for mapping street-level safety perception, providing actionable insights for cycling-oriented urban design and the development of sustainable transport planning.

Road slopes shape mobility patterns and drive the reliability of urban simulations. Yet in most cities, road-level slope information remains scarce. We introduce Vision2Slope, a framework that leverages panoramic street view imagery to estimate road slopes using computer vision techniques. The workflow consists of three steps: (i) projecting panoramic images into road-aligned views; (ii) semantic-prompted image deskewing to correct geometric distortion induced by camera orientation; and (iii) a two-level slope estimation strategy that extracts point- and segment-level slope and relief characteristics from road-edge geometry and iterative regression to reduce outliers. Using Google Street View images from San Francisco and New York City, the framework estimates slopes for over 60,000 locations and 17,000 street segments. Point- and segment-level MAEs are 0.81°/0.57° and 0.72°/0.78°, respectively, with segment relief errors of 1.70 and 1.66 m. Conditional bias analysis reveals the influence of street-level environmental features on estimation accuracy. The proposed framework significantly outperforms the omnipresent 30 m digital elevation models and maintains robustness under simulated changes in camera orientation and imaging conditions. As an open and scalable workflow, Vision2Slope emphasizes the potential of street view imagery for cost-effective, detailed urban road slope mapping, enriching foundational data for vertical-aware urban analytics.

Parks are essential to urban well-being, making park satisfaction crucial for sustainable city development. Traditional survey-based approaches to understand sentiment towards parks among residents are often costly, time-consuming, and limited in scale. Recent social media–based studies have scaled such research but predominantly focus on text and frequently overlook visual information and the joint effects of text–image representations. This study presents an automated multimodal framework using crowdsourced reviews from Google Maps to model park satisfaction by integrating textual and visual features. Using Singapore as a case study, we analysed 76,869 textual reviews and 184,322 images associated with them. The results show that multimodal models are more useful than text-only approaches, with textual sentiment, emotional attributes, and image temporal characteristics identified as the most influential factors. These findings highlight the importance of multimodal analysis for advancing park research and informing planning and policy practices.

In the era of data-intensive science, the complexity and volume of geospatial data have grown exponentially. Compared to traditional data sources, non-traditional sources are more complex and structured, necessitating sophisticated methods and a series of decisions to transform raw data inputs into usable and actionable data products. This Special Issue, “Sustainable geospatial analytics and geoinformatics with repeatable, reproducible, and expandable (RRE) framework and design,” brings together a collection of seven pioneering papers that address the critical need for consistency and transparency in geospatial research. These studies explore diverse domains, including explainable machine learning, disaster risk assessment, urban ecological health, infectious disease control and scientific workflow management. Collectively, they advocate for the adoption of an RRE framework to ensure that results can be verified and reproducible across different environments and expanded with new data or methodologies. By integrating visual programming, service-oriented strategies, as well as Findable, Accessible, Interoperable, and Reusable (FAIR) principles, the featured research lowers technical barriers for non-experts while enhancing the robustness of complex models. This editorial synthesizes the contributions of these papers, highlighting how they foster a sustainable and collaborative geospatial knowledge ecosystem. This collection serves as a roadmap for the next generation of geoinformatics, where transparency and flexibility are foundational to addressing global environmental and social challenges.