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.

Quantifying and assessing urban greenery is consequential for planning and development, reflecting the everlasting importance of green spaces for multiple climate and well-being dimensions of cities. Evaluation can be broadly grouped into objective (e.g., measuring the amount of greenery) and subjective (e.g., polling the perception of people) approaches, which may differ – what people see and feel about how green a place is might not match the measurements of the actual amount of vegetation. In this work, we advance the state of the art by measuring such differences and explaining them through human, geographic, and spatial dimensions. The experiments rely on contextual information extracted from street view imagery and a comprehensive urban visual perception survey collected from 1000 people across five countries with their extensive demographic and personality information. We analyze the discrepancies between objective measures (e.g., Green View Index (GVI)) and subjective scores (e.g., pairwise ratings), examining whether they can be explained by a variety of human and visual factors such as age group and spatial variation of greenery in the scene. The findings reveal that such discrepancies are comparable around the world and that demographics and personality do not play a significant role in perception. Further, while perceived and measured greenery correlate consistently across geographies (both where people and where imagery are from), where people live plays a significant role in explaining perceptual differences, with these two, as the top among seven, features that influences perceived greenery the most. This location influence suggests that cultural, environmental, and experiential factors substantially shape how individuals observe greenery in cities. We also found that the spatial arrangement of greenery in the sight, rather than its proximity to the person, influences perception. Our study provides a new understanding of the deep relationships between objective and subjective street-level greenery assessments, contributing to a more human-centric design of green urban environments.

The availability of 3D building models has been increasing, but they often lack detail at the architectural scale. This paper presents a method for reconstructing façade openings in 3D building models by integrating Street View imagery (SVI). Methodologically, the paper advances opening reconstruction in two key ways: first, by introducing a mathematically derived method for estimating unknown intrinsic camera parameters, enabling metric 2D-to-3D projection without relying on multi-view imagery or pre-existing depth information. Second, the method extends single-image photogrammetry to accurately measure detailed façade openings, converting pixel coordinates into spatial coordinates. The proposed method is validated through case studies in Amsterdam. Quantitative evaluation using the Façade Re-projection Dice Score (FRDS) shows high spatial consistency between reconstructed openings and reference opening geometries, with most scores ranging from 0.84 to 0.98. Given the broad coverage of SVI, there is a significant potential for enhancing 3D city models in diverse urban contexts where current representations remain geometrically basic.

Outdoor thermal comfort is a crucial determinant of urban space quality. While research has developed various heat indices, such as the Universal Thermal Climate Index (UTCI) and the Physiological Equivalent Temperature (PET), these metrics fail to fully capture perceived thermal comfort. Beyond environmental and physiological factors, recent research suggests that visual elements significantly drive outdoor thermal perception. This study integrates computer vision, explainable machine learning, and perceptual assessments to investigate how visual elements in streetscapes affect thermal perception. To provide a comprehensive representation of diverse visual elements, we employed multiple computer vision models (viz. Segment Anything Model, ResNet-50, and Vision Transformer) and applied the Maximum Clique method to systematically select 50 representative ground-level images, each paired with a corresponding thermal image captured simultaneously. An outdoor, web-based survey among 317 students collected thermal sensation votes (TSV), thermal comfort votes (TCV), and element preference data, yielding 2,854 valid responses. The same survey was replicated in an indoor exhibition setting to provide a comparative reference against the outdoor experiment. A Random Forest classifier achieved 70% and 68% accuracy in predicting thermal sensation and comfort, respectively. Using Shapley Additive Explanations to interpret model outcomes, we uncovered that the colour magenta emerged as the most influential visual factor for thermal perception, while greenery – despite being participants most preferred element for cooling – showed weaker correlation with actual thermal perception. These findings challenge conventional assumptions about visual thermal comfort and offer a novel framework for image-based thermal perception research, with important implications for climate-responsive urban design.