Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

Interdisciplinary research has significantly advanced our understanding, benefits, and measurements of Urban Green Space (UGS). Further, the rapid expansion of research on this topic has resulted in a diverse array of definitions, which can rely on implicit assumptions without a formal definition. This variability highlights the need for a carefully structured framework to refine and combine these definitions. This narrative review examines constructs underlying UGS, particularly focusing on the spatial aspects of how we spatially measure UGS, the measurements of UGS, and how we define exposure; the latter focuses on two methods: viewsheds and image segmentation. Our findings reveal a shift in UGS measurement focus, moving beyond simple quantification of how much green space exists, to incorporate visibility, accessibility, and availability dimensions. Furthermore, advancements in computational tools, including artificial intelligence-driven methods, now enable high-resolution visibility measurements on a city-wide scale, supporting epidemiological research and urban development. These insights aim to guide researchers and practitioners in selecting suitable methodologies and datasets, as well as explicitly defining UGS in their work through a construct-based approach.

Interdisciplinary research has significantly advanced Urban Green Space (UGS) measurement by employing objective methods tailored to diverse research objectives. Despite this progress, the varied development of UGS measurement techniques calls for a comprehensive review to integrate and evaluate these approaches. This narrative review examines constructs underlying UGS measurements, emphasizing objective and GIS-enabled datasets, and highlights recent advancements in measuring greenness visibility. Specifically, it focuses on two methods: the viewshed-based and the image segmentation-based methods, proposing their classification under "Perspective View." Our findings reveal a shift in UGS measurement focus, moving beyond simple quantification to incorporate visibility, accessibility, and availability dimensions. Moreover, this review demonstrates the growing relevance of hybrid techniques integrating quantitative and qualitative approaches, such as combining GPS data with visibility analyses. These techniques bridge the gap between physical and perceptual aspects of greenspaces, enhancing their applicability in urban planning and public health. Furthermore, advancements in computational tools, including AI-driven methods, now enable high-resolution visibility measurements on a city-wide scale, supporting epidemiological research and urban development. These insights aim to guide researchers and practitioners in selecting suitable methodologies and datasets, fostering the collection of diverse UGS data for practical management and policymaking. By providing a structured framework for UGS measurement, this review contributes to the development of more equitable and effective urban greenspace strategies.