Urban greenery supports cities in achieving Sustainable Development Goals, but it is increasingly affected by multiple stressors impacting its health. Owing to the high costs of greenery inspection and monitoring, local governments often lack adequate data to effectively manage their urban greenery and prevent damage. In this Review, we present an overview of technology-supported methods and tools to measure the health of urban greenery and discuss the space–time resolution trade-offs associated with the various methods presented. To inform researchers and policymakers in global cities, we highlight how high-resolution urban greenery health data can support in achieving Sustainable Development Goals at scale.@en

An increasing number of studies suggest that biophilia encompasses benefits resulting from human-nature interactions. However, quantifying these effects remains challenging. Since natural features vary worldwide, this study explores whether people perceive biophilia universally or if it is influenced by local or geographical conditions. To this end, we quantify, qualify, and map biophilic perceptions (BP) across terrestrial biomes. We first surveyed 400 people in eight cities to identify urban features evoking more positive feelings via Google Street View imagery. Thereafter, survey outcomes were used to calculate specific metrics (coverage, diversity, distribution, intensity, specificity) aimed at measuring BP using a machine learning model to detect 25 visual biophilic classes (BC). We found that people yield greater benefits from eye contact with nature-based elements within the cityscape unanimously, regardless of biome or gender. We provide AI-driven measurement tools applicable to any city globally to foster understanding and the enhancement of biophilic experiences.@en

Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While the current greenery inspection techniques can help in taking effective measures, they often require a high amount of human labor, making frequent assessments infeasible at city-wide scales. In this article, we present GreenScan, a ground-based sensing system designed to provide health assessments of urban trees at high spatio-temporal resolutions, with low costs. The system uses thermal and multispectral imaging sensors fused using a custom computer vision model to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates a novel approach for autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low costs.

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In response to socio-ecological challenges, cities around the world are implementing greenification and urban forestry. While these strategies contribute to reducing the ecological footprint, they often overlook various social implications. This explains the increasing global attention to Biophilia, which emphasizes human–nature interaction to enhance the quality of urban life. Despite its historical roots spanning centuries, Biophilia is still considered an emerging research field, as shown by debate on evidence-based research and measurement of its multidimensional impacts. Although the beneficial effects of Biophilic Design (BD) are well documented thanks to the small-scale and immediate outcomes, the long-term potential of Biophilic Urbanism (BU) offers less evidence, limiting its utilization and investment. This paper provides a comprehensive theoretical-practical framework on Biophilia, BD, and BU through a 60-year systematic literature review based on a three-metric approach (quality, quantity, and application). Investigating concepts and practices, we delve into biophilic effects on humans and urban livability, analyze tools to measure them, and explore methods to translate them into the built environment. In spite of the growing body of studies and advancements in the last decade, our review findings highlight the need for further insights, especially regarding BU. The study aims to promote Biophilia Upscaling as a strategy to maximize its direct and indirect benefits across urban scales, thereby promoting BU and expediting a paradigm shift in city planning. In metropolises conceived as bioregional systems, where nature plays a key role in ensuring ecological services and citizens’ well-being, BU can assist designers, planners, and city makers in addressing the urban agenda toward higher environmental and social standards.@en

Crime has major influences in urban life, from migration and mobility patterns, to housing prices and neighborhood liveability. However, urban crime studies still largely rely on static data reported by the various institutions and organizations dedicated to urban safety. In this paper, we demonstrate how the use of digital technologies enables the fine-grained analysis of specific crimes over time and space. This paper leverages the rise of ubiquitous sensing to investigate the issue of bike theft in Amsterdam—a city with a dominant cycling culture, where reportedly more than 80,000 bikes are stolen every year. We use active location tracking to unveil where stolen bikes travel to and what their temporal patterns are. This is the first study using tracking technologies to focus on two critical aspects of contemporary cities: active mobility and urban crime.@en

In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city's canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback-Leibler control cost to measure the deviation of the autonomous vessel's movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

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