We present an interdisciplinary study that applies computer vision to analyze urban parks in Abu Dhabi. Leveraging a dataset of approximately 12,000 user-generated images sourced from Google Maps, we built a pipeline to clean, process, and interpret visual information from green spaces, encompassing metadata filtering, geolocation curation, duplicate removal, and semantic outlier detection. Using YOLOv8 object detection, spatial density maps, temporal trend analyses, and behavioral heatmaps, we examine how parks are used and perceived by the public. Our findings reveal distinct spatiotemporal rhythms of park use, including strong evening peaks reflecting climate adaptation and weekend surges tied to family-oriented activity. Frequently detected objects such as trees, flowers, and fountains capture the ecological character of these spaces, with natural elements consistently dominating visual prominence and serving as proxies for the social and restorative functions parks fulfill. Spatial density maps further identify certain parks as primary centers of activity. We also reflect on the challenges of applying pretrained AI models to heterogeneous, user-generated data, including sampling bias and model transferability. This work contributes to broader Nature-Based Solutions (NBS) efforts and supports evidence-based urban green space planning in rapidly urbanizing contexts.

Authors:
Amina Menaa, Sorbonne University Abu Dhabi, United Arab Emirates
Lama Tarsissi, Sorbonne University Abu Dhabi, United Arab Emirates
Xavier Fresquet, Sorbonne Université, France

About the Presenter(s)
Amina Menaa is a master’s student in Data Science and AI at Middlesex University, Dubai, and a research assistant at Sorbonne University, Abu Dhabi. Her current work focuses on computer vision, geography, and environmental psychology.

Connect on Linkedin
https://www.linkedin.com/in/amina-menaa

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