Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery

Abstract

Understanding park events and their categorization offers pivotal insights into urban parks and their integral roles in cities. The objective of this study is to explore the efficacy of Convolutional Neural Networks (CNNs) in categorizing park events through images. Utilizing image and event category data from the New York City Parks Events Listing database, we trained a CNN model with the aim of enhancing the efficiency of park event categorization. While this study focuses on New York City, the approach and findings have the potential to offer valuable insights for urban planners examining park event distributions in different cities. Different CNN models were tuned to complete this multi-label classification task, and their performances were compared. Preliminary results underscore the efficacy of deep learning in automating the event classification process, revealing the multifaceted activities within urban green spaces. The CNN showcased proficiency in discerning various event nuances, emphasizing the diverse recreational and cultural offerings of urban parks. Such categorization has potential applications in urban planning, aiding decision-making processes related to resource distribution, event coordination, and infrastructure enhancements tailored to specific park activities.