Tan, Y.; Li, W.; Chen, D.; Qiu, W. Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery. ISPRS Int. J. Geo-Inf.2023, 12, 419.
Tan, Y.; Li, W.; Chen, D.; Qiu, W. Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery. ISPRS Int. J. Geo-Inf. 2023, 12, 419.
Tan, Y.; Li, W.; Chen, D.; Qiu, W. Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery. ISPRS Int. J. Geo-Inf.2023, 12, 419.
Tan, Y.; Li, W.; Chen, D.; Qiu, W. Identifying Urban Park Events through Computer Vision-Assisted Categorization of Publicly-Available Imagery. ISPRS Int. J. Geo-Inf. 2023, 12, 419.
Abstract
Understanding park events and their categorization offer pivotal insights into urban parks and their integral roles in cities. This study utilized images and event category data from the New York City Parks Events Listing database to train a Convolutional Neural Network (CNN) for image-based park event categorization. Different CNN models were tuned to complete this multi-label classification task, their performances 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.
Keywords
urban green spaces; human activities; Convolutional Neural Networks; computation vision; urban parks
Subject
Social Sciences, Urban Studies and Planning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
