Utilizing Deep Learning to Track Urban Density Parameters in Zoning Practice-Based Areas

Abstract

In response to the challenges posed by rapidly growing cities, there is a pressing need for innovative solutions in urban evaluation and orientation. Building density and building coverage ratio (BCR) are critical factors in this regard. Although several methods have been proposed for calculating BCR, there remains a need for a swift and effective technique to assist city administrators and planners in tracking density parameters. This study summarizes the challenges and future research directions in using a DL-based approach to measure BCR. To support the practical application of this tool, the main stages of the end-to-end process are highlighted, offering insights for further improvement. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.