AI Powered Urban Green Infrastructure Assessment Through Aerial Imagery of an Industrial Township

TL;DR

This paper introduces an AI-based method using deep learning and cloud computing to accurately assess urban green canopy coverage from drone imagery, aiding urban planning and environmental management.

Contribution

It presents a novel, scalable approach combining object-based image analysis and high-performance cloud processing for urban vegetation assessment.

Findings

High accuracy in canopy detection from drone images

Efficient processing of large datasets on cloud platforms

Potential for improved urban forestry management

Abstract

Accurate assessment of urban canopy coverage is crucial for informed urban planning, effective environmental monitoring, and mitigating the impacts of climate change. Traditional practices often face limitations due to inadequate technical requirements, difficulties in scaling and data processing, and the lack of specialized expertise. This study presents an efficient approach for estimating green canopy coverage using artificial intelligence, specifically computer vision techniques, applied to aerial imageries. Our proposed methodology utilizes object-based image analysis, based on deep learning algorithms to accurately identify and segment green canopies from high-resolution drone images. This approach allows the user for detailed analysis of urban vegetation, capturing variations in canopy density and understanding spatial distribution. To overcome the computational challenges associated with processing large datasets, it was implemented over a cloud platform utilizing high-performance processors. This infrastructure efficiently manages space complexity and ensures affordable latency, enabling the rapid analysis of vast amounts of drone imageries. Our results demonstrate the effectiveness of this approach in accurately estimating canopy coverage at the city scale, providing valuable insights for urban forestry management of an industrial township. The resultant data generated by this method can be used to optimize tree plantation and assess the carbon sequestration potential of urban forests. By integrating these insights into sustainable urban planning, we can foster more resilient urban environments, contributing to a greener and healthier future.