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

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
