Publication Date
5-24-2025
Document Type
Conference Proceeding
Publication Title
Communications in Computer and Information Science
Volume
2487 CCIS
DOI
10.1007/978-981-96-6400-9_11
First Page
145
Last Page
156
Abstract
The accurate monitoring of tree canopy coverage is crucial for social and environmental studies as well as city planning. Our goal is to provide a solution that utilizes available resources like yearly aerial imagery to generate tree data to track all tree canopy within a city such as San Jose, CA. By utilizing open-source software and publicly available data, we streamlined the process by eliminating the need for expensive equipment and time-consuming manual efforts associated with traditional methods. The methodology involves the conversion of aerial imagery datasets into geo-referenced TIFF format through a resampling process. To optimize efficiency, we strategically select 2% of the images from the dataset for labeling, minimizing the manual effort required. The labeled images serve as the foundation for training our advanced model, enabling it to accurately identify and categorize individual trees. The result is a highly efficient, cost-effective solution that significantly reduces manual labor while providing precise tree canopy coverage calculations for the entire city. Our approach not only improves accuracy but also enhances scalability for future endeavors such as adaptation to seasonal variations, monitoring tree growth patterns, and identifying areas susceptible to potential canopy loss or even potential adoption of our solution by other cities.
Keywords
computer vision, data mining, deep learning, GIS, machine learning, Tree canopy, urban planning
Department
Computer Engineering
Recommended Citation
Martin Alvarez Lopez, Manan Choksi, Sai Yaaminie Ganda, Vaibhavi Hiteshkumar Savani, and Bernardo Flores. "San Jose Urban Forest – An Open-Source Tree Canopy Surveying and Assessment Tool" Communications in Computer and Information Science (2025): 145-156. https://doi.org/10.1007/978-981-96-6400-9_11
Comments
This is a post-peer-review, pre-copy edit version of a chapter published in Huang, L. (eds) Machine Learning and Soft Computing . ICMLSC 2025. Communications in Computer and Information Science, vol 2487. Springer, Singapore. The final authenticated version is available online at: https://doi.org/10.1007/978-981-96-6400-9_11