Available at: https://digitalcommons.calpoly.edu/theses/3410
Date of Award
8-2026
Degree Name
MS in Computer Science
College
College of Engineering
Advisor
Alex Dekhtyar
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
This thesis presents the Rapid Urban Forest Assessment (RUFA) system, a web-based platform that integrates urban tree inventories and aerial tree detection to assess forest health across California’s census-designated places. RUFA combines inventoried tree records with coordinates detected from high-resolution multispectral imagery using convolutional neural networks, then computes a composite RUFA Score from four metrics: canopy cover percentage, trees per capita, tree diversity (TD-50), and tree evenness. The thesis addresses two engineering challenges in building the dashboard: querying and aggregating over seven million tree records in real time, and rendering spatial summaries at multiple zoom levels without recomputing cluster assignments on every map interaction. An evaluation of spatial indexing strategies motivates a MySQL schema combining R-tree candidate pruning with cascading materialized views, enabling sub-second city-level queries, while a SuperCluster index paired with Voronoi tessellation supports interactive map visualization. Deployed on AWS, RUFA provides urban foresters, planners, and researchers with RUFA Scores comparable across communities and multi-scale map outputs to support evidence-based urban forestry management.