DOI: https://doi.org/10.15368/theses.2022.92
Available at: https://digitalcommons.calpoly.edu/theses/2569
Date of Award
8-2022
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Jonathan Ventura
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Cataloguing and classifying trees in the urban environment is a crucial step in urban and environmental planning. However, manual collection and maintenance of this data is expensive and time-consuming. Algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, though they generally struggle in the more varied urban environment. This work proposes a novel method for the detection of trees in the urban environment that applies deep learning to remote sensing data. Specifically, we train a PointNet-based neural network to predict tree locations directly from LIDAR data augmented with multi-spectral imaging. We compare this model to numerous high-performant baselines on a large and varied dataset in the Southern California region. We find that our best model outperforms all baselines with a 75.5\% F-score and 2.28 meter RMSE, while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach.