Date of Award


Degree Name

MS in Computer Science


Computer Science


College of Engineering


Jonathan Ventura

Advisor Department

Computer Science

Advisor College

College of Engineering


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.

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