Joe Wilder
Master's Thesis Defense
Computer Science
Friday, April 24, 2026, 11:00 a.m.
Chase 175
Abstract
The application of AI/ML to shallow bathymetric mapping with airborne lidar remains limited by a lack of high-quality labeled datasets, extreme spatial variability, and noise introduced by environmental conditions and hardware limitations. These issues reduce the generalizability of traditional statistical approaches for extracting bathymetric points from lidar point clouds, with additional challenges for supervised neural networks arising due to the amount and quality of labeled data required to properly capture broad seafloor characteristics. Recent advances in point cloud machine learning methods have the potential to improve conventional bathymetric processing workflows.
This work examines modern self-supervised learning (SSL) methods for bathymetric lidar point cloud processing. Specific emphasis is given to exploring whether using SSL is effective in the airborne lidar bathymetry domain, and assessing if SSL can reduce the amount of labeled data required for model training while maintaining or possibly improving performance across differing survey sites.
Bio
Joe Wilder earned his undergraduate degree at Southern New Hampshire University in the Spring of 2024. He graduated from the computer science program with a minor in applied mathematics and a concentration in machine learning. During his undergraduate years, he gained valuable experience working on software development projects and artificial intelligence applications. At CCOM, he is pursuing a master’s in computer science and will continue his research on artificial intelligence.
