Applications of Machine Learning in Hydrographic Data Processing U.S. Hydro 2019 Biloxi, MS – March 20, 2019 Presented by: Burns Foster

Recap CARIS has developed a Machine Learning algorithm for Sonar Noise Classification Based on Support Vector Machine (SVM) Beta version released Fall 2018 Surface filter Noise classifier (SVM)

Results Application Data Type Comments Like? Charting Kongsberg Takes a long time Reduces the need for manual cleaning Yes Crashing on large datasets Rejects some good data and keeps some noise data Missed some isolated noise Pipeline inspection R2Sonic Really good job on pipeline data Missed some noise near the pipe Infrastructure survey EM2040 Takes a long time to run Performs well on complex structures Some obvious noise missed No Site survey (wind farm) EM2040D Misses a lot of "noise"

Results Pros Cons Excellent performance in complex environments Overall reduction in manual cleaning Cons Slow Misses noise close to real features

Next Steps Replace SVM with 3D Convolutional Neural Network (CNN) Voxelize data at resolution Send voxels to CNN Map result back to points https://commons.wikimedia.org/wiki/File:Typical_cnn.png

Noise Classification with CNN Pros Less processing – faster! Run on GPU – faster! Learning – handles more cases Can be extended to multiple classes Cons Training requires expensive, specialized hardware GPU not required, but 100x faster

Noise Classification with CNN Results Better Faster Stronger Ready for beta 2.0

Sign up for the beta at booth 17! Burns Foster burns.foster@teledyne.com