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

2. 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)

3. 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"

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

5. Next Steps Replace SVM with 3D Convolutional Neural Network (CNN)
Voxelize data at resolution
Send voxels to CNN
Map result back to points

6. 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

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

8. Sign up for the beta at booth 17!
Burns Foster