System and method of automated training of an airborne lidar bathymetry machine learning system using multibeam echo sounding information

A method comprising:

• obtaining a plurality of lidar frames associated with an airborne light detection and ranging (lidar) bathymetry (ALB) system, each lidar frame of the plurality of lidar frames associated with a respective measurement swath within a surveyed area;
• generating a plurality of features corresponding to each lidar frame of the plurality of lidar frames;
• and
• processing the plurality of features corresponding to each lidar frame using a trained ALB segmentation machine learning network, wherein processing the plurality of features using the trained ALB segmentation machine learning network includes performing inference to generate one or more segmentation masks indicative of predicted seabed feature locations detected in each lidar frame, and wherein the trained ALB segmentation machine learning network is trained using ground truth seabed feature location annotation information determined from multibeam echo sounder (MBES) bathymetry data.

A system comprising:

• at least one processor;
• and
• a memory storing instructions which when executed by the at least one processor, causes the at least one processor to:
• obtain a plurality of lidar frames each comprising a respective plurality of lidar measurement points obtained along a respective measurement swath within a surveyed area and associated with a first coordinate system corresponding to an airborne light detection and ranging (lidar) bathymetry (ALB) system;
• obtain multibeam echo sounder (MBES) bathymetry data comprising a plurality of MBES data points indicative of locations on a seabed within the surveyed area, the plurality of MBES data points associated with a second coordinate system corresponding to the surveyed area and different from the first coordinate system;
• perform projection between the first coordinate system corresponding to the ALB system and the second coordinate system corresponding to the surveyed area, to thereby determine a subset of corresponding MBES data points corresponding to the respective measurement swath for each lidar frame of the plurality of lidar frames;
• generate annotation information indicative of a ground truth location of the seabed within each lidar frame of the plurality of lidar frames, the annotation information generated based on the subset of corresponding MBES data points and using the first coordinate system corresponding to the ALB system;
• and
• train a machine learning network to identify seabed bathymetry features within input lidar frames, wherein the training is performed using training data comprising the plurality of lidar frames and the generated annotation information for each lidar frame of the plurality of lidar frames.