February 6-8, 2022  |  Colorado Convention Center   |  Denver, CO, USA

Agenda Session

AXIS GeoSpatial LLC

Latest Advances & Applications in Topobathymetry

Feb 06 2022

2:00 PM - 3:30 PM MT

Room 111

This session will include presentations showcasing the latest performance of topobathymetric scanning hardware, the potential of full waveform analysis for water bottom detection, and an evaluation of topobathymetric lidar point density for object detection. This technology is especially well-suited for capturing medium-sized clear water rivers and constitutes a cost-effective alternative to topo-bathymetric acquisition from a manned platform. Additionally, airborne topo-bathymetry using a green wavelength has established as a state-of-the-art survey technology for shallow water areas along coasts, lakes and rivers. These presentations will answer your questions about depth, penetration, accuracy, real-world performance and what challenges still remain.

Improved Topo-Bathymetric Lidar Point Density for Coastal Object Detection & River Surveys
Tim Webster, Applied Geomatics Research Group, NSCC

In 2018, the Leica Chiroptera II topo-bathymetric lidar sensor was upgraded to increase the point density by a factor of 4. Experiments were carried out to evaluate and validate the potential benefits of the increased point density for a study site in Nova Scotia that has been surveyed regularly since 2014. The 4X upgrade aided in the maximum depth increase of 2-3 m. Solid 1 m-3 cubes (white & green) were deployed at various depths on the seabed. In all cases, the number of points defining the white cubes increased by 25% and the apparent size of the cubes increased with depth. The point density of the green cube was less than that of the white cube at a given depth, confirming the importance of reflectivity of the seabed for target detection. In 2019 an estuary and river were surveyed for channel detection and improved flood risk modelling and fish passage-habitat studies.

Exploiting Deep Learning Techniques for Topo-Bathy Lidar Data Classification and Point Cloud Editing
Vinod Ramnath, Teledyne Geospatial 

Automatic land-water classification of lidar data acquired by topo-bathy systems are required in order to apply refraction correction to water shots to generate accurate topo-bathy point cloud products. We have developed a Land-water classifier based on Recurrent Neural Network (RNN) architecture to automatically discriminate land and water shots. Long short-term memory (LSTM) architecture is introduced to take advantage of the intensity relationship within a lidar return signal time series and subsequently a pooling operation is applied to incorporate all information together for the final prediction. We have also developed a Noise Classifier based on a 3D Convolutional Neural Network (UNet) that has a strong capability to learn the geospatial and geometrical representation of noise to automate the lidar data editing process by invalidating noise in the lidar point cloud.

Session moderated by Amar Nayegandhi, Senior Vice President, Dewberry.

Session format includes presentations followed by an audience Q&A.

Featuring

Teledyne Geospatial

Hexagon

Dewberry

Airborne HydroMapping GmbH

Applied Geomatics Research Group, NSCC

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