  We propose a novel data-driven approach for high-resolution bathymetric
reconstruction from sidescan. Sidescan sonar (SSS) intensities as a function of
range do contain some information about the slope of the seabed. However, that
information must be inferred. Additionally, the navigation system provides the
estimated trajectory, and normally the altitude along this trajectory is also
available. From these we obtain a very coarse seabed bathymetry as an input.
This is then combined with the indirect but high-resolution seabed slope
information from the sidescan to estimate the full bathymetry. This sparse
depth could be acquired by single-beam echo sounder, Doppler Velocity Log
(DVL), other bottom tracking sensors or bottom tracking algorithm from sidescan
itself. In our work, a fully convolutional network is used to estimate the
depth contour and its aleatoric uncertainty from the sidescan images and sparse
depth in an end-to-end fashion. The estimated depth is then used together with
the range to calculate the point's 3D location on the seafloor. A high-quality
bathymetric map can be reconstructed after fusing the depth predictions and the
corresponding confidence measures from the neural networks. We show the
improvement of the bathymetric map gained by using sparse depths with sidescan
over estimates with sidescan alone. We also show the benefit of confidence
weighting when fusing multiple bathymetric estimates into a single map.
