  It is of interest to localize a ground-based LiDAR point cloud on remote
sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map
a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the
aerial imagery. We proposed a contrastive learning-based method that trains on
DEM and high-resolution optical imagery and experiment the framework on
different data sampling strategies and hyperparameters. In the best scenario,
the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed
method is promising for feature learning from RGB and DEM for localization and
is potentially applicable to other data sources too. Source code will be
released at https://github.com/wtseng530/AVLocalization.
