CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data
This addresses a subtask in Earth Observation data localization for applications like mapping, but it is incremental as it builds on existing contrastive learning approaches.
The paper tackles the problem of localizing a digital elevation model (DEM) from aerial LiDAR on aerial imagery using a contrastive learning-based method, achieving a Top-1 score of 0.71 and Top-5 score of 0.81 in the best scenario.
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.