The process of transforming the geospatial sector through the application of Artificial Intelligence (AI) involves the utilization of technologies that allow machines to interpret images from satellites, drones, LiDAR point clouds, and aerial photographs of the Earth. Nevertheless, developing deep learning algorithms from scratch calls for huge amounts of labeled data, powerful GPUs, and time for development. Therefore, the necessity of using pre-trained models for refinement has appeared in modern workflows of GeoAI.