The applications of GeoAI are as wide and diverse as our imaginations. Many applications like remote sensing, image classification and object detection using computer vision, improve visual clarity and allow higher zoom levels using super-resolution networks, extract geospatial information from unstructured data using natural language processing, deep learning application for large 3D geospatial datasets etc. and have shown positive trends in GeoAI.

With the increasing focus on damage prevention, public safety concerns, and environmental damage due to underground utility strikes, it has become more critical to accuracy locate, store and be able to visualize subsurface utilities. We show several key technical and business details when implementing a subsurface mapping system from data collection to the use of machine learning for 3D modelling and visualization and give real world examples and use cases of the different levels of accuracy needed and how they were implemented in field operations.