For developing the report “Geospatial AI/ML Applications and Policies: A Global Perspective,” WGIC conducted one-to-one interviews with more than thirty AI/ML experts in the geospatial industry, including WGIC Members. Here is the list of Geospatial Artificial Intelligence (GeoAI) Trends – five key developments in GeoAI that we will witness as per the experts.
1. Increased automation
Many of the current geospatial AI/ML tasks require some aspects of human intervention to get adequate results. Advances in AI/ML techniques will allow for full automation in areas like mapping, object identification, feature/attributes extraction in the objects identified e.g. number of lanes in a road, or condition/ damages in the road or building.
2. Better natural resource management
With advances in deep learning techniques and easy access to satellite imagery and remote sensing data, geospatial AI/ML will find greater adoption in industries like agriculture, forestry, climate change, etc., e.g., those that involve tracking and managing natural resources.
3. Real-time applications
Several of the current geospatial AI/ML techniques require days, weeks, or even months to turn geospatial data into actionable results. Increasing computing power, edge computing, better algorithms, and ML support in-field equipment will allow for the development of real-time and near real-time geospatial AI/ML applications.
4. More data for AI/ML
With the advancement of highly-capable miniaturized remote sensing, imagery, and LIDAR systems and satellites, businesses will have access to a greater collection of real-time geospatial and remote sensing data with increasingly higher resolution/quality that will allow for new geospatial AI/ML use-cases currently not possible.
5. Accessibility of AI/ML applications
The application of geospatial AI/ML techniques to business problems currently requires trained data scientists and machine learning engineers. With pre-trained ML models integrated with GIS software, geospatial AI/ML will become accessible for all, from a small farmer to an executive at a large enterprise.