K
Kathleen Martin
Guest
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 GeoAI Trends – five critical developments in GeoAI that we will witness as per the experts.
1. Increased Automation
Many of the current geospatial AI/ML tasks require 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 street 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
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 developing real-time and near real-time geospatial AI/ML applications.
Continue reading: https://www.geospatialworld.net/blogs/top-5-geoai-trends-for-the-year-ahead/
1. Increased Automation
Many of the current geospatial AI/ML tasks require 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 street 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
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 developing real-time and near real-time geospatial AI/ML applications.
Continue reading: https://www.geospatialworld.net/blogs/top-5-geoai-trends-for-the-year-ahead/
