Whenever we talk about Artificial Intelligence, Machine Learning, or Deep Learning, the cautionary tales from science fiction cinema arise where computers learn too much and become a threat.
The reality is that there are things that computers do well and things that humans do well, but they don’t necessarily intersect. Computers crunch numbers and provide statistical analysis, while humans are best at recognizing patterns and making decisions based on that data. Both have their strengths and their roles.
Currently, Machine Learning in the geospatial arena is going through a renaissance where massive amounts of imagery collected can be filtered down to just the mission-critical data that needs to be interpreted by specialists.
This was the key theme of a recent Directions Magazine guest article by Georg Hammerer, Chief Technology Officer – Applications for Hexagon’s Geospatial division.
The article also highlighted how Machine Learning algorithms can be trained to automatically ingest and process ever-growing amounts of data – taking the load off analysts. It also discussed the difference between Machine Learning and Deep Learning, and gave examples of geospatial applications with these capabilities.
Hexagon’s Geospatial division continues to be on the forefront of bringing Deep Learning geospatial applications to life.
- Introduction to Machine Learning:This video provides an introduction into Machine Learning, and shows how solutions from Hexagon’s Geospatial division help in advance geospatial data processing.
- Understanding and Using Machine Learning and Deep Learning Operators: This white paper provides insights into advanced geospatial data processing, and how you can leverage Machine and Deep Learning to perform the heavy lifting of your data.
- Machine and Deep Learning in ERDAS IMAGINE: Machine Learning has been used in ERDAS IMAGINE for years. The solution now includes a number of Machine Learning and Deep Learning operators that allow you to build, train, run, and deploy these new and powerful techniques. In addition, Spatial Modeler provides a graphical tool for creating models to perform remote sensing and GIS processing tasks in a repeatable, scalable way.
Machine and Deep Learning have seen tremendous adoption over last two decades – with no sign of slowing down. Hexagon’s Geospatial division will continue advancing the ability for global organizations to leverage massive datasets for all decision-making.
