Up to now, Deep Learning has mostly aimed at understanding individual components for example, the detection of a very specific object. The results can rarely be generalized and only work in limited conditions. With deeeper.learning we have developed a holistic approach to greatly improve the accuracy, time-to-market and cost of Deep Learning. Instead of loosely “programming” neural networks, we are developing a holistic geo-AI.

and accurate

Holistic, almost human understanding instead of single applications: With every new task, our Geo-AI develops a holistic understanding of the Earth’s surface. It can easily cluster, segment, and compare data. Consequently, the application to new, previously not explicitly trained issues is very easy.

and fully automated

Machine learning from the first to the last step: Through the increased use of machine learning models in every step of the process and reduction of human input to a minimum, an area-wide scalable system is created.

Process steps

Input data selection

Extraction of geospatial information

Quality control of the network output

Integration into the target system