- Data Labeling: Teach the computer how to identify objects. This aspect of machine learning is paramount to achieving high levels of accuracy in the end result sets.
- Neural Nets: The core algorithms behind deep learning. There are many available, and more seem to be popping up every day. Google, Microsoft, Amazon and academia are investing millions into their respective projects to find the smartest and fastest way to answer questions. After all, time is money! By looking at the average salary from these companies on Glassdoor and the amount of committers they have listed on GitHub, we estimate there is approximately $50 million invested in the development of these open source projects. Google seems to have taken the lead with the most APIs, examples and documentation.
- Compute: The GPU hardware needed to both train and inference. NVIDIA is the world leader in the hardware behind this movement and has spent a lot of resources developing it simply based on market demand.
- Program of Record Integration: Use the output from these algorithms to alert, analyze and provide a common operating picture.
- User Engagement: Provide a solid mechanism to allow end users to achieve their objectives. We have an infinite amount of possibilities.



- Broad area geospatial object detection using autogenerated deep learning models
- Synthesizing training data for broad area geospatial object detection