Our technology builds on decades of research on land cover change detection across multiple sectors. The algorithms are not just data driven but also incorporate physical laws which makes them more powerful than traditional black box machine learning techniques.
Algorithms have been validated through extensively using non-trivial reference datasets.
The use of physical principles make the algorithms much more robust to atmospheric distrubances such as clouds, shadows, aerosols which are a major issue in satellite imagery analysis.
Our technology portfolio consists of patented algorithms that span multiple disciplines such as water, agriculture, forestry and urbanization.
Our processing pipelines do all the heavy lifting and produce relevant physical quantities that are easy to integrate in existing workflows.
Founder and CEO
Experienced business and product development executive. Pioneered complex earth observation projects from strategic planning to completion including collaborations in the US and overseas. Holds a B.A. in International Relations from Johns Hopkins University and a M.A. in Security Studies from Georgetown University.
Founder and CTO
Seasoned machine learning expert with extensive experience in satellite imagery analysis. Co-inventer of multiple patents that span across various domains such as water, agriculture, forestry and urbanization. Holds a PhD in Computer Science from University of Minnesota.
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