Solution:

1. We leverage the European Union’s Copernicus SENTINEL-2 mission, which comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit phased at 180° to each other, to access high quality satellite imagery with a high revisit time.
   
2. We then apply our own custom Argus Pixel-level labels to determine forest cover segmentation using advanced machine learning neural networks – to a 99.6% accuracy.
   
3. Our model then detects changes in forest cover — while ignoring seasonal patterns, nonhuman interventions, and more layers of obfuscation.
4. Finally we apply a risk level (low medium high) with threshold distances from nearest deforested area.
   
5. You access our findings via a report available on our Argus self-service digital portal.

Company:

Argus is a machine learning and satellite imagery analysis leader with a focus on creating digital tools that simplify and streamline public compliance across multiple industries Leveraging state-of-the-art machine learning, artificial intelligence, and deep learning techniques, combined with high revisit/high resolution remote satellite imagery to reduce compliance risk and inform business decision-making for customers.