Instant access to science-ready imagery and intelligence from multiple data sources.
For decades, sensors have been collecting data about our planet. Yet complex global systems like agriculture, deforestation, water cycles, and many others, affect billions but are still poorly understood. Not only are these systems critical to understand for the good of humanity, they're also important for businesses as they adapt to — and attempt to thrive in — an ever-changing environment.
We are seeing an exponential rise in the number of real-world sensors, producing ever more data. Combining multiple sources provides a far richer view than that of any single data source. To harness this power and enable global-scale computation, it's necessary to have a data refinery that combines data from diverse sources, cleans it up and makes it ready for modeling - and a platform to upon which to build living, learning models. That's exactly what we've built at Descartes Labs.
Detecting Construction Starts
Using synthetic aperture radar (SAR), we developed a proprietary model that can identify new construction starts on the ground on a monthly basis, regardless of weather conditions. This model enables a real-time look at changes and trends impacting infrastructure growth.
Crop Classification in California
Leveraging our database of industry leading high-resolution imagery, we built a model that first identifies field boundaries and then classifies which crops are growing within each field. With this optimized approach, field teams spend less time surveying ground data and more time focusing on business growth opportunities.
Wind Turbine Detection
Using high-resolution Airbus imagery, we built a computer vision model that can quickly identify all physical wind turbine assets worldwide in just a few hours. This solution automates analysis that would take a fleet of human analysts several months to complete.