Our machine-learning models train and run on multiple types of satellite data
Synthetic Aperture Radar (SAR) Satellite
E.g. ALOS PALSAR, Sentinel-1
E.g. Landsat-7, Landsat-8, Sentinel-2
E.g. proprietary terrestrial and UAV lidar data
Unrivaled machine learning capabilities ensure accuracy
Sylvera uses machine learning (ML) and multiple types of satellite data to identify specific features of forests and land cover. We train proprietary ML models in specific biomes and geographies to produce accurate carbon estimates for different project types.
For example, when assessing forest growth in an afforestation, reforestation, and revegetation (ARR) project, we use ML to estimate the canopy height of trees in the project area (PA). To do this, we train a model to identify forest canopy height by feeding it tens of thousands of labeled data points. Then we run our models on the PA to estimate the canopy height. By running the model over the same area with data from multiple years, we can see changes over time in the forest area.
Every project receives an assessment and distinct rating
Our carbon credit ratings give buyers the confidence to invest in high-quality projects that deliver real climate impact.
Trusted carbon data for real climate action.
Sylvera helps organizations and governments get on track to net zero. To learn more about our products, contact us.