Operated and managed by Microsoft Azure, AI for Earth cloud-hosted software applies machine learning to remote sensing data to support forest inventory management, with the goals of reducing manual fieldwork and improving data quality and precision. Machine learning classification models are applied to high-resolution satellite imagery combined with pre-existing field data to create forest maps with unprecedented fine-grained resolution; their first initiative created 15-meter resolution forest maps covering the entire continental United States. Maps and data produced by SilviaTerra can help determine the impact that climate change has on land practices for supporting ecosystem health and biodiversity, and creating a more sustainable timber harvest. This is an innovative service for conservationists, governments, and landowners. By combining AI, cloud computing and machine learning, SilviaTerra allows stakeholders to quickly assess forest inventory maps and develop sustainable management plans at the fraction of the time and cost of traditional manual surveys. 


Bourgoin, C., Betbeder, J., Couteron, P., Blanc, L., Baghdadi, N., Reymondin, L., Läderach, P., Sist, P. and Gond, V., 2018. Assessing degraded forest structures using UAV and SAR remote sensing data. Association for Forest Spatial Analysis Technologies.


Artificial Intelligence, Climate Change, Data, Ecological Monitoring, Industry/Natural Commodities, Internet of Things