Modeling

DeepEarth synthesizes the natural and applied sciences into predictions about how any landscape will behave and how it can be improved.

  • AI world model for ecosystems DeepEarth integrates species interactions, climate variables, soil chemistry, hydrology, fire behavior, and pollinator dynamics into a unified model of ecosystem function across space and time.
  • Environmental simulation Forecast vegetation dynamics, species distributions, fire propagation, flood susceptibility, and climate response for any site. Trained on global environmental data. Open source.
  • Ecological design optimization Given a site's 3D digital twin, DeepEarth generates planting recommendations that optimize simultaneously for biodiversity, climate resilience, beauty, and financial return. Every recommendation grounded in ecological science.
  • Hazard modeling Property-level fire and flood risk assessment from geospatial and 3D data. Ecological mitigation strategies that reduce exposure while improving environmental outcomes. Risk modeling at portfolio scale for insurers.
Research

DeepEarth at UC Berkeley

DeepEarth is being researched with the Quantitative Ecosystem Dynamics lab at UC Berkeley (Dr. Trevor Keenan) and through the NSF Institute for Geospatial Understanding through an Integrative Discovery Environment.

Interested? lance@ecological.dev