Research theme

Agentic land stewardship

AI agents that integrate diverse data about land, climate, and agriculture, and input from human collaborators, to provide broad perspective and expertise on managing functioning ecosystems.

Agentic stewardship aims to close the loop: sensing → reasoning → action. An agent detects a condition such as a predator, drought stress or fire weather, reasons over the property’s context, and then acts: it might dispatch a drone or alert a person. Humans stay in the loop, and every automated action has limits.

Projects

All projects
A quadcopter drone sits on an orange landing pad on a gravel ranch road beside dry grass, with two people beside an equipment case farther down the road.

Active Underway

Digital twins of landscapes for active management

Living, measured models of DeepForest's sites, built from drone photos and 3D models, LiDAR, satellite time series, AI-managed remote sensing and dated field records, and kept for years so active management (thinning, fuel breaks, biochar, seeding and grazing) can be planned, tested and tracked: where the work was done, how the land responds, and the carbon it holds, as estimates.

San Domingo · Woodland Hilltop — Generational continuity & open datasets, Agentic land stewardship, AI-powered land monitoring