Mapping, monitoring, and understanding the living world.
We study how AI, Earth observation, and open data can make ecosystems more legible — and how that legibility can serve conservation, food security, and the communities and institutions working closest to the ground.
Mapping nature
Earth observation across oceans, rivers, forests, soils, atmosphere, and the view from orbit — assembled into open, comparable layers of the living world.
Monitoring ecosystems in real time
Real-time signals, time-series, indicators, and change detection — so loss, recovery, and trend become legible at the cadence decisions actually need.
Analysis, prediction, and autonomous systems
Multimodal models, ecological digital twins, and agentic workflows — careful AI that knows what it does not know, and acts only when it should.
Valuing nature, biodiversity, and community work
How ecological evidence becomes a number a system can act on — for biodiversity, ecosystem services, and the communities doing the work on the ground.
Conservation, water, and food security
Translating measurement into decisions — for protected areas, restoration, fisheries, watersheds, agriculture, and the people working in each.
Efficient and sovereign computation
Smaller-footprint AI and edge-first, decentralized infrastructure — so the systems that read the planet do not cost the planet to run, and the data stays close to the people who produced it.
Open data and open collaboration
Open methods, open datasets, and shared infrastructure — built with universities, researchers, and the communities closest to the ground.
Governance, ethics, and data rights
How communities keep ownership of what they observe, how AI decisions are scrutinised, and how findings stay auditable — so the systems that read the living world remain accountable to the people who live in it.