AI for the Living Planet

Learning goal

Understand how platforms like iNaturalist use AI to turn millions of citizen observations into scientific-grade biodiversity data — and how you can contribute.

Real scenario

A schoolteacher organises a class biodiversity survey of their local park. A hiking club wants to contribute to species monitoring during their walks. An environmental NGO needs to survey a 200-hectare site with limited staff.

What you gain

You understand the principles behind AI-assisted species identification, know how to use and contribute to citizen science platforms, and can explain their scientific value to funders and partners.

Learning goal

Understand how satellite imagery, acoustic sensors, and AI combine to monitor deforestation, illegal fishing, and ecosystem change — and the organisations driving this work.

Real scenario

An environmental lawyer needs to document illegal logging in a protected zone. A marine conservation NGO wants to understand which AI tools are monitoring fishing activity in the area they cover. A journalist is investigating a company's environmental compliance claims.

What you gain

You know the key tools and platforms for AI-assisted environmental monitoring, understand their capabilities and limitations, and can use this knowledge to strengthen environmental protection and advocacy work.

Learning goal

Identify and apply the AI tools most useful to environmental organisations across their core functions.

Real scenario

A conservation NGO needs to process 5,000 camera trap images to estimate a wolf population. An advocacy organisation wants to use AI to monitor media coverage of a specific policy. A small environmental foundation is overwhelmed by grant applications and needs a screening process.

What you gain

You have a practical map of AI tools applicable to environmental work, know how to evaluate them for your context, and have a clearer picture of where AI can multiply the impact of a small team.

Learning goal

Understand the basics of AI-assisted OSINT and how environmental investigators use satellite data, image analysis, and public records to build evidence.

Real scenario

A journalist suspects that a company is exceeding its licensed extraction volumes. A local residents' group wants to document changes in water quality upstream of an industrial facility. An environmental lawyer needs to establish a timeline of land use changes.

What you gain

You know the key open-source tools and datasets used in environmental investigations, understand the basics of satellite image analysis, and can begin to apply these methods in your own advocacy or reporting work.

Learning goal

Understand the real resource costs of AI use and develop a practice of intentional, proportionate engagement.

Real scenario

A marketing team generates 200 image variations for a campaign, selecting 3. A researcher runs repeated large-model queries to refine a summary they could have written themselves in the same time. An organisation subscribes to multiple AI services they use inconsistently.

What you gain

You can estimate the rough environmental cost of different types of AI use, distinguish productive from wasteful use patterns, and make more intentional choices about when AI is the right tool — and when it is not.