The Nature of AI: Solving the Planet's Data Gap with Drew Purves

Further reading:
• Natural forests of the world: paper (sciety.org/articles/activity/10.31223/x5zx6p), data and benchmarks (github.com/google-deepmind/forest_typology)
• Forest loss drivers: paper, (iopscience.iop.org/article/10.1088/1748-9326/a…) summary from WRI (wri.org/insights/forest-loss-drivers-data-trends), and blog from GFW (globalforestwatch.org/blog/data-and-tools/new-…)
• Forest loss drivers code: Google Earth Engine (developers.google.com/earth-engine/datasets/ca…); at WRI (datasets.wri.org/datasets/dominant-drivers-of-…); at GFW (globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI…); or Zenodo (zenodo.org/records/15366671).
• Deep learning based remote sensing (open source): Jeo (github.com/google-deepmind/jeo), GeeFlow (github.com/google-deepmind/geeflow)
• Species mapping paper: Arxiv (arxiv.org/abs/2503.11900)
• Google resources: Google Earth Engine (earthengine.google.com). Agri with Google (agri.withgoogle.com), wildlife cameras (blog.google/outreach-initiatives/entrepreneurs…)
• Perch: code, (github.com/google-research/perch) paper (nature.com/articles/s41598-023-49989-z.epdf)
• Perch x coral reefs: Blog (blog.google/outreach-initiatives/arts-culture/…)
• Agile Modelling: paper (arxiv.org/abs/2505.03071), code (github.com/google-research/perch-hoplite)
• DolphinGemma: blog.google/technology/ai/dolphingemma

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