Mapping rainforest biodiversity for XPRIZE teams
We built sandbox environments with hard-to-access data for upcoming prizes
The story
The XPRIZE team wanted to populate their Data Collaborative platform with ML tools and data insights for their Rainforest Challenge – but lacked key data science and ML expertise.
Think-it’s role
We thought of the competing teams as clients--how can we democratize access to the world’s most important and hard-to-get data to make it easy for teams to unleash innovation. We used open-source data sets to analyze trends in rainforest biomass and CO2 emissions, forecast biodiversity metrics with time series modeling, and train Machine Learning models to predict and enable opportunities for autonomous data-gathering.
Why it mattered
These ML sandboxes set up competing teams for success in mapping rainforest ecosystems with autonomous data-gathering devices – by arming them ahead of time with insights about forest health and biodiversity from an equitable starting point.
Tech stack
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