Project Description:

GainForest is a groundbreaking initiative aimed at creating an open and transparent planetary computer for biodiversity and climate action. The project tackles the problem of Monitoring, Reporting, and Verification (MRV), which is crucial for carbon ratings and informed decision-making. Current major players in the field, such as Pachama and Sylvera, operate in black boxes and do not open or audit their algorithms. To address this issue, GainForest is developing an open model hub of AI algorithms for nature, including carbon counting and biodiversity estimation. This approach leverages citizen science and Decentralized Science (DeSci) to reward local data collectors and model developers.

Our roadmap consists of the following steps with their respective timelines:

Building a model hub on Hugging Face (Until Q3 2023): This hub will collect and advance open models, such as MegaDetector and Tree Delineation. More information can be found at https://huggingface.co/gainforest.

Collaborating with the Bacalhau Team (Q2 2023): We will provide decentralized compute and storage for all open models, enabling reproducible and auditable computation pipelines. All data schemas will be shared on https://CO2.storage to allow interoperability.

Participatory science with AI Chat Bots (June 2023): These chatbots will engage citizen science and local community participation, fostering a more inclusive approach to data collection and model development. A small demo can be found in our Discord: https://discord.com/invite/SsQhjPDasg.

Competing in the XPRIZE Rainforest Semifinals (June 2023): GainForest is among the last 15 teams in this competition, testing our approach at a highly competitive level with global media attention: https://www.biodivx.org/.

Integrating data and models for conservation projects (Q3 2023): All data and models will be freely available and deeply integrated for all conservation projects on GainForest (https://maps.gainforest.app), empowering NGOs and small conservation organizations to utilize their collected on-the-ground data, such as tree images and wildlife cams, without the need for AI expertise.