Lacuna Fund – Climate & Forests

Funding is available to organisations based in Africa, Latin America, South Asia and Southeast Asia and their partners elsewhere to develop training and evaluation datasets that will lead to better understanding of the relationship between climate and forests, and generate interventions that could mitigate the impacts of climate change.

Lacuna Fund began as a funder collaborative, but has evolved into a multi-stakeholder engagement supported by a range of development, philanthropic and research institutions. Collectively, they are committed to creating and mobilising labelled datasets that solve urgent local problems and lead to a step change in machine learning’s (ML) potential worldwide. Lacuna Fund supports the creation, expansion and maintenance of equitably labelled datasets that enable the robust application of ML tools of high social value in low- and middle-income contexts globally, whether through research, commercial innovation or improved public sector services.

The purpose of the Climate & Forests Request for Proposals (RFP) is to support organisations working at the intersection of artificial intelligence (AI) and social impact in their efforts to develop open and accessible datasets for ML applications that will help communities understand and address the relationship between climate change and forests in Africa, South and Southeast Asia, and Latin America. Particularly in low- and middle-income contexts, the effective use of ML is hampered by a lack of ground truth data. Filling in data gaps and increasing accessibility to ML technologies can help policymakers, researchers and local communities take advantage of ML tools to better understand future trends and inform action to mitigate and adapt to the impacts of a changing climate on forest ecosystems.

Proposals may include, but are not limited to:

• Collecting and/or annotating new data.
• Annotating or releasing existing data.
• Augmenting existing datasets to fill gaps in local ground truth data, decrease bias or increase the usability of data and technology related to climate and forests in low- and middle-income contexts.
• Improving prior datasets based on learnings to date.
• Linking and harmonising existing datasets.
• Maintaining/updating an existing high value dataset from one of the four target regions.
• Developing a baseline model(s) to ensure the quality of the funded dataset and/or to facilitate the use of the dataset for socially beneficial applications.

Datasets may include, but are not limited to:

• Land cover/land use change datasets with spatial signatures that improve understanding of the relationship between land cover/land use change and climate change.
• Afforestation and reforestation datasets that inform strategic restoration efforts to mitigate climate change.
• Datasets that show the value of non-extractive forest ecosystem services and products that could help Indigenous Peoples and local communities adapt to climate change and/or receive compensation for activities that preserve forest ecosystems and mitigate climate change.
• Contextualising Indigenous knowledge of biodiversity into labelled training data to highlight how climate change is impacting biodiversity in forest ecosystems.
• Datasets that correlate supply chain interventions with forest loss and climate change.
• Datasets that link environmental/nature crimes to climate change.
• Datasets that can be used to show the impact of deforestation on wildfires and climate change.
• Carbon sequestration datasets utilising biomass estimation or species dominance.
• Invasive species datasets that link with climate change data.
• Datasets showing how climate change affects animal species movements.
• Datasets linking changes in forest health to climate change.

It is anticipated that six to nine projects will be funded in total, from which at least one project will be funded in each of the four target regions.

This call for proposals is supported by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ – German Society for International Collaboration).