Forests play a crucial role in maintaining the earth's climate, economy, and biodiversity, yet they continue to be lost at an alarming rate, with 6.7 million hectares of tropical forest disappearing last year alone. Traditionally, satellite data has been used to measure this loss, but a new initiative called "ForestCast" aims to predict future deforestation risks using deep learning models. This approach utilizes satellite data to forecast deforestation risk, offering a more consistent and up-to-date method compared to previous models that relied on outdated input maps. By releasing a public benchmark dataset, the initiative encourages further development and application of these predictive models, potentially transforming forest conservation efforts. This matters because accurately predicting deforestation risk can help implement proactive conservation strategies, ultimately preserving vital ecosystems and combating climate change.
Google DeepMind and Google Research, in collaboration with the World Resources Institute (WRI) and the International Institute for Applied Systems Analysis (IIASA), are leveraging AI technology to distinguish between natural forests and other types of tree cover. This initiative aims to support the creation of deforestation-free supply chains by providing more accurate data on forest cover. The project involves a diverse group of experts and early map reviewers from various organizations, ensuring the development of reliable tools for environmental conservation. By improving the precision of forest mapping, this work is crucial for sustainable resource management and combating deforestation globally.