Keys, PatrickBarnes, ElizabethCarter, Neil2020-11-022020-11-022020https://hdl.handle.net/10217/216207http://dx.doi.org/10.25675/10217/216207The data are a set of gridded raster maps corresponding to the Human Footprint Index (HFI) for the years 2000 and 2019. The data presented here are based on two other openly accessible datasets: 1. The human footprint index data by Williams et al. https://github.com/scabecks/humanfootprint_2000-2013; 2. Global Forest Cover data https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html.School of Global Environmental SustainabilityDepartment of Atmospheric ScienceFundamental to the success of sustainable development is a foundation of intact ecosystems. While the United Nations Sustainable Development Goal 15, “Life on Land”, seeks to protect biodiversity in terrestrial ecosystems, accelerating human-driven changes across the Earth system are undermining efforts to preserve biodiversity. Understanding this tension has never been more urgent and requires tools that reveal pathways for development that also support biodiversity. Here we introduce a near-present, global-scale machine learning-based human footprint index which is capable of routine update. By comparing global changes in the machine learning human footprint index between 2000 and 2019 to national-scale biodiversity metrics for Goal 15, we find that some countries are experiencing increases in their human footprint while biodiversity metrics are improving as well. We further examine development and policy dynamics to uncover enabling mechanisms for balancing increased human pressure with biodiversity gains. This has immediate policy relevance, since the majority of countries globally are not on track to achieve Goal 15 by the declared deadline of 2030. Moving forward, the machine learning human footprint index can be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.PDFHDF5engbiodiversity conservationsustainable developmenthuman impactshuman footprint indexmachine learningneural networkremote sensingDatasetThe material is open access and distributed under the terms and conditions of the Creative Commons Attribution International License (https://creativecommons.org/licenses/by/4.0/).