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David Roy
Michigan State UniversityMeeting roles in:
Self-supervised explainable deep learning to forecast future fire at monthly, seasonal, and annual, landscape scale
Humid tropical forest foliar height diversity (FHD) increases after deforestation with stand age – a comprehensive Democratic Republic of Congo multi-decadal study using the NASA GEDI FHD and European Union Tropical Moist Forest Cover Loss products
HLS-GPT: A Generative Pretrained Transformer (GPT) Model for Accurate Harmonized Landsat and Sentinel-2 (HLS) Annual Reflectance Time Series Reconstruction
Pathfinding the steps to ensure global analysis ready consistent reflectance from the Landsat MSS to Landsat Next era
Combining GEDI and Landsat-based satellite forest cover loss data to improve understanding of Democratic Republic of Congo humid tropical forest regrowth and carbon sequestration
Conterminous United States multi-annual crop field boundary delineation
NASA Acres and the Essential Agriculture Variables: Contributions and Collaborations toward a Globally-Consistent, Site-Relevant Agricultural Knowledge System
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