The dataset is a 10m global industrial and smallholder oil palm map for 2019. It covers areas where oil palm plantations were detected. The classified images are the output of a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. See article for additional …
This image collection provides per-pixel probability that the underlying area is in oil palm cultivation. These probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. Labeled examples of oil palm plantations were supplied by community contributors to …
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["The datasets provide recent maps and probabilities of global oil palm plantations, aiding in monitoring and understanding land use related to this crop."],["They utilize satellite imagery and machine learning to identify and classify oil palm cultivation areas, offering valuable insights for biodiversity and conservation efforts."],["One dataset provides a 2019 global map of industrial and smallholder oil palm plantations at 10m resolution, while another offers per-pixel probabilities of oil palm presence for 2024 at the same resolution."],["These resources are useful for researchers, policymakers, and stakeholders concerned with the environmental and economic impacts of oil palm cultivation."]]],[]]
