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Land and Carbon Lab, founded by World Resources Institute and the Bezos Earth Fund in 2021, develops breakthroughs in geospatial monitoring to help governments, businesses and communities power solutions for sustainable landscapes. Global Forest Watch, established in 2014 by a consortium of partners led by the World Resources Institute, is a forest monitoring initiative that provides open access to data about the current status of forests and recent forest change.
This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected …
This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected …
This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected …
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[],null,["# Land & Carbon Lab\n\nLand and Carbon Lab, founded by World Resources Institute and the Bezos Earth Fund in 2021, develops breakthroughs in geospatial monitoring to help governments, businesses and communities power solutions for sustainable landscapes. Global Forest Watch, established in 2014 by a consortium of partners led by the World Resources Institute, is a forest monitoring initiative that provides open access to data about the current status of forests and recent forest change. \n[](https://landcarbonlab.org/) \n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2022 v1.0](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2023 v1.1](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |\n\n-\n\n |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2024 v1.2](/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024) |\n | This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [forest-biomass](/earth-engine/datasets/tags/forest-biomass) [google](/earth-engine/datasets/tags/google) [landandcarbon](/earth-engine/datasets/tags/landandcarbon) |"]]
