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This dataset is part of a Publisher Catalog, and not managed by Google Earth Engine.
Contact forestdatapartnership@googlegroups.com
for bugs or view more datasets
from the Forest Data Partnership Catalog. Learn more about Publisher datasets.
Note: This dataset is not yet peer-reviewed. Please see the GitHub
README associated with this model for more information.
This image collection provides per-pixel probability that the underlying
area is occupied by rubber trees.
The probability estimates are provided at 10 meter resolution, and have
been generated by a machine learning model. This dataset corresponds to
2020 and 2023 output from model 2024a in the
Forest Data Partnership repo
on Github.
The primary purpose of this image collection is to support the mission of
the Forest Data Partnership
which aims to halt and reverse forest loss from commodity production
by collaboratively improving global monitoring, supply chain tracking,
and restoration.
Note that this dataset has separate terms of use for commercial users of
Earth Engine. Please see "Terms of Use" tab for details.
This community data product is meant to evolve over time, as more data
becomes available from the community and the model used to produce the
maps continuously improves. To provide map-based feedback on this
collection, please see our
Collect Earth Online instance
and follow
these instructions.
If you would like to provide general feedback or additional datasets to
improve these layers, please reach out through
this form.
Limitations: Model output is limited to selected countries as calendar
year composites for 2020 and 2023. Not all regions of the output are
represented by training data. Accuracy is reported in aggregate, is based
on a notional threshold, and will vary geographically and with user chosen
thresholds. Sensor artifacts based on data availability, cross-track
nonuniformity, or cloudiness may be visually apparent in output
probabilities and result in classification errors at some thresholds.
Geographic scope: SE Asia (Thailand, Indonesia, Vietnam, Malaysia,
Philippines, Hainan Island), Africa (Côte d'Ivoire, Ghana).
Bands
Pixel Size 10 meters
Bands
Name
Min
Max
Pixel Size
Description
probability
0
1
meters
Probability that the pixel includes rubber trees for the given year.
Terms of Use
Terms of Use
For non-commercial users of Earth Engine, use of the dataset is subject to
CC-BY 4.0 NC license and requires the following attribution:
"Produced by Google for the Forest Data Partnership".
Note: This dataset is not yet peer-reviewed. Please see the GitHub README associated with this model for more information. This image collection provides per-pixel probability that the underlying area is occupied by rubber trees. The probability estimates are provided at 10 meter resolution, and have been generated by a machine …
[[["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"]],[],[[["\u003cp\u003eThis dataset provides the probability of rubber tree presence at 10-meter resolution for 2020 and 2023 using a machine learning model.\u003c/p\u003e\n"],["\u003cp\u003eIt covers select regions in Southeast Asia and Africa and is intended to support the Forest Data Partnership's mission to combat deforestation.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset has separate commercial and non-commercial terms of use, with commercial access granted on a case-by-case basis.\u003c/p\u003e\n"],["\u003cp\u003eUsers should be aware of the limitations, including the geographic scope, potential sensor artifacts, and the dataset's pre-review status.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is expected to improve over time with additional data and model refinements.\u003c/p\u003e\n"]]],["This dataset, from the Forest Data Partnership, offers per-pixel probability of rubber tree presence at a 10-meter resolution, derived from a machine learning model (model 2024a). The data covers selected countries in SE Asia and Africa for 2020 and 2023. It's accessible through Google Earth Engine and is intended to support global monitoring, supply chain tracking, and restoration efforts. Commercial use requires separate access. Users can contact forestdatapartnership@googlegroups.com for bugs and data improvement.\n"],null,["# Rubber Tree Probability model 2024a [deprecated]\n\n**Caution:** This dataset has been superseded by [projects/forestdatapartnership/assets/rubber/model_2025a](/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2025a). \ninfo\n\n\nThis dataset is part of a Publisher Catalog, and not managed by Google Earth Engine.\n\nContact forestdatapartnership@googlegroups.com\n\nfor bugs or [view more datasets](https://developers.google.com/earth-engine/datasets/publisher/forestdatapartnership)\nfrom the Forest Data Partnership Catalog. [Learn more about Publisher datasets](/earth-engine/datasets/publisher). \n[](https://forestdatapartnership.org) \n\nCatalog Owner\n: Forest Data Partnership\n\nDataset Availability\n: 2020-01-01T00:00:00Z--2023-12-31T23:59:59Z\n\nDataset Provider\n:\n\n\n [Produced by Google for the Forest Data Partnership](https://www.forestdatapartnership.org/)\n\nTags\n:\n agriculture \n biodiversity \n conservation \n crop \n eudr \n forestdatapartnership \n landuse \n plantation \n pre-review \n publisher-dataset \nrubber \n\n#### Description\n\n**Note: This dataset is not yet peer-reviewed. Please see the GitHub\nREADME associated with this model for more information.**\n\nThis image collection provides per-pixel probability that the underlying\narea is occupied by rubber trees.\n\nThe probability estimates are provided at 10 meter resolution, and have\nbeen generated by a machine learning model. This dataset corresponds to\n2020 and 2023 output from model 2024a in the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main/models/rubber)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/)\nwhich aims to halt and reverse forest loss from commodity production\nby collaboratively improving global monitoring, supply chain tracking,\nand restoration.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine. Please see \"Terms of Use\" tab for details.**\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the\nmaps continuously improves. To provide map-based feedback on this\ncollection, please see our\n[Collect Earth Online instance](https://app.collect.earth/collection?projectId=50862)\nand follow\n[these instructions](https://collect-earth-online-doc.readthedocs.io/en/latest/collection/simplified.html).\n\nIf you would like to provide general feedback or additional datasets to\nimprove these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\n**Limitations**: Model output is limited to selected countries as calendar\nyear composites for 2020 and 2023. Not all regions of the output are\nrepresented by training data. Accuracy is reported in aggregate, is based\non a notional threshold, and will vary geographically and with user chosen\nthresholds. Sensor artifacts based on data availability, cross-track\nnonuniformity, or cloudiness may be visually apparent in output\nprobabilities and result in classification errors at some thresholds.\nGeographic scope: SE Asia (Thailand, Indonesia, Vietnam, Malaysia,\nPhilippines, Hainan Island), Africa (Côte d'Ivoire, Ghana).\n\n### Bands\n\n\n**Pixel Size**\n\n10 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|---------------|-----|-----|------------|----------------------------------------------------------------------|\n| `probability` | 0 | 1 | meters | Probability that the pixel includes rubber trees for the given year. |\n\n### Terms of Use\n\n**Terms of Use**\n\nFor non-commercial users of Earth Engine, use of the dataset is subject to\nCC-BY 4.0 NC license and requires the following attribution:\n\"Produced by Google for the Forest Data Partnership\".\n\nFor commercial use of the dataset you may request access using\n[this form](https://docs.google.com/forms/d/e/1FAIpQLSe7L3eh6t2JIPqEtAQwXwY7ZmW52v8W5vrIi4QN_XYgTNJZLw/viewform).\nAccess will be granted or denied on a case by case basis. Commercial use\nof the dataset is subject to the [Forest Data Partnership Datasets\nCommercial Terms of Use](https://services.google.com/fh/files/misc/forest_data_partnership_datasets_commerical_terms_of_use.pdf).\n\nContains modified Copernicus Sentinel data \\[2015-present\\].\nSee the [Sentinel Data Legal Notice](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice).\n\n### Citations\n\nCitations:\n\n- [Forest Data Partnership](https://github.com/google/forest-data-partnership/blob/main/models/rubber/README.md)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nMap.setCenter(106.48584, 11.17099, 11);\n\nvar collection = ee.ImageCollection(\n 'projects/forestdatapartnership/assets/rubber/model_2024a');\n\nvar r2020 = collection.filterDate('2020-01-01', '2020-12-31').mosaic();\nMap.addLayer(\n r2020.selfMask(), {min: 0.5, max: 1, palette: 'white,blue'}, 'rubber 2020');\n\nvar r2023 = collection.filterDate('2023-01-01', '2023-12-31').mosaic();\nMap.addLayer(\n r2023.selfMask(), {min: 0.5, max: 1, palette: 'white,green'},\n 'rubber 2023');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a) \n[Rubber Tree Probability model 2024a \\[deprecated\\]](/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2024a) \nNote: This dataset is not yet peer-reviewed. Please see the GitHub README associated with this model for more information. This image collection provides per-pixel probability that the underlying area is occupied by rubber trees. The probability estimates are provided at 10 meter resolution, and have been generated by a machine ... \nprojects/forestdatapartnership/assets/rubber/model_2024a, agriculture,biodiversity,conservation,crop,eudr,forestdatapartnership,landuse,plantation,pre-review,publisher-dataset \n2020-01-01T00:00:00Z/2023-12-31T23:59:59Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://www.forestdatapartnership.org/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2024a)"]]
