Dynamic World V1

Dataset Availability
2015-06-23T00:00:00Z - 2022-06-26T00:00:00
Dataset Provider
Earth Engine Snippet
global google landcover landuse nrt sentinel2-derived


Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes.

Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for images for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform.

Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g:


has a matching Dynamic World image named: ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT').

All probability bands except the "label" band collectively sum to 1.

To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series.

Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 "probabilities" for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon.

To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated "probability" of the top-1 prediction.


10 meters


Name Min Max Description
water 0 1

Estimated probability of complete coverage by water

trees 0 1

Estimated probability of complete coverage by trees

grass 0 1

Estimated probability of complete coverage by grass

flooded_vegetation 0 1

Estimated probability of complete coverage by flooded vegetation

crops 0 1

Estimated probability of complete coverage by crops

shrub_and_scrub 0 1

Estimated probability of complete coverage by shrub and scrub

built 0 1

Estimated probability of complete coverage by built

bare 0 1

Estimated probability of complete coverage by bare

snow_and_ice 0 1

Estimated probability of complete coverage by snow and ice

label 0 8

Index of the band with the highest estimated probability

label Class Table

Value Color Description
0 419BDF water
1 397D49 trees
2 88B053 grass
3 7A87C6 flooded_vegetation
4 E49635 crops
5 DFC35A shrub_and_scrub
6 C4281B built
7 A59B8F bare
8 B39FE1 snow_and_ice

Image Properties

Image Properties

Name Type Description
dynamicworld_algorithm_version STRING

The version string uniquely identifying the Dynamic World model and inference process used to produce the image.

qa_algorithm_version STRING

The version string uniquely identifying the cloud masking process used to produce the image.

Terms of Use

Terms of Use

This dataset is licensed under CC-BY 4.0 and requires the following attribution: "This dataset is produced for the Dynamic World Project by Google in partnership with National Geographic Society and the World Resources Institute."

Contains modified Copernicus Sentinel data [2015-present]. See the Sentinel Data Legal Notice.


  • In press at Nature Scientific Data

Explore in Earth Engine

// Construct a collection of corresponding Dynamic World and Sentinel-2 for
// inspection. Filter the DW and S2 collections by region and date.
var COL_FILTER = ee.Filter.and(
    ee.Filter.bounds(ee.Geometry.Point(20.6729, 52.4305)),
    ee.Filter.date('2021-04-02', '2021-04-03'));

var dwCol = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1').filter(COL_FILTER);
var s2Col = ee.ImageCollection('COPERNICUS/S2').filter(COL_FILTER);

// Join corresponding DW and S2 images (by system:index).
var DwS2Col = ee.Join.saveFirst('s2_img').apply(dwCol, s2Col,
    ee.Filter.equals({leftField: 'system:index', rightField: 'system:index'}));

// Extract an example DW image and its source S2 image.
var dwImage = ee.Image(DwS2Col.first());
var s2Image = ee.Image(dwImage.get('s2_img'));

// Create a visualization that blends DW class label with probability.
// Define list pairs of DW LULC label and color.
    'water', 'trees', 'grass', 'flooded_vegetation', 'crops',
    'shrub_and_scrub', 'built', 'bare', 'snow_and_ice'];

    '419BDF', '397D49', '88B053', '7A87C6',
    'E49635', 'DFC35A', 'C4281B', 'A59B8F',

// Create an RGB image of the label (most likely class) on [0, 1].
var dwRgb = dwImage
    .visualize({min: 0, max: 8, palette: VIS_PALETTE})

// Get the most likely class probability.
var top1Prob = dwImage.select(CLASS_NAMES).reduce(ee.Reducer.max());

// Create a hillshade of the most likely class probability on [0, 1];
var top1ProbHillshade =

// Combine the RGB image with the hillshade.
var dwRgbHillshade = dwRgb.multiply(top1ProbHillshade);

// Display the Dynamic World visualization with the source Sentinel-2 image.
Map.setCenter(20.6729, 52.4305, 12);
    {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']},
    'Sentinel-2 L1C');
    {min: 0, max: 0.65},
    'Dynamic World');