Dynamic World V1

Dataset Availability

2015-06-27T00:00:00Z–2024-11-08T17:01:38Z

Dataset Provider

World Resources Institute Google

Earth Engine Snippet

ee.ImageCollection("GOOGLE/DYNAMICWORLD/V1")

Tags

global google landcover landuse nrt sentinel2-derived

Description

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 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:

ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT')

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.

Bands

Resolution
10 meters

Bands

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.

Citations

Citations:

• Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). doi:10.1038/s41597-022-01307-4

DOIs

• https://doi.org/10.1038/s41597-022-01307-4

Explore with Earth Engine

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Code Editor (JavaScript)

// Construct a collection of corresponding Dynamic World and Sentinel-2 for
// inspection. Filter by region and date.
var START = ee.Date('2021-04-02');
var END = START.advance(1, 'day');

var colFilter = ee.Filter.and(
    ee.Filter.bounds(ee.Geometry.Point(20.6729, 52.4305)),
    ee.Filter.date(START, END));

var dwCol = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1').filter(colFilter);
var s2Col = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

// Link DW and S2 source images.
var linkedCol = dwCol.linkCollection(s2Col, s2Col.first().bandNames());

// Get example DW image with linked S2 image.
var linkedImg = ee.Image(linkedCol.first());

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

var VIS_PALETTE = [
    '419bdf', '397d49', '88b053', '7a87c6', 'e49635', 'dfc35a', 'c4281b',
    'a59b8f', 'b39fe1'];

// Create an RGB image of the label (most likely class) on [0, 1].
var dwRgb = linkedImg
    .select('label')
    .visualize({min: 0, max: 8, palette: VIS_PALETTE})
    .divide(255);

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

// Create a hillshade of the most likely class probability on [0, 1];
var top1ProbHillshade =
    ee.Terrain.hillshade(top1Prob.multiply(100))
    .divide(255);

// 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);
Map.addLayer(
    linkedImg, {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']}, 'Sentinel-2 L1C');
Map.addLayer(
    dwRgbHillshade, {min: 0, max: 0.65}, 'Dynamic World V1 - label hillshade');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Construct a collection of corresponding Dynamic World and Sentinel-2 for
# inspection. Filter by region and date.
START = ee.Date('2021-04-02')
END = START.advance(1, 'day')

col_filter = ee.Filter.And(
    ee.Filter.bounds(ee.Geometry.Point(20.6729, 52.4305)),
    ee.Filter.date(START, END),
)

dw_col = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1').filter(col_filter)
s2_col = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

# Link DW and S2 source images.
linked_col = dw_col.linkCollection(s2_col, s2_col.first().bandNames());

# Get example DW image with linked S2 image.
linked_image = ee.Image(linked_col.first())

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

VIS_PALETTE = [
    '419bdf',
    '397d49',
    '88b053',
    '7a87c6',
    'e49635',
    'dfc35a',
    'c4281b',
    'a59b8f',
    'b39fe1',
]

# Create an RGB image of the label (most likely class) on [0, 1].
dw_rgb = (
    linked_image.select('label')
    .visualize(min=0, max=8, palette=VIS_PALETTE)
    .divide(255)
)

# Get the most likely class probability.
top1_prob = linked_image.select(CLASS_NAMES).reduce(ee.Reducer.max())

# Create a hillshade of the most likely class probability on [0, 1]
top1_prob_hillshade = ee.Terrain.hillshade(top1_prob.multiply(100)).divide(255)

# Combine the RGB image with the hillshade.
dw_rgb_hillshade = dw_rgb.multiply(top1_prob_hillshade)

# Display the Dynamic World visualization with the source Sentinel-2 image.
m = geemap.Map()
m.set_center(20.6729, 52.4305, 12)
m.add_layer(
    linked_image,
    {'min': 0, 'max': 3000, 'bands': ['B4', 'B3', 'B2']},
    'Sentinel-2 L1C',
)
m.add_layer(
    dw_rgb_hillshade,
    {'min': 0, 'max': 0.65},
    'Dynamic World V1 - label hillshade',
)
m

Open in Code Editor

Dynamic World V1

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 …

GOOGLE/DYNAMICWORLD/V1, global,google,landcover,landuse,nrt,sentinel2-derived

2015-06-27T00:00:00Z/2024-11-08T17:01:38Z

Google Earth Engine

https://developers.google.com/earth-engine/datasets

• https://doi.org/10.1038/s41597-022-01307-4
• https://doi.org/10.1038/s41597-022-01307-4