Sentinel-2 10m land use/land cover time series of the world. Produced by Impact Observatory and Esri. A brief summary of the item is not available. Add a brief summary about the item.
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Item created: Oct 18, 2022 Item updated: May 28, 2024 View count: 956,669
Description
The algorithm generates LULC predictions for nine classes, described in detail below.
The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.
Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84
Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)
Extent: Global
Source imagery: Sentinel-2 L2A
Cell Size: 10-meters
Type: Thematic
Attribution: Esri, Impact Observatory
Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth.
This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes.
Value | Name | Description |
1 | Water | Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains. |
2 | Trees | Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath). |
4 | Flooded vegetation | Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture. |
5 | Crops | Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land. |
7 | Built Area | Human made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt. |
8 | Bare ground | Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines. |
9 | Snow/Ice | Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. |
10 | Clouds | No land cover information due to persistent cloud cover. |
11 | Rangeland | Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants. |
Classification Process
These maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.
The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.
The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.
Citation
Karra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.
Acknowledgements
Training data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.
An in-depth description of the item is not available.
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Dashboard views: Desktop
Source: Image Service
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Dependent items in the recycle bin
Applicable: 2d
Size: 1 KB
ID: cfcb7609de5f478eb7666240902d4d3d
Image Count: 0
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Using tiles from a cache
Dynamically from data
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Tags
Sentinel-2, Land Cover, Land Use, land, use, cover, 10m, global, world, lulc, Time Series
Credits (Attribution)
No acknowledgements.Esri, Impact Observatory
Comments (32)
This land cover has symbolised high resolution of extraction, I am very grateful using for my projects and MSc thesis
pleas help me to get this data, thank you so much my Gmail is: souheilhydro@gmail.com
I also got the way how to download the map. Thank you very much for all these tasks.
This land cover map is very important. I am using it for my PhD dissertation works.
I am glad to be a member of the community for further analysis and observation regarding the conservation of our planet from global warming gratefully thanks for the community.