10m Annual Land Use Land Cover (9-class)

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/io-lulc-dataset")

fs.listdir("s3://io-10m-annual-lulc")

Description:

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution for the years 2017 - 2021. Each year is generated from Impact Observatory’s deep learning AI land classification model using a massive training dataset of billions of human-labeled image pixels. The global maps were produced by applying this model to every Sentinel-2 scene, processing over 400,000 Earth observations per year. Leaders in governments, NGOs, finance and industry need trustworthy, actionable information about the changing world to understand opportunities, identify threats, and measure the impacts of actions. Many of the most useful applications of LULC maps require the ability to measure changes in land use and land cover over time. With a time-series of LULC maps, monitoring of deforestation, urban expansion, agricultural land conversion, and surface water scarcity all become possible. The algorithm generates LULC predictions for 9 classes globally. These classifications include Built, Crops, Trees, Water, Rangeland, Flooded Vegetation, Snow/Ice, Bare Ground, and Clouds.

Contact:

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution for the years 2017 - 2021. Each year is generated from Impact Observatory’s deep learning AI land classification model using a massive training dataset of billions of human-labeled image pixels. The global maps were produced by applying this model to every Sentinel-2 scene, processing over 400,000 Earth observations per year. Leaders in governments, NGOs, finance and industry need trustworthy, actionable information about the changing world to understand opportunities, identify threats, and measure the impacts of actions. Many of the most useful applications of LULC maps require the ability to measure changes in land use and land cover over time. With a time-series of LULC maps, monitoring of deforestation, urban expansion, agricultural land conversion, and surface water scarcity all become possible. The algorithm generates LULC predictions for 9 classes globally. These classifications include Built, Crops, Trees, Water, Rangeland, Flooded Vegetation, Snow/Ice, Bare Ground, and Clouds.

Update Frequency:

Annually, each January

Managed By:

https://www.impactobservatory.com/

Resources:

1. resource:
   • Description: 10m Annual Land Use Land Cover (9-class)
   • ARN: arn:aws:s3:::io-10m-annual-lulc
   • Region: us-west-2
   • Type: S3 Bucket

Tags:

aws-pds, earth observation, environmental, geospatial, satellite imagery, sustainability, stac, cog, land cover, land use, machine learning, mapping, planetary

Tools & Applications:

1. tools & applications:
   • Title: View the dataset on UN Biodiversity Lab Map Viewer
   • URL: https://map.unbiodiversitylab.org/earth?basemap=grayscale&coordinates=20,0,2&layers=10m-annual-land-use-land-cover-9-class-01_100
   • AuthorName: United Nations Development Programme

Publication:

1. publication:

   • Title: Global land use / land cover with Sentinel 2 and deep learning
   • URL: https://ieeexplore.ieee.org/document/9553499
   • AuthorName: K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby

2. publication:

   • Title: Mapping the world with unmatched frequency
   • URL: https://medium.com/impactobservatoryinc/mapping-the-world-with-unmatched-frequency-54a83527f138
   • AuthorName: Mark Hannel

3. publication:

   • Title: ‘Very Dire’: Devastated by Floods, Pakistan Faces Looming Food Crisis
   • URL: https://www.nytimes.com/2022/09/11/world/asia/pakistan-floods-food-crisis.html
   • AuthorName: New York Times

4. publication:

   • Title: These maps from satellite data show how much Earth has changed in only five years
   • URL: https://www.fastcompany.com/90729824/these-maps-from-satellite-data-show-how-much-earth-has-changed-in-only-five-years
   • AuthorName: Fast Company

5. publication:

   • Title: The world's most populated and greenest megacities (and how we found out)
   • URL: https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/worlds-greenest-megacities/
   • AuthorName: Esri