Land Cover 9-Class
Description
This dataset classifies land cover at 10 m spatial resolution, with global spatial coverage annually from 2017 onwards. Pixels are assigned one of 9 land cover classes.
a4bb9aea…Usage notes
New time points are delivered in January for the previous year. Data are delivered as annual composites of predictions made on multiple Sentinel-2 observations.
Variables
land_cover_class
Description
Index of the predicted land cover class. Predictions are made using a deep learning segmentation model trained to predict > 5 billion manually annotated land cover classifications from Sentinel-2 imagery.
Usage notes
Grass and Shrub classes are merged into a single Rangeland class (index 11 ). Overall accuracy is 76% compared to a majority consensus. Lowest accuracy is for Flooded Vegetation (57%) and Rangeland (58%). Highest accuracy is for Crops (90%) and Water (87%).
| Index | Name | Description |
|---|---|---|
| 1 | Water | Water with little to no accompanying vegetation, rock, outcrops, or infrastructure. May not include sporadic or ephemeral water bodies. |
| 2 | Trees | A clustering of tall dense vegetation with closed or dense canopy. |
| 4 | Flooded Vegetation | Any vegetation mixed with water throughout most of the year, including seasonally flooded areas. |
| 5 | Crops | Cultivated cereals, grasses, or other non-tree crops. |
| 7 | Built Area | Human-made structures, including rail/road networks, buildings, and large impervious surfaces (e.g. parking lots). |
| 8 | Bare Ground | Rock, soil, or sand with little to no vegetation for the entire year. |
| 9 | Snow/Ice | Homogenous areas of permanent snow or ice. Typically only found in mountainous and high latitude regions. |
| 10 | Clouds | No land cover information due to persistent cloud cover. |
| 11 | Rangeland | Low vegetation with little to no taller vegetation. Includes patchy landscapes of grass, shrubs, scrub, and bare ground. |
Pricing
This dataset has no data acquisition cost.
Resources
Provider
Impact Observatory
www.impactobservatory.comImpact Observatory is a nature data analytics company based in Washington DC. Impact Observatory uses AI-based approaches to build land cover products that inform environmental analysis for governments, industries, and markets.