Land Use Land Cover (LULC) Maps on Demand for 2018-2022
Impact Observatory's LULC Maps on Demand service is available for any area of interest over user-specified time periods, beginning in 2018, and including the most recently available Sentinel-2 data. Impact Observatory uses a unique machine learning approach to classify land use and land cover (LULC) categories globally using Sentinel-2 imagery. Results have an average accuracy of 85% (compared to human expert labels).
The LULC Map on Demand provides users with a custom map of land use/land cover for a user-specified area of interest and time period (2018-2022). The map is derived from ESA Sentinel-2 imagery at 10m resolution. It is a composite of LULC predictions for 9 classes over the specified time period (3 months or more is usually required for sufficient cloud-free scenes), generating a representative snapshot of LULC.
Data Projection: Universal Transverse Mercator (UTM)
Mosaic Projection: WGS84
Source imagery: Sentinel-2
Cell Size: 10m (0.00008983152098239751 degrees)
Land Use Classes
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.
Any significant clustering of tall (~15 m 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).
Any area of low, non-flooded vegetation with very little-to-no taller (~15m or higher) vegetation, homogeneous or heterogeneous, containing any degree of the following: wild cereals and grasses with no obvious human plotting (i.e. not a plotted field); mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; clearings of homogeneous grasses; 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.
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.
Human planted / plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.
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.
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.
Snow / Ice
Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snowfields.
No land cover information due to persistent cloud cover.
About Impact Observatory
Impact Observatory brings AI-powered algorithms and on-demand data to sustainability and environmental risk analysis for governments, industries, and markets. Impact Observatory empowers decision-makers with the timely, actionable, science-based geospatial insights they need to succeed.
Founded in 2020 and based in Washington, DC, Impact Observatory’s machine learning (ML) and science teams blend capabilities ranging from data science, software engineering, ML, science communications, program management, and strategic planning to provide the science-based insights you need for your business, mission or impact goals.
- Supply Chain Digitization
- Economic Development
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- Situational Awareness
- Risk Management
- Architecture, Engineering, and Construction
- National Government
- Natural Resources
Feb 25, 2022