Monitoring on Demand
Impact Observatory’s AI-powered monitoring solutions enable decision makers to understand risks and anticipate change at unprecedented speed and scale
Our Land Use & Land Cover Maps on Demand
IO uses a unique machine learning approach to classify land use and land cover (LULC) categories globally using Sentinel-2 imagery. Custom LULC results are available for any area of interest over user-specified time periods, beginning in 2018, and including the most recently available Sentinel-2 data. Results have an average accuracy of 85% (compared to human expert labels).
Forest fires in 2020 ravaged Butte County, California, resulting in significant tree loss in the 2021 land-cover.
Comparing the maximum snow extent at austral winter 2020 and 2021 in the Andes mountains east of Santiago, Chile
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.
For detailed information of our Land Use & Land Cover's 9 classes, explore our behind the scenes section of our Automated Annual Global Maps product.
Interested in our maps?
Contact us today, if you are ready to see your map on demand come to life.
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Urban expansion highlights from the annual 2017 and 2021 LULC maps over Bujumbura, Burundi.