Radiant Earth Foundation releases benchmark land cover training data for Africa
Information from Radiant Earth Foundation
Radiant Earth Foundation has released “LandCoverNet,” a human-labelled global land cover classification training dataset. This release contains data across Africa, which accounts for ~1/5 of the global dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping for timely insights into natural and anthropogenic impacts on the Earth.
Global land cover maps derived from Earth observations are not new, but the influx of open-access high spatial resolution Earth observations, such as that from the European Space Agency’s Sentinel missions, coupled with improved computer power, encouraged the development of advanced algorithms.
Machine learning models applied to high resolution remotely sensed imagery can classify land cover classes more accurately and faster, given the availability of high-quality training data. As a result, applications that extract intelligence on agricultural productivity, urban structures, maritime monitoring, and other insights have emerged in the last decade. These efforts underscore the potential of using machine learning to solve global development and humanitarian challenges, including the need for regularly updated land cover maps, which is essential to monitor and measure progress toward several Sustainable Development Goals (SDGs).
LandCoverNet is an annual land cover classification training dataset with labels for the multi-spectral high-quality satellite imagery from Sentinel-2 satellites, covering Africa, Asia, Australia, Europe, North America, and South America. Seven land cover class types are identified: water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice. The annual land cover classes are labelled based on 24 scenes of Sentinel-2 for each tile throughout 2018.
This first version of LandCoverNet, which contains image chips across Africa, provides high-quality training data for pixel-wise land cover classification and a consensus score to indicate the uncertainty in human interpretation of each class. Data scientists and practitioners can use this data to develop new land cover classification models or validate their own models’ accuracy. Land cover maps created with LandCoverNet can also identify underrepresented areas where more data are needed.
LandCoverNet is distributed under the Creative Commons Attribution 4.0 license (CC BY 4.0) on Radiant MLHub. You can read the dataset documentation and access the example Jupyter notebook on the Radiant MLHub registry page.
Read more here about how the training data was generated.