Up-to-date land-cover maps are a basic source of information to track the impact that human activity, natural processes and climate change have on land cover. They are critical for making informed policy, development and resource management decisions, and for disciplines such as agriculture, forestry, water management, urban planning, environmental protection and crisis management.
While the Copernicus Sentinel-2 mission delivers ideal images to map land cover, producing maps means that huge amounts of time-series data have to be processed. To make this possible, the ESA-funded Sentinel-2 for Science Land Cover project explored novel ways of capitalising on the latest cloud-computing technologies and machine learning to automate mapping. While still in the experimental stage, the results demonstrate that fully-automated mapping is just around the corner. For example, Europe’s land-cover has been mapped showing 13 land cover classifications.