Radiant Earth Foundation Releases LandCoverNet, the First Global Multi-Satellite Training Dataset for Land Cover Classification
LandCoverNet is a human-annotated training dataset with images from Sentinel-1, Sentinel-2, and Landsat 8 satellite missions to support natural resource management in Africa, Asia, Australia and Oceania, Europe, and North and South America
Radiant Earth Foundation, the leading nonprofit dedicated to open Earth observation training data and machine learning models, announced the release of LandCoverNet, the world’s first training dataset for global land cover classification. This global annual land cover classification training dataset will enable scientists and practitioners to create high-resolution and up-to-date land cover maps, a piece of critical information for monitoring the sustainable use of natural resources.
Cropland expansion, urbanization, and deforestation rates are among the changes land cover maps can measure. Tracking these changes provide critical insights into human and non-human activities that profoundly impact the global landscape. Scientists and policymakers can use the insights from these maps to help communities and governments meet Sustainable Development Goals (SDGs). Real-world application examples of land cover maps that measure our planet’s health include Impact Observatory’s automated annual global map, and Google and the World Resources Institute’s recently released Dynamic World. While high-resolution satellite-based land cover maps are becoming more available, there is an overwhelming lack of open-access training datasets that will allow practitioners to create thematic maps to monitor our natural resources on a global or regional scale or validate the accuracy of existing maps.
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Training data is the building block for producing machine learning models. In the case of land cover mapping, the training data contains satellite images along with labels specifying land cover classes present in the image. Models learn the pattern of these classes from the training data and can generate maps at large spatial scales. LandCoverNet supplies just that — training data for annual land cover classification that allows practitioners to build planetary change detection models on every continent of this world inhabited by humans.
LandCoverNet identifies seven land cover class types: water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice. Each labeled pixel is also associated with a consensus score indicating the uncertainty from the human annotation process. These scores can help the model better learn the differences and similarities of each land cover class. Radiant Earth generated the training datasets from 300 geographically diverse tiles of ESA’s Sentinel-2 mission covering Africa, Asia, Australia and Oceania, Europe, North America, and South America. A total of 8,941 image chips of 256 x 256 pixels were labeled globally, resulting in ~586 million pixels for the entire training dataset. In partnership with TaQadam, a geospatial annotation platform specializing in social impact projects, and B.O.T. (Bridge. Outsource. Transform), the first impact sourcing platform in the Middle East and North Africa, annotators were trained to label and validate each chip in the training dataset.
Schmidt Futures provided the initial funding for the development of LandCoverNet. The first version, released in 2019, contained image chips across Africa based solely on Sentinel-2 data. Today’s release completes the global dataset that guarantees more accurate and scalable classification models across diverse geographies. NASA ACCESS and Microsoft Planetary Computer programs offered additional support leading to the completion of LandCoverNet, while Sinergise provided in-kind technology support throughout the development of LandCoverNet.
Dr. Hamed Alemohammad, Executive Director and Chief Data Scientist at Radiant, remarked on the release of the global LandCoverNet series. “LandCoverNet is an essential benchmark for data scientists across the globe who wish to build advanced monitoring tools of our environment,” Alemohammad said. “It was born out of a community effort to improve the accuracy of global land cover maps. The applications built from this dataset will enable fully-automated and dynamic land cover classification algorithms using open-access satellite imagery. We are incredibly thankful for all funders and experts that made this great accomplishment possible.”
“The availability and reusability of large-scale training datasets and models have increased the possibilities of advanced science applications of AI,” said Dr. Manil Maskey, Senior Research Scientist at NASA. “As we move towards adopting open science principles within the NASA Science Mission Directorate, we envision engaging a broad community of experts to solve some of the challenging scientific problems using AI coupled with the large-scale training datasets and benchmark models. Radiant Earth’s work in providing the opportunity to engage a broad community and infrastructure to support training datasets and models is instrumental in advancing AI for science with open science principles. We are excited about the release of LandCoverNet and the possible applications.”
Dr. Bruno Sánchez-Andrade Nuño, Director of Microsoft Planetary Computer, shared: “We are incredibly excited to see what people build using LandCoverNet. Training data is one of the biggest technical bottlenecks of progress with AI. Thus, this project provides incredible value to everyone, and doing so through Radiant Earth ensures that it finds its way to the hands of stakeholders that need it the most. At the end of the day, it is not what AI can do, but how it helps people and the planet.”
LandCoverNet is available for download under the Creative Commons Attribution 4.0 license (CC BY 4.0) on Radiant MLHub, an ecosystem of open geospatial training data and machine learning models managed by Radiant Earth.
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