ArticleScientific data2023
A global land cover training dataset from 1984 to 2020.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- National 10-m soybean maps for South Africa from 2018 to 2025.Scientific data · 2026Article
- Global emission factor dataset for Scope 3 machine learning applications.Scientific data · 2026Article
- Natural forests of the world - a 2020 baseline for deforestation and degradation monitoring.Scientific data · 2025Article
- Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations.Scientific data · 2025Article
- The Applicability of a Complete Archive of Keyhole Imagery for Land-Use Change Detection in China (1960-1984).Sensors (Basel, Switzerland) · 2025Article
- STOPOVER HOTSPOTS FOR MIGRATORY BIRDS IN NORTH AND CENTRAL AMERICA.Biodiversity informatics · 2025Article
- Article
- Annual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine Learning.Scientific data · 2024Article
- Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets.Scientific data · 2024Article
- A global land cover training dataset from 1984 to 2020.Scientific data · 2023Article
Corrections and comments
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Authors and funding
26 authors.
Funding
Abstract
State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.