Journal
REMOTE SENSING
Volume 15, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/rs15153774
Keywords
training data; high-resolution land cover; global land cover; machine learning; deep learning; satellite image classification; classification accuracy assessment
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Land cover information is crucial for sustainable development and decision-making. This study created a new benchmark dataset called MOLCA by integrating multiple existing high-resolution land cover datasets, covering Sub-Saharan Africa, the Amazon, and Siberia. MOLCA has a higher number of pixels and coverage for regions with limited training data availability, making it a valuable resource for future high-resolution land cover mapping.
Land cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps.
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