Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision

Abstract

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.

Keywords

• training data
• high-resolution land cover
• global land cover
• machine learning
• deep learning
• satellite image classification