A multi-source data fusion method for land cover production: a case study of the East European Plain

Abstract

ABSTRACTLarge-area high-precision land cover mapping faces challenges such as a lack of uniform classification systems and the inability to compare different products. The current use of deep learning methods in land cover data product generation provides opportunities to address these issues. However, this requires the creation of many manually labeled samples, and this involves high time and labor costs. Therefore, research is being conducted to examine methods for producing land cover products by integrating multiple data sources. This study focuses on the East European Plain and is based on land cover types that include water, forest, grass, wetland, crop, shrub, built area, bare area, ice, and tundra. Label images were fused using data from Dynamic World, ESA World Cover, ESRI Global LULC, GlobeLand30, and Open Land Map. Using a modified Dynamic World model, predictions for the East European Plain for 2022 were made, ultimately resulting in a land cover product at 10 m resolution. Compared to Dynamic World data, the classification system of this dataset aligns with the land cover conditions of the study area. The dataset possessed higher accuracy. This method integrates the advantages of existing data products, automates the generation of training labels, and effectively reduces manual costs.

Keywords

• Land cover
• East European Plain
• multi-source data fusion
• deep learning