An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations

As one of the foundational datasets in geographical information science, land use and land cover (LULC) data plays a crucial role in the study of human–environment interaction mechanisms, urban sustainable development, and other related issues. Although existing research has explored land use type recognition from remote sensing imagery, interpretation algorithms, and other perspectives, significant spatial discrepancies exist between these data products. Therefore, we introduced a multi-source LULC data integration approach that incorporates spatial dependencies, employing a fully connected neural network alongside geographical environmental variables to enhance the accuracy of land use data. The Yangtze River Delta was chosen as the case study area for method evaluation and validation. Our results show that the proposed method significantly improves land use classification accuracy. A comparative analysis from both global and category-specific perspectives revealed that the data product obtained exhibited notably higher overall accuracy, Kappa coefficient, and intersection over union compared to the China land cover dataset, the global 30 m fine land cover dynamic monitoring dataset, and the multi-period land use remote sensing monitoring dataset. Additionally, both the quantity and allocation disagreements of the fused LULC data were improved. The proposed multi-source land use data fusion method and its products can provide support and services for urban sustainable construction, resource management, and environmental monitoring and protection, demonstrating significant research value and importance.