Land use and land cover (LULC) classification plays a crucial role in addressing numerous real-world challenges. Hence, we proposed methodological advances in LULC classification from a data-centric artificial intelligence perspective, which prioritizes data quality as a key factor in improving machine learning performance. The main contributions include evaluations of novel approaches for: (i) constructing an accurately labeled dataset based on agreement among existing reliable maps; (ii) curating remote sensing data to improve accuracy, consistency, unbiasedness, relevance, diversity, and completeness; (iii) generating training samples that capture the spatial, temporal, and spectral dimensions of remote sensing data; and (iv) developing a deep learning model designed to leverage multidimensional features. The study evaluates a sample generation method grounded in reference map agreement and multidimensional feature extraction, along with a deep learning model that leverages these features, attaining high accuracy across all LULC classes and providing a robust basis for large-scale, data-centric LULC mapping.