Accurate Land Use and Land Cover (LULC) data are vital for effective land planning and management. This study evaluates the U-Net model for LULC mapping using high-spatial-resolution (2 m) imagery from the WPM sensor on the CBERS 04A satellite. The research focuses on the Servidão River Basin in Rio Claro, Brazil, an urban watershed susceptible to flooding. A pseudo-labeling framework is proposed to reduce reliance on manually annotated training data. Training samples were automatically generated by integrating spectral indices (NDVI, NDWI, SOCI, CI, NISI), Principal Component Analysis, and unsupervised Iso-Cluster classification. Several U-Net configurations were evaluated, with a ResNet-34 backbone with class weighting achieving the highest performance. The model was then retrained using a manually refined reference dataset to enhance the representation of spectrally complex classes. Accuracy assessment resulted in an Overall Accuracy of 0.93, average Precision and Recall of 0.92, and a mean Intersection over Union (IoU) of 0.86. These findings indicate that the proposed pseudo-labeling strategy, combined with a U-Net, offers a robust approach for LULC mapping in complex urban environments using freely available CBERS 04A imagery.