Mapping banana crops in heterogeneous tropical landscapes remains challenging due to spectral similarity with surrounding vegetation, fragmented smallholder systems, and complex land-use mosaics. This study applies a deep learning approach, using a U-Net model, on high spatial resolution CBERS-4A imagery to map banana crops in Brazil’s Ribeira Valley, a subtropical region with high rainfall and heterogeneous land cover. Reference data were created through manual interpretation of satellite imagery supported by field knowledge. Representative image tiles were selected and divided into smaller patches for model training, validation, and testing. The U-Net model was trained with standard optimization techniques and evaluated using common semantic segmentation metrics. On the validation set, it achieved strong performance (accuracy 0.91, F1-score 0.84, AUC-ROC 0.96, AUC-PR 0.92). Performance was maintained or improved on the independent test set (accuracy 0.91, F1-score 0.86, AUC-ROC 0.97, AUC-PR 0.93), indicating good generalization. with high agreement between predicted and reference data. Most errors occurred at boundaries between crops and natural vegetation. Additional validation using official agricultural statistics confirmed consistency at the municipal scale. The approach demonstrates that high-resolution imagery combined with deep learning can effectively map banana crops in the region and offers a promising tool for agricultural monitoring and land-use planning in complex environments. The code, trained models, and data are publicly available at https://github.com/hnbendini/banana-unet-mapping.