Sentinel-2 Multispectral Dataset for Coffee Crop Semantic Segmentation in the IGCV Region, Brazil

This dataset provides a collection of multispectral image patches and corresponding binary masks designed for the semantic segmentation of coffee plantations within the Campo das Vertentes Geographical Indication (IGCV), Brazil. Derived from harmonized Sentinel-2 Level-2A surface reflectance imagery, the dataset includes samples in 64×64, 128×128, and 256×256 pixel dimensions, maintaining a 10 meter spatial resolution. Each patch comprises five channels: Red (B4), Green (B3), Blue (B2), Near Infrared (B8), and NDVI. The corresponding ground truth masks are based on official vector data from EMATER MG. To ensure a stable spectral representation of the crop canopies, the imagery was processed using annual median composites synchronized with the reference year of each mask to minimize cloud and shadow interference. This collection is particularly suited for evaluating deep learning architectures in fragmented agricultural landscapes dealing with class imbalance. Finally, the dataset also includes model artifacts generated during the experiments, such as predicted masks, weights of the best performing models, and TensorBoard training logs. These resources enable the inspection of training dynamics, reproducibility of the results, and further benchmarking of segmentation models.