A Dataset of Aligned RGB and Multispectral UAV Imagery for Semantic Segmentation of Weedy Rice

This dataset includes 734 UAV-captured RGB images and their corresponding aligned multispectral (MS) images for the semantic segmentation of weedy rice in cultivated rice fields. The images were collected using a DJI Mavic 3 Multispectral UAV during three cropping seasons in Vietnam’s Mekong Delta. Each sample contains an RGB image, four MS bands (Green, Red, Red Edge, Near-Infrared), a binary mask indicating weedy rice regions, and a visualization overlay. All images were preprocessed (radiometric correction, undistortion, alignment, cropping) and resized to 1280 × 960 pixels. Ground-truth masks were generated using a fine-tuned Segment Anything Model (SAM), followed by manual verification. Spatial metadata and file mappings are included. The dataset supports research in precision agriculture, multi-modal semantic segmentation, and UAV-based crop monitoring.