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.

本数据集包含734幅无人机（UAV）采集的RGB图像及其配准后的多光谱（MS）图像，用于栽培稻田内杂草稻的语义分割任务。所有图像由DJI Mavic 3 Multispectral无人机于越南湄公河三角洲的三个种植季中采集。每个样本均包含一幅RGB图像、四个多光谱波段（绿、红、红边与近红外）、用于标注杂草稻区域的二值掩码，以及可视化叠加图层。所有图像均经过辐射校正、畸变校正、配准与裁剪预处理，并统一调整至1280 × 960像素尺寸。真值掩码通过微调后的Segment Anything Model（SAM）生成，并经人工核验。数据集同时包含空间元数据与文件映射关系，可支撑精准农业、多模态语义分割、无人机作物监测等领域的相关研究。