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.

本数据集涵盖734张无人机（UAV）采集的RGB图像，以及与之配准的多光谱（MS）图像，用于开展栽培稻田内杂草稻的语义分割研究。该数据集的图像采集自越南湄公河三角洲的三个种植季，采集设备为大疆（DJI）Mavic 3 Multispectral无人机。每份样本均包含一张RGB图像、四个多光谱波段（绿波段、红波段、红边波段、近红外波段）、用于标识杂草稻区域的二值掩码，以及可视化叠加图。所有图像均经过辐射校正、畸变校正、配准与裁剪等预处理操作，并统一调整至1280×960像素的尺寸。真值掩码由经过微调的分段任意模型（Segment Anything Model, SAM）生成，随后经人工审核验证。数据集还包含空间元数据与文件映射关系。本数据集可支撑精准农业、多模态语义分割以及基于无人机的作物监测相关研究工作。