A Dataset of Aligned RGB and Multispectral UAV Imagery for Semantic Segmentation of Weedy Rice
Description
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.
Files
Steps to reproduce
1. Unzip the dataset and explore the five subfolders: RGB, Multispectral, Masks, Overlay, and Metadata. 2. Use the 'image_metadata.csv' file to access acquisition information and GPS data. 3. RGB and MS images are aligned and resized to 1280×960 pixels for model training. 4. Binary segmentation masks are stored in PNG format, with a pixel value of 255 indicating weedy rice. 5. The recommended input format for training semantic segmentation models is an RGB image accompanied by a mask or a multi-modal input comprising RGB and four MS bands. 6. Sample training, validation, and test splits are provided as text files. 7. Users may visualize overlay images to verify label quality or utilize metadata for geospatial integration.