A 4-band (RGBN) Crop Segmentation Dataset for Oilseed Rape and Wheat

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Authors: Fajian Zong, Xuzhou Qu, Liping Chen, Baoyuan Zhang, C. Song, Yongkun Ji, Siwen Zhao, Shengnan Zhai, Yancang Wang, Jianwei Wu, Xiaohe Gu
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Abstract

Oilseed Rape and Wheat Segmentation Dataset (RGBN) DescriptionThis dataset provides high-resolution 4-band (RGBN) imagery and pixel-level labels for crop classification. Data Format Imagery: 4-band GeoTIFF (Red, Green, Blue, Near-Infrared). Patch Size: 512 x 512 pixels. Labels: 8-bit grayscale raster masks. Class Definitions 0: Background 1: Oilseed Rape 2: Wheat 3: Other Crops Dataset StructureThe data is organized into three subsets (Datasets A, B, and C) in Pascal VOC format: JPEGImages: Original 4-band image patches. SegmentationClass: Pixel-level labels (0-3). ImageSets/Segmentation: Text files for training and validation splits. Data GenerationInitial labels were generated using Maximum Likelihood Classification (MLC). All boundaries were then manually inspected and corrected in ArcGIS 10.8 to ensure high geometric precision.

Fields of Study & Subjects

Smart Agriculture and AI Remote Sensing in Agriculture Remote-Sensing Image Classification

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 February 2026
Language
EN
Document type
Dataset