BaleUAVision: Hay Bales UAV Captured Dataset

BaleUAVision dataset comprises a comprehensive collection of UAV-captured images of agricultural fields with hay bales. It includes high-resolution RGB imagery (in both raw and annotated -COCO, CSV, JSON, YOLO, Segmentation Masks- formats), catering to a wide range of applications from precision agriculture to machine learning in computer vision and autonomous navigation. More specifically, it encompasses detailed UAV-captured data from agricultural fields, characterized by varied flight parameters to optimize image capture for machine learning applications. This dataset is distinctive due to its diverse altitude range (50-100m), multiple speed settings (3.7-5m/s), and different overlap ratios ensuring comprehensive field coverage. The total area covered by the dataset is 938,715 square meters, with a Ground Sampling Distance (GSD) ranging from 1.53 to 3.06 cm/pixel, facilitating fine-grained analysis. The data includes 2,599 high-resolution RGB images, each meticulously annotated for semantic segmentation, and is coupled with orthophotos to support simulation tasks such as autonomous hay bale collection scenarios. This dataset is a valuable asset for advancements in precision agriculture, offering extensive resources for developing and testing computer vision and path-planning algorithms. Dataset Details Images: High-resolution RGB images of 16 Hay bale fields Number of images: 2,599 Formats: Raw RGB images and Annotated images in {COCO, CSV, JSON, YOLO, Segmentation Masks} formats Annotations: Semantic segmentation with polygons Dataset Task Type Usage: Segmentation and Classification/Detection Tasks Annotation Software Used: Label Studio Captured Fields: The dataset includes imagery from 16 fields, with 14 located in the Xanthi region and 2 in the Drama region, both situated in the northern part of Greece Orthophotos: Orthomosaic views for each subset of the dataset, generated through an image stitching process, offering a macro-perspective of the fields Size: ~45.5GB Resolution: 4056x3040 (RGB) Flight Parameters: Various altitudes, speeds and overlaps Geo-location: Yes, each image is geo-referenced Total Area Covered: 938,715 square meters (m²) in total Additional Information: The number of hay bales has been manually counted for each field from the orthophoto representations, providing a reliable reference for users aiming to develop or evaluate algorithms for automated hay bale counting Files Structure ├── BaleUAVision ├── Annotated ├── Hay bales 1 ├── Hay-bales-1-YOLO # folder which contains **YOLO** formated .txt files ├── images # folder which contains images with prefixes ├── Masks # folder that contains image **Segmentation Masks** using the python script "segmentation_masks.py" ├── classes # .txt file which contains the name of the class ├── Hay-bales-1-COCO # .json file which is for **COCO** format ├── Hay-bales-1-CSV # classic .csv file for **CSV** format ├── Hay-bales-1-JSON # .json file for **JSON** format └── notes ├── Hay bales 2 ├── Hay-bales-2-YOLO ├── images ├── Masks ├── classes ├── Hay-bales-2-COCO ├── Hay-bales-2-CSV ├── Hay-bales-2-JSON └── notes ... └── Hay bales 16 ├── Hay-bales-16-YOLO ├── images ├── Masks ├── classes ├── Hay-bales-16-COCO ├── Hay-bales-16-CSV ├── Hay-bales-16-JSON └── notes ├── Orthophotos ├── Hay bales 1 orthophoto # .tiff images for classic orthomosaic/panorama representation ├── Hay bales 2 orthophoto ... └── Hay bales 16 orthophoto ├── Raw Data ├── Hay bales 1 # contains 205 .jpg images ├── Hay bales 2 # contains 423 .jpg images ... └── Hay bales 16 # contains 119 .jpg images └── Dataset Description.csv # contains details and metadata for each Hay bale sub-setBaleUAVision数据集是一套涵盖全面的无人机拍摄农田干草堆图像集合。其包含高分辨率RGB影像，涵盖原始格式与多种标注格式——包括COCO、CSV、JSON、YOLO及分割掩码格式，可应用于精准农业、计算机视觉机器学习、自主导航等诸多领域。具体而言，该数据集包含由无人机拍摄的农田详细影像，通过多样化的飞行参数优化图像采集流程，以适配机器学习应用需求。本数据集的特色在于其覆盖了多样的飞行高度区间（50至100米）、多种飞行速度设置（3.7至5米/秒）以及不同的重叠率，确保实现农田的全面覆盖。数据集总覆盖面积达938,715平方米，地面采样距离（Ground Sampling Distance, GSD）为1.53至3.06厘米/像素，支持精细化分析。数据包含2,599张高分辨率RGB影像，每张影像均经过精细的语义分割标注，并配套正射影像以支持诸如干草堆自主收集场景等模拟任务。该数据集是推动精准农业发展的宝贵资源，可为计算机视觉与路径规划算法的开发与测试提供丰富支撑。 数据集详情 图像：16个干草堆农田的高分辨率RGB影像 图像数量：2,599张 格式：原始RGB影像，以及采用{COCO、CSV、JSON、YOLO、分割掩码}格式的标注影像 标注：带多边形的语义分割标注 数据集任务类型与用途：分割任务与分类/检测任务 所用标注软件：Label Studio 拍摄农田：数据集涵盖16块农田的影像，其中14块位于希腊北部的克桑西（Xanthi）地区，2块位于希腊北部的德拉马（Drama）地区 正射影像：数据集每个子集均配有通过图像拼接生成的正射镶嵌视图，可呈现农田的宏观视角 数据体量：约45.5GB 影像分辨率：4056×3040（RGB格式） 飞行参数：涵盖多种高度、速度及重叠率参数 地理定位：支持，每张影像均带有地理参考信息 总覆盖面积：总计938,715平方米（m²） 附加信息：研究人员已通过正射影像手动统计了每块农田内的干草堆数量，可为开发或评估自动化干草堆计数算法的用户提供可靠参考基准。 文件结构 ├── BaleUAVision ├── Annotated ├── Hay bales 1 ├── Hay-bales-1-YOLO # 存放**YOLO**格式.txt文本文件的文件夹 ├── images # 带有前缀的影像文件夹 ├── Masks # 存放分割掩码的文件夹，通过Python脚本“segmentation_masks.py”生成 ├── classes.txt # 包含类别名称的文本文件 ├── Hay-bales-1-COCO # **COCO**格式的.json标注文件 ├── Hay-bales-1-CSV # **CSV**格式的经典.csv标注文件 ├── Hay-bales-1-JSON # **JSON**格式的.json标注文件 └── notes ├── Hay bales 2 ├── Hay-bales-2-YOLO ├── images ├── Masks ├── classes.txt ├── Hay-bales-2-COCO ├── Hay-bales-2-CSV ├── Hay-bales-2-JSON └── notes ... └── Hay bales 16 ├── Hay-bales-16-YOLO ├── images ├── Masks ├── classes.txt ├── Hay-bales-16-COCO ├── Hay-bales-16-CSV ├── Hay-bales-16-JSON └── notes ├── Orthophotos ├── Hay bales 1 orthophoto # 经典正射镶嵌/全景展示用.tiff影像 ├── Hay bales 2 orthophoto ... └── Hay bales 16 orthophoto ├── Raw Data ├── Hay bales 1 # 包含205张.jpg格式影像 ├── Hay bales 2 # 包含423张.jpg格式影像 ... └── Hay bales 16 # 包含119张.jpg格式影像 └── Dataset Description.csv # 包含每个干草包子集的详细信息与元数据的.csv文件