DOI: 10.5281/zenodo.15304714
title: BaleUAVision: Hay Bales UAV Captured Dataset

description: 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-set