Find research datasets worth reusing

RBOD: An annotated satellite imagery dataset for automated river barrier object detection

<h3>Introduction:</h3> <p>Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on the locations and types of river barriers, particularly small barriers such as weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD), making it the first publicly available dataset specifically for river barrier object detection.</p> <p>The RBOD dataset comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs). In this dataset, river barriers can be classified into five classes: dams, groynes, locks, sluices, and weirs. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, namely YOLOV8-OBB, Oriented R-CNN, Rotated Faster R-CNN, R3Det, and Rotated RetinaNet, which provide performance benchmarks for future applications.</p> <h3><span>Usage Notes:</span></h3> <p>The RBOD dataset consists of three folders (namely, '<em>images</em>', '<em>labels_voc</em>', and '<em>labels_yolo</em>') and a .txt file named '<em>class</em>':</p> <p>&middot;'<em>images</em>' folder - contains 4872 satellite images (.jpg).</p> <p>&middot;'<em>labels_voc</em>' folder - contains 11,741 .xml files for annotations in PASCAL VOC format. In these .xml files, the position of OBB is represented as (cx, cy, width, height, angle), where 'cx' and 'cy' denote the center coordinates, 'width' and 'height' are the lengths along the x- and y-axes, and 'angle' is the clockwise rotation angle relative to the x-axis.</p> <p>&middot;'<em>labels_yolo</em>' folder - contains 11,741 .txt files for annotations in YOLO format. In these .txt files, the OBB is represented as (class_index, x1, y1, x2, y2, x3, y3, x4, y4), where &lsquo;class_index&rsquo; denotes the target category, and &lsquo;x1, y1, x2, y2, x3, y3, x4, y4&rsquo; are the normalized coordinates of the four corners of the bounding box.</p> <p>&middot;'<em>class</em>' txt file - record the classifications of river barriers and their indices, which correspond to the &lsquo;class_index&rsquo;.</p> <p>Note that each folder splits into three subfolders: train (70%), test (20%), and val (10%).</p>