Dota V1.5

[Faith Lienhard]

DOTAstands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).

DOTA serves as a benchmark for training and evaluating models specifically tailored for aerial image analysis. With the inclusion of OBB annotations, it provides a unique challenge, enabling the development of specialized object detection models that cater to aerial imagery's nuances.

Typically, datasets incorporate a YAML (Yet Another Markup Language) file detailing the dataset's configuration. For DOTA v1 and DOTA v1.5, Ultralytics provides DOTAv1.yaml and DOTAv1.5.yaml files. For additional details on these as well as DOTA v2 please consult DOTA's official repository and documentation.

Please note that all images and associated annotations in the DOTAv1 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!

A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the official DOTA website.

The DOTA dataset is a specialized dataset focused on object detection in aerial images. It features Oriented Bounding Boxes (OBB), providing annotated images from diverse aerial scenes. DOTA's diversity in object orientation, scale, and shape across its 1.7M annotations and 18 categories makes it ideal for developing and evaluating models tailored for aerial imagery analysis, such as those used in surveillance, environmental monitoring, and disaster management.

DOTA utilizes Oriented Bounding Boxes (OBB) for annotation, which are represented by rotated rectangles encapsulating objects regardless of their orientation. This method ensures that objects, whether small or at different angles, are accurately captured. The dataset's multiscale images, ranging from 800 800 to 20,000 20,000 pixels, further allow for the detection of both small and large objects effectively.

The DOTA-v1.5 images are collected from the Google Earth, GF-2 and JL-1 satellite provided by the China Centre for Resources Satellite Data and Application, and aerial images provided by CycloMedia B.V. DOTA consists of RGB images and grayscale images. The RGB images are from Google Earth and CycloMedia, while the grayscale images are from the panchromatic band of GF-2 and JL-1 satellite images. All the images are stored in 'png' formats.

The object categories in DOTA-v1.5: plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, swimming pool and container crane.

Regarding the construction of DOTA, the authors emphasize the importance of collecting images from various sensors and platforms to address dataset biases. They describe the acquisition of such as Google Earth, the Gaofen-2 Satellite, Jilin-1 Satellite, and CycloMedia airborne images. These images vary in resolution and sensor type, reflecting real-world conditions. Additionally, they detail the selection of 18 object categories for annotation based on their relevance and frequency in real-world applications.

An example image taken from DOTA. (a) A typical image in DOTA consisting of many instances from multiple categories. (b), , (d), (e) are cropped from the source image. We can see that instances such as small vehicles have arbitrary orientations. There is also a massive scale variation across different instances. Moreover, the instances are not distributed uniformly. The instances are sparse in most areas but crowded in some local areas. Large vehicles and ships have large ARs. (f) and (g) exhibit the size and orientation histograms, respectively, for all instances.

The aspect ratio (AR) of instances is essential for anchor-based models. DOTA considers two ARs for instances: one based on the original Oriented Bounding Boxes (OBBs) and another based on Horizontal Bounding Boxes (HBBs). The distribution of these two ARs is explored in the dataset. Instances exhibit significant variation in aspect ratio, with many instances having a large aspect ratio.

The number of instances per image varies widely in DOTA, with some images containing up to 1000 instances while others have just one instance. This property is compared to other object detection datasets. The density of instances varies across categories, with some categories having significantly denser instances than others. The authors provide quantitative analysis by measuring the distance between instances within the same category and binning them into three density categories: dense, normal, and sparse. The density is measured by calculating the distance to the closest instance.

The authors also note significant improvements in DOTA from earlier versions (DOTA v1.0 and DOTA v1.5), which included addressing challenges related to tiny objects, large-scale images, and multi-source overhead images. In DOTA-v2.0, there are 18 common categories, 11,268 images, and 1,793,658 instances, with the addition of new categories like airport and helipad. The dataset is divided into train, val, test-dev, and test-challenge (not available at download source - comm. dninja) subsets, each with specific proportions to avoid overfitting. Additionally, two test subsets, test-dev and test-challenge, have been introduced for evaluation, following a similar structure to the MS COCO dataset.

In summary, the authors of the dataset have made significant contributions to the field of object detection in aerial images by providing a comprehensive dataset, baselines, and tools to facilitate research and development in this domain. They have addressed various challenges and limitations to create a more robust benchmark dataset for oriented object detection in aerial images.

The dataset consists of 5215 images with 349589 labeled objects belonging to 18 different classes including small vehicle, large vehicle, ship, and other: harbor, tennis court, airport, bridge, swimming pool, ground track field, roundabout, storage tank, plane, soccer ball field, baseball diamond, basketball court, helicopter, container crane, and helipad.

DOTA dataset has 5215 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

There are 18 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

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