DOTA: A Large-scale Dataset for Object Detection in Aerial Images

TL;DR

DOTA is a comprehensive large-scale dataset of aerial images with detailed annotations, designed to advance object detection research in Earth Observation by providing diverse, challenging data for developing and benchmarking algorithms.

Contribution

The paper introduces DOTA, a large-scale, richly annotated dataset for aerial image object detection, addressing the scarcity of suitable datasets in Earth Vision.

Findings

State-of-the-art detectors perform poorly on DOTA, indicating its challenging nature.

DOTA covers diverse object scales, orientations, and shapes, reflecting real-world aerial imagery.

Baseline evaluations demonstrate the dataset's utility for advancing Earth Observation object detection.

Abstract

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.