  Semantic segmentation of drone images is critical for various aerial vision
tasks as it provides essential semantic details to understand scenes on the
ground. Ensuring high accuracy of semantic segmentation models for drones
requires access to diverse, large-scale, and high-resolution datasets, which
are often scarce in the field of aerial image processing. While existing
datasets typically focus on urban scenes and are relatively small, our Varied
Drone Dataset (VDD) addresses these limitations by offering a large-scale,
densely labeled collection of 400 high-resolution images spanning 7 classes.
This dataset features various scenes in urban, industrial, rural, and natural
areas, captured from different camera angles and under diverse lighting
conditions. We also make new annotations to UDD and UAVid, integrating them
under VDD annotation standards, to create the Integrated Drone Dataset (IDD).
We train seven state-of-the-art models on drone datasets as baselines. It's
expected that our dataset will generate considerable interest in drone image
segmentation and serve as a foundation for other drone vision tasks. Datasets
are publicly available at \href{our website}{https://github.com/RussRobin/VDD}.
