RSI-LS-VHR-2-DATASE

To advance performance evaluation research in remote sensing object detection, we built the Remote Sensing Imagery of Large-Scale-VHR-2 categories (RSI LS-VHR-2) dataset, which is much larger than most existing datasets in this field. Table Ⅰ lists the details of the dataset for two categories, aircraft and ship.

TABLE Ⅰ. DESCRIPTION OF THE RSI LS-VHR-2 DATASET

Label	Name	Total instances	Complete instances	Fragmentary instances	Scene class	Images	Image width	Sub-images
1	aircraft	103917	85975	17942	203	2858	6000-15000	62129
2	ship	68436	54386	14050	30	397	5000-18000	53860

As shown in Table Ⅰ, the RSI LS-VHR-2 dataset has four notable characteristics:

1. Rich image variability: this dataset is collected from different sensors and platforms and includes 203 airports and 30 harbors.
2. Large scale: the width and height of each original image varies from 5000 to 18,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes.
3. Abundant instances: the dataset consists of 172,353 positive samples (103,917 aircrafts and 68,436 ships) obtained from 3255 large-scale remote sensing images distributed in 115,989 sub-images cropped from the original large-scale images.
4. Multiple target difference: an additional 31,992 fragmented instances were added to the dataset for data augmentation to test the capacity of trained models to detect incomplete targets. All the original large-scale images were cropped with a non-overlapping sliding window to generate sub-images. To facilitate feature extraction, the sub-image size is a uniform 600×600 pixels.

TABLE Ⅱ. DETAILS OF THE TEST IMAGES

Label	Scale(pixels)	Images	Instances	Sub-images
aircraft	8000 x 8000	5	272	980
ship	8000 x 8000	5	225	980

version：RSI-LS-VHR-2-DATASE - v1.0

It contains all the images for training and verification!

We will continue to improve it in the future.