Omkar2118/datasets huggingface dataset:
CeyMo

CeyMo is a novel benchmark dataset for road marking detection which covers a wide variety of challenging urban, sub-urban and rural road scenarios. The dataset consists of 2887 total images of 1920 × 1080 resolution with 4706 road marking instances belonging to 11 classes. The test set is divided into six categories: normal, crowded, dazzle light, night, rain and shadow.

Introducing paper: CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection
DSIFN-CD

The dataset is manually collected from Google Earth. It consists of six large bi-temporal high resolution images covering six cities (i.e., Beijing, Chengdu, Shenzhen, Chongqing, Wuhan, Xian) in China. The five large image-pairs (i.e., Beijing, Chengdu, Shenzhen, Chongqing, Wuhan) are clipped into 394 subimage pairs with sizes of 512×512. After data augmentation, a collection of 3940 bi-temporal image pairs is acquired. Xian image pair is clipped into 48 image pairs for model testing. There are 3600 image pairs in the training dataset, 340 image paris in the validation dataset, and 48 image pairs in the test dataset.

Introducing paper: A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images