  Cloud-based overlays are often present in optical remote sensing images, thus
limiting the application of acquired data. Removing clouds is an indispensable
pre-processing step in remote sensing image analysis. Deep learning has
achieved great success in the field of remote sensing in recent years,
including scene classification and change detection. However, deep learning is
rarely applied in remote sensing image removal clouds. The reason is the lack
of data sets for training neural networks. In order to solve this problem, this
paper first proposed the Remote sensing Image Cloud rEmoving dataset (RICE).
The proposed dataset consists of two parts: RICE1 contains 500 pairs of images,
each pair has images with cloud and cloudless size of 512*512; RICE2 contains
450 sets of images, each set contains three 512*512 size images. ,
respectively, the reference picture without clouds, the picture of the cloud
and the mask of its cloud. The dataset is freely available at
\url{https://github.com/BUPTLdy/RICE_DATASET}.
