Remote sensing image classification is a fundamental task in remote sensing image processing. Remote sensing field still lacks of such a large-scale benchmark compared to ImageNet, Place2. We propose a remote sensing image classification benchmark (RSI-CB) based on crowd-source data which is massive, scalable, and diversity. Using crowdsource data, we can efficiently annotate ground objects in remotes sensing image by point of interests, vectors data from OSM or other crowd-source data. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. In this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size respectively since different convolution neural networks requirement different image size. The former sub dataset contains 6 categories with 35 subclasses with total of more than 24,000 images; the later one contains 6 categories with 45 subclasses with total of more than 36,000 images. The six categories are agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland and other land, and each category has several subclasses. This classification system is defined according to the national standard of land use classification in China, and is inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on handcrafted features, such as such as SIFT, and classical CNN models, such as AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB have good performance when transfer to other dataset, i.e. UC-Merced, and good generalization ability. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification task than other ones in big data era, and can be potentially used in practical applications.