Fast multiclass object detection for remote sensing images plays an important role for a wide range of applications. Traditional methods based on a sliding window search lead to heavy computational costs and are unsuitable for multiclass detection. Recently, deep learning algorithms, especially faster region based convolutional neural networks (Faster R-CNN), which adopt a region proposal paradigm to avoid exhaustive search, has achieved state-of-the-art multiclass detection performance in computer vision. This paper investigates the use of Faster R-CNN in the earth observation community. We have three contributions: 1) It's the first time to successfully use Faster R-CNN for object detection in remote sensing images. It achieved faster speed (22 ×faster) and better performance (a mAP of 78% vs. 72%) than traditional methods; 2) we adopt data augmentation to train Faster R-CNN with limited samples; 3) we successfully tested our method on large-scale google earth images, which shows robustness of our method.