Due to the diversity of geographical objects, it makes great sense to introduce multiscale segmentation/representation into the analysis and interpretation of high-spatial-resolution remote sensing images. However, with the increasing use of high-resolution images, traditional multiscale segmentation methods gradually show their lack in efficiency, particularly when handling large-scale images. In this paper, a novel bilevel scale-sets model (BSM) is proposed for multiscale region-based representation of large-scale remote sensing images. In the BSM, first, an image is divided into blocks with overlapped margins, and a low-level scale-sets model is blockwisely implemented. Second, a segmentation result is obtained by retrieving and mosaicking the blockwise segmentation results, based on which a high-level scale-sets model is implemented covering the whole image. To further improve the efficiency of the BSM, a parallel implementation is presented for the blockwise scale-sets model. In the experiments, first, the effectiveness of the BSM is validated using a WorldView2 image covering a coastal area of Shenzhen, where the BSM obtains accurate multiscale representation results without any mosaic artifacts. Then, the efficiency of the BSM is demonstrated by comparing with the state-of-the-art multiscale segmentation method, i.e., the one integrated in the commercial software eCognition v9.2, where the proposed BSM takes about 7 min to process a 24 000 x 24 000 multispectral ZY3 image and is two to three times faster than the competing method.