Cloud-Based Parallel Tiling Algorithm for Large Scale Remote Sensing Datasets.

Tiled remote sensing data is essential for web-based remote sensing applications, widely applied in online map services, cloud-based remote sensing processing, etc. However, most existing tiling algorithms focus on tiling single remote sensing images with stand-alone machines. As the volume of remote sensing data increases, the demand for tiling high-resolution and large-scale remote sensing datasets increases dramatically. In this research, we propose a cloud-based parallel tiling algorithm for large-scale remote sensing datasets. A three-step processing flow is designed to implement the tiling of datasets composed of a set of images. Furthermore, three types of cloud-based storage are adopted to improve the efficiency of data extraction, namely, cloud storage, block storage, and NoSQL. We experimented with the proposed algorithm for tiling a national-scale 2 m resolution remote sensing dataset. The whole process took about 25.7 hours with up to 180 cores.