A High-Performance Automated Large-Area Land Cover Mapping Framework

Land cover mapping plays a pivotal role in global resource monitoring, sustainable development research, and effective management. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, thereby bringing challenges to creating multi-timesteps large-area products for monitoring dynamic land cover. Therefore, improving the efficiency of each stage in land cover mapping and automating the mapping process is currently an urgent issue to be addressed. This study proposes a high-performance automated large-area land cover mapping framework (HALF). By leveraging Docker and workflow technologies, the HALF effectively tackles model heterogeneity in complex land cover mapping processes, thereby simplifying model deployment and achieving a high degree of decoupling between production models. It optimizes key processes by incorporating high-performance computing techniques. To validate these methods, this study utilized Landsat imagery data and extracted samples using GLC_FCS and FROM_GLC, all of which were acquired at a spatial resolution of 30 m. Several 10&deg: &times: 10&deg: regions were chosen globally to illustrate the viability of generating large-area land cover using the HALF. In the sample collection phase, the HALF introduced an automated method for generating samples, which overlayed multiple prior products to generate a substantial number of samples, thus saving valuable manpower resources. Additionally, the HALF utilized high-performance computing technology to enhance the efficiency of the sample&ndash:image matching phase, thereby achieving a speed that was ten times faster than traditional matching methods. In the mapping stage, the HALF employed adaptive classification models to train the data in each region separately. Moreover, to address the challenge of handling a large number of classification results in a large area, the HALF utilized a parallel mosaicking method for classification results based on the concept of grid division, and the average processing time for a single image was approximately 6.5 s.