Zhang, J.; Fu, Z.; Zhu, Y.; Wang, B.; Sun, K.; Zhang, F. A High-Performance Automated Large-Area Land Cover Mapping Framework. Remote Sens.2023, 15, 3143.
Zhang, J.; Fu, Z.; Zhu, Y.; Wang, B.; Sun, K.; Zhang, F. A High-Performance Automated Large-Area Land Cover Mapping Framework. Remote Sens. 2023, 15, 3143.
Zhang, J.; Fu, Z.; Zhu, Y.; Wang, B.; Sun, K.; Zhang, F. A High-Performance Automated Large-Area Land Cover Mapping Framework. Remote Sens.2023, 15, 3143.
Zhang, J.; Fu, Z.; Zhu, Y.; Wang, B.; Sun, K.; Zhang, F. A High-Performance Automated Large-Area Land Cover Mapping Framework. Remote Sens. 2023, 15, 3143.
Abstract
Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. 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, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.
Keywords
Land Cover; High-performance computing; Remote sensing; Workflow; Automation
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
