Integrating pre-processing pipelines in ODC based framework
arXiv:2210.01528 · doi:10.1109/IGARSS46834.2022.9884209
Abstract
Using on-demand processing pipelines to generate virtual geospatial products is beneficial to optimizing resource management and decreasing processing requirements and data storage space. Additionally, pre-processed products improve data quality for data-driven analytical algorithms, such as machine learning or deep learning models. This paper proposes a method to integrate virtual products based on integrating open-source processing pipelines. In order to validate and evaluate the functioning of this approach, we have integrated it into a geo-imagery management framework based on Open Data Cube (ODC). To validate the methodology, we have performed three experiments developing on-demand processing pipelines using multi-sensor remote sensing data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated using open-source processing frameworks.
4 pages, 5 figures, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium