Integrating pre-processing pipelines in ODC based framework

TL;DR

This paper presents a method to integrate on-demand pre-processing pipelines into an Open Data Cube framework, enhancing geospatial data processing efficiency and data quality for analytical applications.

Contribution

It introduces an approach to incorporate open-source processing pipelines into ODC, validated through experiments with multi-sensor remote sensing data.

Findings

Improved data processing efficiency in ODC framework

Enhanced data quality for machine learning applications

Successful integration of multi-sensor pipelines

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.