Integrating Pre-Processing Pipelines in ODC Based Framework

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...

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Bibliographic Details
Published inIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium pp. 4094 - 4097
Main Authors Otamendi, U., Azpiroz, I., Quartulli, M., Olaizola, I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.07.2022
Subjects
Analytical models
data exploitation
data processing
Libraries
machine learning
management
Memory
optimization
Pipelines
Satellites
Sentinel
SNAP
Vegetation mapping
Voting
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9884209