Open Data Cube (ODC) in Taiwan: The Initiative and Protocol Development

Cheng, Ming-Chih; Chiou, Chi-Ryong; Chen, Bin; Liu, Cynthia; Lin, Hongbo; Shih, I-Liang; Chung, Chi-Jui; Lin, Huan‐Yu; Chou, Cheng-Fu

doi:10.1109/igarss.2019.8898576

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The ODC services primarily rely on the computational facilities of the ODC server, where the actual computations are carried out. Swiss DC uses ODC [10] for efficient EO data analysis to monitor land degradation on a national scale, while [11] for forest monitoring of eastern Taiwan. The African ODC [12] provides a portfolio of services, such as crop phenology monitoring in Ghana, forest monitoring in Senegal, mangroves in Sierra Leone, and land degradation monitoring in Tanzania.…”

Section: Motivationmentioning

confidence: 99%

Scalable Data Processing Platform for Earth Observation Data Repositories

Astsatryan

Lalayan

Giuliani

2023

SCPE

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Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

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Section: Motivationmentioning

confidence: 99%

Scalable Data Processing Platform for Earth Observation Data Repositories

Astsatryan

Lalayan

Giuliani

2023

SCPE

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“…Recently, the Committee on Earth Observation Satellites (CEOS) has championed the Open Data Cube (ODC) initiative in an effort to mitigate the difficulties in generating and accessing ARD. A number of data cubes for multiple territories have become available using ODC technology, e.g., Switzerland [21,22], Columbia [23], Taiwan [24], Australia [25] and Africa [26], providing a combination of SAR and optical data. A recent effort has expanded the scope of ARD to include SAR data with interferometric coherence along with normalised radar backscatter and dual-polarization decomposition for Australia [27].…”

Section: Introductionmentioning

confidence: 99%

A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data

et al. 2022

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The rich, complementary data provided by Sentinel-1 and Sentinel-2 satellite constellations host considerable potential to transform Earth observation (EO) applications. However, a substantial amount of effort and infrastructure is still required for the generation of analysis-ready data (ARD) from the low-level products provided by the European Space Agency (ESA). Here, a flexible Python framework able to generate a range of consistent ARD aligned with the ESA-recommended processing pipeline is detailed. Sentinel-1 Synthetic Aperture Radar (SAR) data are radiometrically calibrated, speckle-filtered and terrain-corrected, and Sentinel-2 multi-spectral data resampled in order to harmonise the spatial resolution between the two streams and to allow stacking with multiple scene classification masks. The global coverage and flexibility of the framework allows users to define a specific region of interest (ROI) and time window to create geo-referenced Sentinel-1 and Sentinel-2 images, or a combination of both with closest temporal alignment. The framework can be applied to any location and is user-centric and versatile in generating multi-modal and multi-temporal ARD. Finally, the framework handles automatically the inherent challenges in processing Sentinel data, such as boundary regions with missing values within Sentinel-1 and the filtering of Sentinel-2 scenes based on ROI cloud coverage.

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“…EODC are tackling Big Data challenges by providing efficient and effective solutions to store, organize, manage and analyze large volumes of freely available data in analysis ready (ARD) format [38,39]. Various implementations exist mostly at the national and regional scales such as in Australia [40,41], Africa [42], Armenia [43], Austria [44], Brazil [45], Colombia [46], Mexico [31], Switzerland [47] and Taiwan [48]. However, none of these EODC are neither explored the potential nor routinely generating EVs.…”

Section: Introductionmentioning

confidence: 99%

Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?

Giuliani

Egger

Italiano

et al. 2020

Data

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Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.

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Open Data Cube (ODC) in Taiwan: The Initiative and Protocol Development

Cited by 7 publications

References 6 publications

Scalable Data Processing Platform for Earth Observation Data Repositories

Scalable Data Processing Platform for Earth Observation Data Repositories

A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data

Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?

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