The Open Data Cube (ODC) [1], [2], [3] is an open-source geospatial data management and analysis software package growing in popularity. With increased popularity there is increased demand for the computational power and storage capabilities required to analyze large spatial and temporal datasets. Cloud resource providers have bolstered their computational and satellite data offerings, simplifying access and management so that average users are able to tailor systems to their specific needs. Consequently, there exists substantial community interest in the comparative capabilities and technological performance of different cloud providers. Making informed choices in cloud providers offerings is crucial for optimizing geospatial data management and processing to meet user-specific needs. In this paper we begin to evaluate the deployment and performance of the ODC, standard notebooks, and datasets, to better determine functional differences. Our work finds that datasets saved in the local environment execute operations significantly faster than from the cloud, Google Earth Engine (GEE) has longer execution times when the metadata for a given asset is indexed locally, and that more thorough benchmarking is required to better understand the end-to-end performance of EO processes on the cloud.

Every scientific experiment starts with the data, which needs to be fine-tuned for the specific use-case. We call this "analysis ready data (ARD)". In some cases, for the sake of reusability and comparability, the specifications on how ARD should be prepared, are well defined - CEOS is working hard in this direction. In many other cases, however, the procedures are not yet mature enough to support standardisation. In Earth Observation (EO) field this is especially true, as the whole community is moving from (semi) manually analysing individual scenes, from the time there were any data barely available, to processing of time-series, now that Landsat and Sentinel made this possible. We are now even facing a problem where there is simply too much of data, with PBs of open and commercial imagery being readily available. Machine learning (ML) approach can address the challenge of shifting through data, but ML as well requires data to be pre-processed for purpose. Therefore, it is essential to have facility, which can generate ARD data customised for the specific analysis' requirements. Sentinel Hub is one of such tools.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open-source software architecture that continues to grow in popularity around the world. In late 2021, CEOS released a new ODC sandbox tool that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open-source toolset allows users to run application algorithms using a Google Colaboratory (“Colab”) Python notebook environment [1]. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. One example application has been developed and demonstrated to support flooding disasters and recovery using Sentinel-1 radar data. This application can be easily used to monitor peak flooding extent, flooding recovery, and the time series flood history of a region.

This paper outlines a comparative study where Sentinel-1 Interferometric Wide Swath (IWS) data obtained from two different sources - the Google Earth Engine and the Sentinel Hub by Sinergise - have been compared with respect to their geometric and radiometric characteristics. Assessed over five study sites with different land cover and topographic features - in Australia, Brazil, Ethiopia, Indonesia and the USA - the results indicate comparable absolute geolocation accuracy between the two sets of data. Radiometric performance was also similar within the main (0 - -30 dB) dynamic range of the data.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that continues to gain global popularity through the integration of analysis-ready data (ARD) on cloud computing frameworks. In 2021, CEOS released a new ODC sandbox that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open source toolset allows users to run application algorithms using a Google Colab Python notebook environment. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. Basic operation of the tool will support many users but can also be scaled in size and scope to support enhanced user needs. The creation of the ODC sandbox was prompted by the migration of many CEOS ARD satellite datasets to the cloud. The combination of these datasets in an interoperable data cube framework will inspire the creation of many new application products and advance open science.

The use of satellite Earth Observation (EO) data to make important local, regional, and national decisions has become increasingly important as society works toward collective benefit through global sustainable development frameworks such as the United Nations Sustainable Development Goals. While advancements have been made with respect to core satellite data management and analysis, less progress has been made on providing the broad range of EO stakeholders (including policy makers and citizens) with clear insight into this data and true understanding of the resulting analysis. This paper presents work toward a systematic approach to utilizing visualization inside the Open Data Cube ecosystem. This Open Data Cube Visualization Framework (ODC-Vis) and associated visualization applications can help bridge the gap between EO data, decisions, and development goals by helping viewers understand data and analysis on a more intuitive and engaging level. The ODC Visualization Framework seeks to increase the value of satellite data across the entire EO value-chain. Within the context of the ODC Visualization Framework, this paper also presents several application examples catering to a range of stakeholders. These examples include: (1) ODC-Vis Explorer - an application for interactively exploring the data cube (2) ODC-Vis Story - an application for interactive storytelling, and (3) ODC-Vis VR - a Virtual/Augmented Reality application that supports interactive ODC analysis. As advanced visualization is more broadly used across the ODC community, improved analysis, greater insight, and better advocacy will result.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that supports analysis-ready satellite data packaged into “cubes” to minimize data preparation complexity and take advantage of modern computing for increased value and impact of Earth observation data. Since its inception in 2017, the ODC has fostered a community to develop, sustain and grow the technology and its applications. Such early success has resulted in bold goals for facilitating the implementation of country-level data cubes around the world to achieve a virtual global data cube through connected regional deployments. Advancements in the ODC infrastructure and its associated application algorithms along with a regional demonstration in Africa suggest this goal is achievable. In addition, there are also considerable advancements in the specification, production and use of Analysis Ready Data (ARD), which is the core element of any data cube deployment. Such advancements will optimize the value of ARD for global users by demonstrating increased access and use of diverse datasets, interoperability between datasets, and increased use of machine learning. This paper summarizes the past, present and future of the ODC and ARD initiatives.

There has been a need for African countries to report on United Nations Sustainable Development Goals (SDGs) to keep up with other developed nations. However, there has been a lack of data especially geospatial that can be used by National Statistics Offices (NSOs) to report on SDGs. The Africa Regional Data Cube (ARDC) is part of the Open Data Cube (ODC) and represents an example of multi-stakeholder collaboration and responding to country-level demand. The ARDC was created by the Committee on Earth Observation Satellites in partnership with GPSDD, Office of the Deputy President representing Kenya, Group on Earth Observation (GEO), Strathmore University, and Amazon Web Services. The ARDC offers free and open satellite data to address the SDGs. The ARDC allows users to select and analyze time series Earth Observation datasets to study land change and report on SDGs. In this paper, ARDC countries namely Ghana, Kenya, Senegal, Sierra Leone and Tanzania showcase some of their work in reporting on SDGs 2.4.1, 6.6.1, 11.3.1, 14.5, 15.1.1 and 15.3. Further, the countries have been able to institutionalize ARDC into various technical committees that have helped develop thematic use cases, which contribute and inform country specific policy. The preliminary results are useful for NSOs on reporting on SDGs and contributing to national development agenda including the Africa Union Agenda 2063. Overall, the ADRC provides opportunities for countries in Africa to report on SDGS.

In 2015, all United Nations (UN) Member States adopted the 2030 Agenda for Sustainable Development. The Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The Committee on Earth Observation Satellites (CEOS) Systems Engineering Office (SEO) team has recently developed and released a set of innovative notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. The main contributions in this paper are: (1) briefly describing the framework of the UN SDG notebooks, (2) enumerating the notebooks' salient features, and (3) discussing current limitations and proposing approaches to overcome these limitations.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.

The amounts of satellite data available to the public have increased dramatically the last few years, in particular thanks to the public open release of certain key Earth observation missions, stimulating the development of new applications that use large volumes of data. With an armada of new high-volume satellite missions around the corner - both public and commercial - data producers and data users alike now risk becoming overwhelmed by the sheer amounts of data to handle, and new solutions to data structures, access and processing tools are required. Analysis-Ready Data, Data Cubes and cloud-based processing solutions are responses to new situation. Some examples of the former two are outlined briefly in this paper, highlighting some specific efforts by the Committee on Earth Observation Satellites (CEOS) and the Open Data Cube (ODC) initiative to address these. Some specific efforts relating to Synthetic Aperture Radar (SAR) are outlined.

Over the past several years Analysis Ready Data has been a major focus for the Committee on Earth Observation Satellites (CEOS). The CEOS Analysis Ready Data concept within the context of Land started in 2015 and has been led by the CEOS Land Surface Imaging Virtual Constellation (LSI-VC) community. LSI-VC has developed the definition that CEOS uses for ARD for Land and the overall framework [1]. The CEOS ARD definition identifies that the major benefit of ARD lies in enabling a broad community of non-remote sensing experts to better use CEOS data; and in increasing the interoperability of products through space and time as a foundation for `Big Data' analyses, future data architectures and the application of AI (artificial intelligence and machine learning) methods. LSI-VC also took the responsibility to coordinate the development of the Product Family Specifications (PFS). An important step in the CARD specification approach is that it is not prescriptive with regard to the data processing approach. This recognises that there are generally multiple approaches and that these will evolve through time. Four PFS have been endorsed by the CEOS LSI-VC community during 2019/2020. These endorsements represent the conclusion of over two years of work since the agreement of the CEOS ARD definition & framework at CEOS Plenary in 2016. The endorsed specifications contain inputs from experts around the world and it demonstrates the resolution of various points of view. It also shows the emerging acceptance of the concept of Analysis Ready Data and the value proposition of the CEOS ARD. As the increase of available EO data and processing platforms offer an unprecedented choice for the users, the need for harmonized data is growing [2]. Thus, over the past few years, there has been an increased interest from the private sector in the CEOS ARD initiative as well, and the expectation is that the availability of CEOS ARD datasets from both institutional and private data providers will increase in the coming years. To address the CEOS ARD long-term view and to ensure a consensus between players is achieved, the CEOS ARD strategy was developed in 2019. The strategy recognises that the private industry has different roles to play within the CEOS ARD initiative such as data users, data providers, data hosts or as providers of processing chains to produce data that meet CEOS ARD specifications. The strategy is also focused on the need, prioritization and continuity of CEOS ARD PFS development including the development of ARD specifications for other thematic areas such as ocean and atmosphere. This paper intends to give an update on the current CEOS ARD for Land accomplishments, and what activities are envisaged for the near future.

Users of satellite Earth observation data typically invest a large amount of effort into data preparation which is a major barrier for many global users and limits the impact and utilization of this data around the world. The Committee on Earth Observation Satellites (CEOS) has initiated an effort to define and promote the production and use of Analysis Ready Data (ARD) to lower the burden on global users and increase the use and impact of its data. Through the Open Data Cube (ODC) initiative and its demonstration in Africa, ARD is making an impact on this developing region of the world. This paper discusses how this data and the associated ODC tools are being used to address water extent, land change and urbanization, which are key application areas aligned with the United Nations Sustainable Development Goals (SDG). Through the ODC, the impact of ARD is growing rapidly around the world and our ability to address local and national decision-making needs will continue to improve.

The Open Data Cube (ODC) initiative seeks to increase the value and impact of global Earth observation satellite data by providing an open and freely accessible data exploitation architecture and to foster a community to develop, sustain, and grow the technology and the breadth and depth of its applications for societal benefit. Successful ODC implementations exist in Australia, Colombia, Switzerland and Taiwan and are under development or consideration in another 39 countries. Since its inception in early 2017, the ODC has made significant progress in the advancement of open source software tools and algorithms that support the deployment and operation of data cubes on local or cloud computing systems around the world. With the increased interest in data cubes, the ODC is poised to become a world leader in open source data cube technologies to enhance the value of satellite data for all users.

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia's WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS.

For many land monitoring applications using remote sensing, lack of data is no longer an issue, as it may have been in the past. Programs, such as Copernicus by the European Commission and the Landsat Missions by the United States Geological Survey, have adopted systematic acquisition strategies, and distribute vast amounts of satellite data under open licenses. In parallel, storage and computing capability have evolved to make it cost-effective and practical to process and analyze these data at various scales. Data architecture solutions, such as the Open Data Cube (ODC) and the Copernicus Data and Information Access Services (DIAS), are providing frameworks that make [scientific] analysis much simpler and straightforward. However, enabling non-expert users without the expertise and/or computation resources to pre-process and store low-level data products in order to exploit these capabilities, has proven more challenging. The Committee on Earth Observation Satellites (CEOS1) is working to address this challenge through the CEOS Analysis Ready Data for Land (CARD4L) initiative [1]. CARD4 L is foreseen to enable users to access satellite data products that are `ready to use' for a wide range of land applications. Moreover, CARD4L aims to enable non-expert users access to products that have been processed `far enough' to be suitable for immediate analysis for a range of applications, while ensuring they are not too specific to only be used for particular topics or areas. CARD4L will be an important enabler of the Open Data Cube (ODC) initiative [2]. Through CARD4L, users will be able to easily locate products that are suitable for ingestion into Data Cubes [3], and will have confidence that these different CARD4 L products will limit as far as possible barriers to interoperability.

There is an urgent need to increase the capacity of developing countries to take part in the study and monitoring of their environments through remote sensing and space-based Earth observation technologies. The Open Data Cube (ODC) provides a mechanism for efficient storage and a powerful framework for processing and analyzing satellite data. While this is ideal for scientific research, the expansive feature space can also be daunting for end-users and decision-makers who simply require a solution which provides easy exploration, analysis, and visualization of Analysis Ready Data (ARD). Utilizing innovative web-design and a modular architecture, the Committee on Earth Observation Satellites (CEOS) has created a web-based user interface (UI) which harnesses the power of the ODC yet provides a simple and familiar user experience: the CEOS Data Cube (CDC). This paper presents an overview of the CDC architecture and the salient features of the UI. In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The fully-customizable source code of the UI is available at our public repository. Interested parties can download the source and build their own UIs. The UI empowers users by providing features that assist with streamlining data preparation, data processing, data visualization, and sub-setting ARD products in order to achieve a wide variety of Earth imaging objectives through an easy to use web interface.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using Amazon Web Services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives.

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

The Committee on Earth Observing Satellites (CEOS) Visualization Environment (COVE) tool is a browser-based application that leverages Google Earth web to display satellite sensor coverage areas. The analysis tool can also be used to identify near simultaneous surface observation locations for two or more satellites. The National Aeronautics and Space Administration (NASA) CEOS System Engineering Office (SEO) worked with the CEOS Working Group on Calibration and Validation (WGCV) to develop the COVE tool. The CEOS member organizations are currently operating and planning hundreds of Earth Observation (EO) satellites. Standard cross-comparison exercises between multiple sensors to compare near-simultaneous surface observations and to identify corresponding image pairs are time-consuming and labor-intensive. COVE is a suite of tools that have been developed to make such tasks easier.