The increasing availability and accessibility of commercial satellite data presents a unique opportunity for the Earth observation (EO) community to leverage new sources of remote sensing imagery. However, despite improved access to data and computation, conducting analyses with data from multiple platforms remains challenging due to interoperability barriers. Calibration and Validation (Cal/Val) efforts are essential to overcoming these obstacles by ensuring the quality and reliability of EO data and quantifying necessary adjustments for seamless integration. The Committee on Earth Observation Satellites (CEOS) Systems Engineering Office (SEO) has launched the CEOS Analytics Lab (CAL), an Open Data Cube (ODC)-based tool, to facilitate data processing and collaboration among researchers. In this paper, we present a pilot study using tools developed for CAL to compare commercial imagery to traditional civil government Earth observations. We demonstrate the usefulness of collaborative environments such as CAL and provide the community with tools which assist with commonly identified calibration and validation tasks necessary to perform interoperability studies. This demonstration lays a foundation for operations around the use and evaluation of commercial satellite data, supporting further developments in the CEOS and Open EO communities. We also present a set of tools, CalValToolbox, to the EO community to perform Cal/Val tasks.

Generative artificial intelligence (AI), specifically Large Language Models (LLMS) such as generative pretrained transformers (GPTS) have emerged as a transformational technology in modern research and applications. This technology will revolutionize a wide variety of fields, including remote sensing and Earth observation. We explore the use of LLMS to improve and automate EO analysis, making these results more accessible to a broad range of decision makers. We develop a framework using this technology to support the Committee on Earth Observation Satellites (CEOS) and NASA’s Open Science initiative. This framework leverages current work on Earth observation tools, including the Open Data Cube (ODC) [1] and the OEA Algorithm Hub (AlgoHub). [2]In our work, we investigate the ability of large language models to orchestrate and reason with tools created to access geospatial data using the Open Data Cube and the OEA Algorithm Hub [2]. In this paper we propose a natural language analytical framework for orchestrating Earth observation (EO) analysis. The framework uses large language models to interpret an analysis request, devise a solution based on ODC and AlgoHub tools, and execute the analysis. Our experimental investigations demonstrate that an LLM, with its embedded contextual knowledge, can effectively discern and sequence the necessary processing steps required to fulfill Earth observation analysis requests. This research demonstrates the potential of Generative AI (LLMs) in streamlining and enhancing EO analyses and empowering the observation community.In summary, Generative AI (LLMs) can enhance the capabilities of Earth observation analysis by automating tasks, providing natural language interfaces, and facilitating the extraction of valuable insights from the vast and complex datasets generated by remote sensing and EO technologies.

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.

Riparian and shoreline buffers are vital landscape elements of coastal and riverine shorelines, providing significant ecosystem services, habitats, and aesthetic qualities. One critical role is the filtration and uptake of non-point source pollutants, particularly in estuaries that have seen extensive land cover change, loss of wetlands, and that face sea-level rise such as the Chesapeake Bay. Combining a remote sensing and coastal ecological approach, this project presents data science tools to investigate coastal riparian and shoreline vegetation relationships with in situ water quality sampling. This project provides tools and examples for quantifying riparian buffer vegetation, calculating Total Suspended Matter (TSM) from Earth Observation (EO) data, and demonstrates the use of data science methods to explore relationships between non-point source pollutants.

Collecting data via small Unmanned Aerial Systems (UASs) can fill a niche amongst geospatial data collection methods by providing recent, very high-resolution imagery, at a fraction of the cost of space-based providers. We detail an end-to-end system for acquiring, processing, and importing data into the Open Data Cube (ODC), a free and open-source geospatial data management and analysis platform. The process employs third party software that can be low-cost or free. The acquisition component of our pipeline consists of low-cost drones, either assembled with commodity components, or available from retail stores to enable the community at large to better take advantage of UASs as a reliable data collection platform. We discuss a selection of third-party photogrammetry suites which output assembled images into tiles that can be indexed by the ODC. Finally, we demonstrate the indexing of orthographic drone data into the Open Data Cube.

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.

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.

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.