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.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observations, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions.Since 2021, NASA’s Advanced Information Systems Technology (AIST) program has invested in two ESDT efforts to tackle the impacts of our changing climate. The establishment of ESDT for flood and air quality enabled our teams to formalize the software framework. The open-source framework is called the Integrated Digital Earth Analysis System (IDEAS). By working with the Apache Science Data Analytics Platform (SDAP) community, IDEAS is now a subproject of SDAP. The paper presents the ongoing development of IDEAS and its current applications.

Since Al Gore originally pitched his vision for a Digital Earth in 1999, we’ve made significant progress towards its realization. Earth observation (EO) data is no longer shared in a raw form, it is processed to agreed analysis ready data specifications and made available at a global scale. The cloud optimized geotiff has emerged as the predominate data format for storing EO data and the spatio-temporal asset catalog specification has been broadly adopted, providing a common method of describing and discovering available data. This combination of technologies and capabilities represents a new paradigm for working with the vast collections of data now accessible on the public cloud and is referred to as cloud native geospatial.This paper covers background concepts relating to cloud native geospatial, then charts the history of some Digital Earth programs and places them in context with the new availability of global analysis ready data collections. It then provides some examples of how this new capability can be used to enable more people to access more data more easily.

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.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA's Advanced Information Systems Technology (AIST)'s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)'s FloodDAM project and the Centre National d'Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.