Abstract
Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies.
Highlights
The history of earth observation data, and evolution of information technology and the internet have created a transition of scene-based and project-based thinking to imagery as a seamless time series
To perform analysis on data cubes, we developed efficient search, parallel read/write, and distributed computation capabilities, which can be deployed to an on-premise private cluster, or in commercial cloud such as Amazon or Azure
We described described here here aa platform platform for for accessing, accessing, analyzing, analyzing, and and sharing sharing earth earth observation observation imagery imagery from a variety of data cubes
Summary
The history of earth observation data, and evolution of information technology and the internet have created a transition of scene-based and project-based thinking to imagery as a seamless time series. Killough (2018) described a goal of the CEOS Open Data Cube as increased global impact of satellite data. To achieve this goal, we see four requirements, some already in process, all still evolving. The first three decades of Landsat and the broader earth observation community were dominated by specialists working on individual projects often on individual satellite scenes of a single date or a few dates from a single senor. In 2009, just before the advent of geospatial cloud computing, the U.S government made all Landsat available
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