GSio: A programmatic interface for delivering Big Earth data-as-a-service

Larraondo, Pablo Rozas; Pringle, Sean; Jian, Guo; Antony, Joseph; Evans, Ben

doi:10.1080/20964471.2017.1397898

Cited by 2 publications

References 23 publications

(20 reference statements)

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Achieving the Full Vision of Earth Observation Data Cubes

Kopp

Becker

Doshi

et al. 2019

Data

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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.

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Achieving the Full Vision of Earth Observation Data Cubes

Kopp

Becker

Doshi

et al. 2019

Data

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Harnessing the Power of Geospatial Data

Ahmad,

Ali,

Tin

et al. 2024

Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models

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This chapter explored the transformative impact of cloud computing on geospatial data management, highlighting its scalability, cost-efficiency, and security features. This exploration adopted a qualitative method based on a literature review, systematically analyzing existing works to gain insights into the qualitative dimensions of the evolving intersection between cloud computing and geospatial data management. It detailed the integration of parallel processing, GIS platforms, machine learning, and data visualization within the digital landscape, fostering innovation. The narrative extended to include emerging technologies like edge computing, blockchain, AR/VR, and geospatial data marketplaces, giving rise to a groundbreaking geospatial data as a service (DaaS) model. Emphasizing the cloud's pivotal role in handling geospatial big data, the chapter outlined capabilities in parallel processing, GIS orchestration, machine learning integration, and disaster recovery.

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GSio: A programmatic interface for delivering Big Earth data-as-a-service

Cited by 2 publications

References 23 publications

Achieving the Full Vision of Earth Observation Data Cubes

Achieving the Full Vision of Earth Observation Data Cubes

Harnessing the Power of Geospatial Data

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