STARE INTO THE FUTURE OF GEODATA INTEGRATIVE ANALYSIS

Area of Science:

• Earth Science
• Data Integration
• Geospatial Analysis

Background:

• Diverse Earth Science observations (e.g., visible-infrared imagery, radar) have unique strengths for scientific modeling.
• Integrating heterogeneous data, including cross-calibrations between sensors on different platforms, is crucial but complex.
• Current data integration methods are often customized, tedious, and do not scale with data volume or variety.

Purpose of the Study:

• To address the challenges of scalably integrating diverse Earth Science data.
• To propose an alternative to conventional rectilinear geo-partitioning and floating-point longitude-latitude (lon-lat) encoding.
• To enable efficient organization and analysis of low-level, spatiotemporally irregular data.

Main Methods:

• Introduced the SpatioTemporal Adaptive Resolution Encoding (STARE) as a novel geo-spatiotemporal encoding system.
• Demonstrated STARE's ability to transcend traditional file structures and native array indexing.
• Utilized STARE for organizing diverse data on scalable, distributed computing and storage platforms.

Main Results:

• STARE provides a common geospatial semantics, overcoming limitations of rectilinear partitioning and lon-lat encoding.
• The encoding facilitates the organization of diverse data, including irregular, low-level observations.
• This approach enables scalable integration and analysis of Earth Science data.

Conclusions:

• STARE offers a scalable and efficient method for integrating diverse Earth Science data.
• The encoding overcomes the limitations of traditional data organization, enabling better scientific insights.
• This advancement supports the development of more robust scientific models and hypotheses through integrated data analysis.