A graph based multimodal geospatial interpolation framework | IEEE Conference Publication | IEEE Xplore

A graph based multimodal geospatial interpolation framework


Abstract:

Recent multimedia research has increasingly focused on large scale multimodal data from disparate geospatial sensors. In addition to the volume of the data, the diversity...Show More

Abstract:

Recent multimedia research has increasingly focused on large scale multimodal data from disparate geospatial sensors. In addition to the volume of the data, the diversity and granularity of the data poses a major challenge in extracting meaningful and actionable information. To address this, we present a novel spatial interpolation framework, capable of incorporating multimodal data sources and modeling the spatial processes comprehensively at multiple resolutions. The framework transforms the spatial interpolation problem into a graph structure learning problem, based on the latent structure of the data. This enables more efficient and accurate predictions at unobserved locations. We demonstrate the effectiveness of our approach by testing it on air pollution interpolation.
Date of Conference: 11-15 July 2016
Date Added to IEEE Xplore: 29 August 2016
ISBN Information:
Electronic ISSN: 1945-788X
Conference Location: Seattle, WA, USA

References

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