@inproceedings{1281c1ea35e54ef18ac088474e4b8f42,
title = "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 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.",
keywords = "Air pollution, Graph based model, Interpolation, Multimodal data, Spatial Gaussian Process, Spectral analysis",
author = "Mengfan Tang and Pranav Agrawal and Feiping Nie and Siripen Pongpaichet and Ramesh Jain",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 ; Conference date: 11-07-2016 Through 15-07-2016",
year = "2016",
month = aug,
day = "25",
doi = "10.1109/ICME.2016.7552968",
language = "英语",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2016 IEEE International Conference on Multimedia and Expo, ICME 2016",
}