TY - GEN
T1 - A graph based multimodal geospatial interpolation framework
AU - Tang, Mengfan
AU - Agrawal, Pranav
AU - Nie, Feiping
AU - Pongpaichet, Siripen
AU - Jain, Ramesh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/25
Y1 - 2016/8/25
N2 - 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.
AB - 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.
KW - Air pollution
KW - Graph based model
KW - Interpolation
KW - Multimodal data
KW - Spatial Gaussian Process
KW - Spectral analysis
UR - https://www.scopus.com/pages/publications/84987653365
U2 - 10.1109/ICME.2016.7552968
DO - 10.1109/ICME.2016.7552968
M3 - Conference contribution
AN - SCOPUS:84987653365
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PB - IEEE Computer Society
T2 - 2016 IEEE International Conference on Multimedia and Expo, ICME 2016
Y2 - 11 July 2016 through 15 July 2016
ER -