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Space-time Prediction of High Resolution Raster Data: An Approach based on Spatio-temporal Bayesian Network (STBN)

Authors:

Soumya K. GhoshAuthors Info & Claims

CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

Pages 129 - 135

https://doi.org/10.1145/3297001.3297017

Published: 03 January 2019 Publication History

Abstract

Prediction of spatial raster time series, especially those obtained from satellite remote sensing imagery, plays a crucial role in monitoring various complex spatio-temporal processes, such as urban growth, deforestation, flooding, and so on. With the recent advancement of remote sensing technology, the availability of such raster data with high spatial/temporal density has increased exponentially. However, efficiently extracting complex spatio-temporal relationships by proper utilization of these enormous volumes of data is a critical issue which imposes significant challenge in timely processing and prediction of such spatio-temporal data. This paper proposes spatio-temporal Bayesian network (STBN) which is able to efficiently model/capture the temporal dynamics of spatial dependency among variables and eventually helps accelerating the spatial time series prediction process. The performance of the proposed STBN has been evaluated in comparison with several state-of-the-art prediction models and baseline techniques. Overall, the proposed STBN-based prediction model is found to show improved accuracy with considerably reduced computational cost.

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Cited By

Fang YPei TSong CChen JWang XChen XLiu Y(2023)A kriging interpolation model for geographical flowsInternational Journal of Geographical Information Science10.1080/13658816.2023.224850237:10(2150-2174)Online publication date: 23-Aug-2023
https://doi.org/10.1080/13658816.2023.2248502
Das M(2021)Real-time prediction of spatial raster time series: a context-aware autonomous learning modelJournal of Real-Time Image Processing10.1007/s11554-021-01099-718:5(1591-1605)Online publication date: 9-Apr-2021
https://dl.acm.org/doi/10.1007/s11554-021-01099-7
Das MGhosh SDas MGhosh S(2019)Summary and Future ResearchEnhanced Bayesian Network Models for Spatial Time Series Prediction10.1007/978-3-030-27749-9_9(137-142)Online publication date: 8-Nov-2019
https://doi.org/10.1007/978-3-030-27749-9_9
Show More Cited By

Index Terms

Space-time Prediction of High Resolution Raster Data: An Approach based on Spatio-temporal Bayesian Network (STBN)
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Other conferences

CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

January 2019

380 pages

ISBN:9781450362078

DOI:10.1145/3297001

General Chairs:
Lipika Dey
TCS Innovation Labs
,
Surajit Chaudhury
Microsoft Research
,
Program Chairs:
Raghu Krishnapuram
Robert Bosch Center, IISc Bangalore
,
Parag Singla
IIT Delhi
,
Publications Chair:
Rishiraj Saha Roy
Max Planck Institute for Informatics

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 January 2019

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CoDS-COMAD '19

CoDS-COMAD '19: 6th ACM IKDD CoDS and 24th COMAD

January 3 - 5, 2019

Kolkata, India

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CODS-COMAD '19 Paper Acceptance Rate 62 of 198 submissions, 31%;

Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

Fang YPei TSong CChen JWang XChen XLiu Y(2023)A kriging interpolation model for geographical flowsInternational Journal of Geographical Information Science10.1080/13658816.2023.224850237:10(2150-2174)Online publication date: 23-Aug-2023
https://doi.org/10.1080/13658816.2023.2248502
Das M(2021)Real-time prediction of spatial raster time series: a context-aware autonomous learning modelJournal of Real-Time Image Processing10.1007/s11554-021-01099-718:5(1591-1605)Online publication date: 9-Apr-2021
https://dl.acm.org/doi/10.1007/s11554-021-01099-7
Das MGhosh SDas MGhosh S(2019)Summary and Future ResearchEnhanced Bayesian Network Models for Spatial Time Series Prediction10.1007/978-3-030-27749-9_9(137-142)Online publication date: 8-Nov-2019
https://doi.org/10.1007/978-3-030-27749-9_9
Das MGhosh SDas MGhosh S(2019)Spatial Time Series Prediction Using Advanced BN Models—An Application PerspectiveEnhanced Bayesian Network Models for Spatial Time Series Prediction10.1007/978-3-030-27749-9_8(125-136)Online publication date: 8-Nov-2019
https://doi.org/10.1007/978-3-030-27749-9_8

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