Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring

Kuldeep Kurte; Abhishek Potnis; Surya Durbha

doi:10.1145/3356395.3365545

Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring

Kuldeep Kurte, Abhishek Potnis, Surya Durbha

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatio-temporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatio-temporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatio-temporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatio-temporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatio-temporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t₁ have been demonstrated with promising results.

Original language	English
Title of host publication	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019
Editors	Bandana Kar, Olufemi A. Omitaomu, Xinyue Ye, Shima Mohebbi, Guangtao Fu
Publisher	Association for Computing Machinery, Inc
Pages	41-50
Number of pages	10
ISBN (Electronic)	9781450369541
DOIs	https://doi.org/10.1145/3356395.3365545
State	Published - Nov 5 2019
Externally published	Yes
Event	2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 - Chicago, United States Duration: Nov 5 2019 → …

Publication series

Name	Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019

Conference

Conference	2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019
Country/Territory	United States
City	Chicago
Period	11/5/19 → …

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No.DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. ARIC’19, November 5–8, 2019, Chicago, IL, USA © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-6954-1/19/11...$15.00 https://doi.org/10.1145/3356395.3365545

Keywords

Discrete mereotopology
Flood disaster
Ontology
Semantics
Spatial relations
Spatio-temporal

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10.1145/3356395.3365545

Cite this

Kurte, K., Potnis, A., & Durbha, S. (2019). Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring. In B. Kar, O. A. Omitaomu, X. Ye, S. Mohebbi, & G. Fu (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 (pp. 41-50). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3356395.3365545

Kurte, Kuldeep ; Potnis, Abhishek ; Durbha, Surya. / Semantics-enabled spatio-temporal modeling of earth observation data : An application to flood monitoring. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. editor / Bandana Kar ; Olufemi A. Omitaomu ; Xinyue Ye ; Shima Mohebbi ; Guangtao Fu. Association for Computing Machinery, Inc, 2019. pp. 41-50 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019).

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title = "Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring",

abstract = "Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatio-temporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatio-temporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatio-temporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatio-temporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatio-temporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.",

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author = "Kuldeep Kurte and Abhishek Potnis and Surya Durbha",

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Kurte, K, Potnis, A & Durbha, S 2019, Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring. in B Kar, OA Omitaomu, X Ye, S Mohebbi & G Fu (eds), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019, Association for Computing Machinery, Inc, pp. 41-50, 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019, Chicago, United States, 11/5/19. https://doi.org/10.1145/3356395.3365545

Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring. / Kurte, Kuldeep; Potnis, Abhishek; Durbha, Surya.
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. ed. / Bandana Kar; Olufemi A. Omitaomu; Xinyue Ye; Shima Mohebbi; Guangtao Fu. Association for Computing Machinery, Inc, 2019. p. 41-50 (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatio-temporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatio-temporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatio-temporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatio-temporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatio-temporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

AB - Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatio-temporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatio-temporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatio-temporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatio-temporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatio-temporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

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Kurte K, Potnis A, Durbha S. Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring. In Kar B, Omitaomu OA, Ye X, Mohebbi S, Fu G, editors, Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019. Association for Computing Machinery, Inc. 2019. p. 41-50. (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019). doi: 10.1145/3356395.3365545

Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring

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