A formal model to infer geographic events from sensor observations

Devaraju, Anusuriya; Kuhn, Walther; Renschler, Chris S.

doi:10.1080/13658816.2014.933480

Cited by 21 publications

(9 citation statements)

References 48 publications

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“…Many previous works have discussed why ontology is needed to solve decision making problems in disaster prediction systems. Devaraju et al [7] presented an ontology-based approach to infer geographical events from sensor observations, by exploiting the ontological vocabularies with reasoning and querying mechanisms. However, this approach does not handle the data integration issue especially the volume and the variety of data.…”

Section: B Related Work On Ontology-based Prediction Systemsmentioning

confidence: 99%

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Masmoudi

Taktak

Lamine³

et al. 2018

2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)

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The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

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Section: B Related Work On Ontology-based Prediction Systemsmentioning

confidence: 99%

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Masmoudi

Taktak

Lamine³

et al. 2018

2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)

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“…Geospatial big data has become a focus of spatio-temporal analysis in the past few years, because of the large amounts of data being acquired by new sensors and new data sources such as social media, GNSStrajectories gathered by mobile phones (Sester et al 2014, Hahmann andBurghardt 2013) and sensor networks (Devaraju et al 2015). The data collected with such systems has the properties of being abundant, streaming, continuously changing, mostly geo-referenced, and being (partially) unstructured.…”

Section: Spatial Data Modelling and Analysismentioning

confidence: 99%

Information from imagery: ISPRS scientific vision and research agenda

Chen

Dowman

et al. 2016

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The Semantic Web has been used for queries by a user for natural events using observation sensor data (Devaraju et al, 2015) (Yu and Liu, 2013). In particular Devaraju et al (2015) describe a number of ontologies used to model various sensors and rules used to map queries such as flooding in an area to the need to sample a number of point water sensors.…”

Section: Background and Related Researchmentioning

confidence: 99%

Automating Government Spatial Transactions

Varadharajulu

West

McMeekin

et al. 2016

Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management

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The land development approval process between local authorities and government land and planning departments is manual, time consuming and resource intensive. For example, when new land subdivisions, new roads and road naming, and administrative boundary changes are requested, approval and changes to spatial datasets are needed. The land developer submits plans, usually on paper, and a number of employees use rules, constraints and policies to determine if such plans are acceptable. This paper presents an approach using Semantic Web and Artificial Intelligence techniques to automate the decision-making process in Australian jurisdictions. Feedback on the proposed plan is communicated to the land developer in real-time, thus reducing process handling time for both developer and the government agency. The Web Ontology Language is used to represent relationships between different entities in the spatial database schema. Rules on geometry, policy, naming conventions, standards and other aspects are obtained from government policy documents and subject-matter experts and described using the Semantic Web Rule Language. Then when the developer submits an application, the software checks the rules against the request for compliance. This paper describes the proposed approach and presents a case study that deals with new road proposals and road name approvals.

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A formal model to infer geographic events from sensor observations

Cited by 21 publications

References 48 publications

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Information from imagery: ISPRS scientific vision and research agenda

Automating Government Spatial Transactions

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