Improving geographic information retrieval in spatial data infrastructures

Andrade, Fabio Gomes de; Baptista, Cláudio de Souza; Davis, Clodoveu A.

doi:10.1007/s10707-014-0202-x

Cited by 21 publications

(14 citation statements)

References 29 publications

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“…In order to assign a proper weight to each part, an approach proposed by Andrade [52] is adopted. Each weight is calculated using the Pearson correlation coefficient based on the training set, which contains result samples of weights from several spatial queries.…”

Section: Final Semantic Similaritymentioning

confidence: 99%

A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard

Wang

et al. 2017

Sustainability

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Social media is valuable in propagating information during disasters for its timely and available characteristics nowadays, and assists in making decisions when tagged with locations. Considering the ambiguity and inaccuracy in some social data, additional authoritative data are needed for important verification. However, current works often fail to leverage both social and authoritative data and, on most occasions, the data are used in disaster analysis after the fact. Moreover, current works organize the data from the perspective of the spatial location, but not from the perspective of the disaster, making it difficult to dynamically analyze the disaster. All of the disaster-related data around the affected locations need to be retrieved. To solve these limitations, this study develops a geo-event-based geospatial information service (GEGIS) framework and proceeded as follows: (1) a geo-event-related ontology was constructed to provide a uniform semantic basis for the system; (2) geo-events and attributes were extracted from the web using a natural language process (NLP) and used in the semantic similarity match of the geospatial resources; and (3) a geospatial information service prototype system was designed and implemented for automatically retrieving and organizing geo-event-related geospatial resources. A case study of a typhoon hazard is analyzed here within the GEGIS and shows that the system would be effective when typhoons occur.

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Section: Final Semantic Similaritymentioning

confidence: 99%

A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard

Wang

et al. 2017

Sustainability

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“…cloud services, as well as grid technology are described in [12] on the example of a project of the European Commission "enviroGRIDS". In [11] there is a suggestion of enhancing standard cataloguing services in SDI with semantic elements, while in [9] -a semantic development of cataloguing services in SDI for a region in Italy.…”

Section: Spatial Data Infrastructuresmentioning

confidence: 99%

The Applicability Of SWE In Polish Spatial Data Infrastructures - The Example Of The SensorML Language

Rossa

Rogulski

2015

Foundations of Computing and Decision Sciences

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Abstract. Mobile and stationary sensors currently used to measure various environmental parameters, functioning independently or as part of monitoring networks and measurement stations, provide vast amounts of data on the state and quality of the environment on the Earth. If the data is to be used effectively, they must be exchanged and shared among IT systems. Systems which offer services of searching, exchange, sharing, visualisation and analysis of dispersed and varied data resources on the widely understood environment are, for example, spatial data infrastructures.The article presents an overview of IT technologies and standards which offer interoperability in spatial data infrastructures. It first defines interoperability and then describes the most important issues connected with spatial data infrastructures on the example of INSPIRE. An example standard which facilitates interoperability in INSPIRE is the SensorML language, a component of Sensor Web Enablement (SWE). Its practical application is proposed -for description of processes of air monitoring in a spatial data infrastructure that is an element of the Polish national environmental monitoring plan.

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“…These three methods were used for geographic information retrieval and are not suitable for assessing the similarity between geospatial data and geospatial data. Andrade et al [18] proposed several similarity metrics to solve spatial, semantic, and temporal queries and combined them by a weighted sum method. Al-Bakri and Fairbairn [24] measured semantic, structural, and data type similarities between categories of formal data and volunteered geographic information (VGI) and obtained the overall similarity based on a weighted sum combination of these three measures.…”

Section: Introductionmentioning

confidence: 99%

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

Chen

Song

Yang

2018

IJGI

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Abstract:To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear.

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Improving geographic information retrieval in spatial data infrastructures

Cited by 21 publications

References 29 publications

A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard

A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard

The Applicability Of SWE In Polish Spatial Data Infrastructures - The Example Of The SensorML Language

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

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