Overcoming the semantic and other barriers to GIS interoperability

Bishr, Yaser

doi:10.1080/136588198241806

Cited by 347 publications

(175 citation statements)

References 10 publications

Supporting

Mentioning

149

Contrasting

Unclassified

Order By: Relevance

“…Ahlqvist writes that “to negotiate and compare information stemming from different classification systems (Bishr, 1998; Mizen, Dolbear, & Hart, 2005)… a translation can be achieved by matching the concepts in one system with concepts in another , either directly or through an intermediate classification (Feng & Flewelling, 2004; Kavouras & Kokla, 2002)” (Ahlqvist, 2005). Stehman describes four common types of thematic map-pair comparisons (Stehman, 1999).…”

Section: Original Hybrid Eight-step Guideline For Identification Of Amentioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

et al. 2018

View full text Add to dashboard Cite

ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2—Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

show abstract

Section: Original Hybrid Eight-step Guideline For Identification Of Amentioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Data sources can be heterogeneous in syntax, schema, or semantics, thus making data interoperation a difficult task [6]. Syntactic heterogeneity is caused by the use of different models or languages.…”

Section: Data Heterogeneitymentioning

confidence: 99%

Semantic Integration of Collection Description

Lourdi¹,

Papatheodorou²,

Doerr³

2009

D-Lib Magazine

View full text Add to dashboard Cite

In this paper, we discuss the use of ontologies for data integration. We consider two different settings depending on the system architecture: central and peer-to-peer data integration. Within those settings, we discuss five different cases studies that illustrate the use of ontologies in metadata representation, in global conceptualization, in high-level querying, in declarative mediation, and in mapping support. Each case study is described in detail and accompanied by examples.

show abstract

“…Difference in semantics used in different data sources is one of the major problems in spatial data sharing and data interoperability [6,16]. One possible approach to overcome the problem of semantic heterogeneity is by means of ontology [18,31,32].…”

Section: Rdf-based Data Integrationmentioning

confidence: 99%

Ontology-Based Geospatial Data Query and Integration

Zhao

Zhang

Wei

et al. 2008

Geographic Information Science

View full text Add to dashboard Cite

Abstract. Geospatial data sharing is an increasingly important subject as large amount of data is produced by a variety of sources, stored in incompatible formats, and accessible through different GIS applications. Past efforts to enable sharing have produced standardized data format such as GML and data access protocols such as Web Feature Service (WFS). While these standards help enabling client applications to gain access to heterogeneous data stored in different formats from diverse sources, the usability of the access is limited due to the lack of data semantics encoded in the WFS feature types. Past research has used ontology languages to describe the semantics of geospatial data but ontology-based queries cannot be applied directly to legacy data stored in databases or shapefiles, or to feature data in WFS services. This paper presents a method to enable ontology query on spatial data available from WFS services and on data stored in databases. We do not create ontology instances explicitly and thus avoid the problems of data replication. Instead, user queries are rewritten to WFS getFeature requests and SQL queries to database. The method also has the benefits of being able to utilize existing tools of databases, WFS, and GML while enabling query based on ontology semantics.

show abstract

Overcoming the semantic and other barriers to GIS interoperability

Cited by 347 publications

References 10 publications

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

Semantic Integration of Collection Description

Ontology-Based Geospatial Data Query and Integration

Contact Info

Product

Resources

About