Abstract:Today, there is a huge amount of data gathered about the Earth, not only from new spatial information systems, but also from new and more sophisticated data collection technologies. This scenario leads to a number of interesting research challenges, such as how to integrate geographic information of different kinds. The basic motivation of this paper is to introduce a GIS architecture that can enable geographic information integration in a seamless and flexible way based on its semantic value and regardless of… Show more
“…The harmonization of ontologies and the comparison of thematic maps with different legends are the subject of research of a minor body of literature, e.g., refer to works in ontology-driven geographic information systems (ODGIS) (Fonseca, Egenhofer, Agouris, & Camara, 2002; Guarino, 1995; Sowa, 2000). 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).…”
Section: Original Hybrid Eight-step Guideline For Identification Of Amentioning
confidence: 99%
“…Rather, the vice versa holds: if ESA EO Level 2 product generation is accomplished, then NASA EO Level 2 product generation is also fulfilled.10.1080/23312041.2018.1467357-F0002Figure 2.Graphical representation of a dependence relationship part-of , denoted with symbol “→” pointing from the supplier to the client in agreement with the standard Unified Modeling Language (UML) for graphical modeling of object-oriented software (Fowler, 2003), between computer vision (CV), whose special case is EO image understanding (EO-IU) in operating mode, where relationship subset-of , denoted with symbol “⊃” meaning specialization with inheritance from the superset to the subset, holds true, and a Global Earth Observation System of Systems (GEOSS) (GEO, 2005), such that “NASA EO Level 2 product → ESA EO Level 2 product ⊂ EO-IU in operating mode ⊂ CV → GEOSS”. Synonym of 4D spatio-temporal scene from (2D) image reconstruction and understanding, vision is acknowledged to be a cognitive problem very difficult to solve because: (i) non-polynomial (NP)-hard in computational complexity (Frintrop, 2011; Tsotsos, 1990), (ii) inherently ill-posed in the Hadamard sense, as it is affected by: (I) a 4D-to-2D data dimensionality reduction from the scene- to the image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap from ever-varying sub-symbolic sensory data (sensations) in the image-domain to stable symbolic percepts in the modeled world (mental world, world ontology, world model) (Fonseca et al, 2002; Laurini & Thompson, 1992; Matsuyama & Hwang, 1990; Sonka et al, 1994; Sowa, 2000). A NASA Earth observation (EO) Level 2 product, defined as “a data-derived geophysical variable at the same resolution and location as Level 1 source data” (NASA 2016b), is part-of the ESA EO Level 2 product, defined as follows (ESA, 2015; DLR & VEGA, 2011): (a) a single-date multi-spectral (MS) image whose digital numbers (DNs) are radiometrically calibrated into surface reflectance (SURF) values corrected for atmospheric, adjacency and topographic effects, stacked with (b) its data-derived general-purpose, user- and application-independent scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow (CNES, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Synonym of 4D spatio-temporal scene from (2D) image reconstruction and understanding, vision is acknowledged to be a cognitive problem very difficult to solve because: (i) non-polynomial (NP)-hard in computational complexity (Frintrop, 2011; Tsotsos, 1990), (ii) inherently ill-posed in the Hadamard sense, as it is affected by: (I) a 4D-to-2D data dimensionality reduction from the scene- to the image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap from ever-varying sub-symbolic sensory data (sensations) in the image-domain to stable symbolic percepts in the modeled world (mental world, world ontology, world model) (Fonseca et al, 2002; Laurini & Thompson, 1992; Matsuyama & Hwang, 1990; Sonka et al, 1994; Sowa, 2000). A NASA Earth observation (EO) Level 2 product, defined as “a data-derived geophysical variable at the same resolution and location as Level 1 source data” (NASA 2016b), is part-of the ESA EO Level 2 product, defined as follows (ESA, 2015; DLR & VEGA, 2011): (a) a single-date multi-spectral (MS) image whose digital numbers (DNs) are radiometrically calibrated into surface reflectance (SURF) values corrected for atmospheric, adjacency and topographic effects, stacked with (b) its data-derived general-purpose, user- and application-independent scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow (CNES, 2015).…”
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.
“…The harmonization of ontologies and the comparison of thematic maps with different legends are the subject of research of a minor body of literature, e.g., refer to works in ontology-driven geographic information systems (ODGIS) (Fonseca, Egenhofer, Agouris, & Camara, 2002; Guarino, 1995; Sowa, 2000). 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).…”
Section: Original Hybrid Eight-step Guideline For Identification Of Amentioning
confidence: 99%
“…Rather, the vice versa holds: if ESA EO Level 2 product generation is accomplished, then NASA EO Level 2 product generation is also fulfilled.10.1080/23312041.2018.1467357-F0002Figure 2.Graphical representation of a dependence relationship part-of , denoted with symbol “→” pointing from the supplier to the client in agreement with the standard Unified Modeling Language (UML) for graphical modeling of object-oriented software (Fowler, 2003), between computer vision (CV), whose special case is EO image understanding (EO-IU) in operating mode, where relationship subset-of , denoted with symbol “⊃” meaning specialization with inheritance from the superset to the subset, holds true, and a Global Earth Observation System of Systems (GEOSS) (GEO, 2005), such that “NASA EO Level 2 product → ESA EO Level 2 product ⊂ EO-IU in operating mode ⊂ CV → GEOSS”. Synonym of 4D spatio-temporal scene from (2D) image reconstruction and understanding, vision is acknowledged to be a cognitive problem very difficult to solve because: (i) non-polynomial (NP)-hard in computational complexity (Frintrop, 2011; Tsotsos, 1990), (ii) inherently ill-posed in the Hadamard sense, as it is affected by: (I) a 4D-to-2D data dimensionality reduction from the scene- to the image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap from ever-varying sub-symbolic sensory data (sensations) in the image-domain to stable symbolic percepts in the modeled world (mental world, world ontology, world model) (Fonseca et al, 2002; Laurini & Thompson, 1992; Matsuyama & Hwang, 1990; Sonka et al, 1994; Sowa, 2000). A NASA Earth observation (EO) Level 2 product, defined as “a data-derived geophysical variable at the same resolution and location as Level 1 source data” (NASA 2016b), is part-of the ESA EO Level 2 product, defined as follows (ESA, 2015; DLR & VEGA, 2011): (a) a single-date multi-spectral (MS) image whose digital numbers (DNs) are radiometrically calibrated into surface reflectance (SURF) values corrected for atmospheric, adjacency and topographic effects, stacked with (b) its data-derived general-purpose, user- and application-independent scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow (CNES, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Synonym of 4D spatio-temporal scene from (2D) image reconstruction and understanding, vision is acknowledged to be a cognitive problem very difficult to solve because: (i) non-polynomial (NP)-hard in computational complexity (Frintrop, 2011; Tsotsos, 1990), (ii) inherently ill-posed in the Hadamard sense, as it is affected by: (I) a 4D-to-2D data dimensionality reduction from the scene- to the image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap from ever-varying sub-symbolic sensory data (sensations) in the image-domain to stable symbolic percepts in the modeled world (mental world, world ontology, world model) (Fonseca et al, 2002; Laurini & Thompson, 1992; Matsuyama & Hwang, 1990; Sonka et al, 1994; Sowa, 2000). A NASA Earth observation (EO) Level 2 product, defined as “a data-derived geophysical variable at the same resolution and location as Level 1 source data” (NASA 2016b), is part-of the ESA EO Level 2 product, defined as follows (ESA, 2015; DLR & VEGA, 2011): (a) a single-date multi-spectral (MS) image whose digital numbers (DNs) are radiometrically calibrated into surface reflectance (SURF) values corrected for atmospheric, adjacency and topographic effects, stacked with (b) its data-derived general-purpose, user- and application-independent scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow (CNES, 2015).…”
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.
“…Geographical information has all these problems [4,2,12,18], its specific aspect being to deal with geographical-space areas, called parcels, on which we need to operate union and intersection.…”
Section: Introductionmentioning
confidence: 99%
“…Ontology is often used for representing a structured vocabulary [12], and the fusion of ontology-based geospatial information must face the problem of heterogeneous vocabularies [10]. This paper deals with terminology integration and discusses the merging of information provided by different sources using multiple space partitions, and expressed with more or less precise labels from the same ontology resulting from a preliminary alignment.…”
Spatial information associates properties to labeled areas. Space is partitioned into (elementary) parcels, and union of parcels constitute areas. Properties may have various level of generality, giving birth to a taxonomy of properties for a given universe of discourse. Thus, the set of properties pertaining to a conceptual taxonomy, as the set of areas and parcels, are structured by a natural partial order. We refer to such structures as ontologies. In fusion problems, information coming from distinct sources may be expressed in terms of different conceptual and/or spatial ontologies, and may be pervaded with uncertainty. Dealing with several conceptual (or spatial) ontologies in a fusion perspective presupposes that these ontologies be aligned. This paper introduces a basic representation format called attributive formula, which is a pair made of a property and a set of parcels (to which the property applies), possibly associated with a certainty level. Uncertain attributive formulas are processed in a possibilistic logic manner, augmented with a two-sorted characterization: the property may be true everywhere in an area, or at least true somewhere in the area. The fusion process combines the factual information encoded by the attributive formulas provided by the different sources together with the logical encoding of the conceptual and spatial ontologies (obtained after alignment). Then, inconsistency encountered in the fusion process may be handled by taking advantage of the existence of different fusion modes, or by relaxing when necessary a closed world-like assumption stating by default that what is true somewhere in an area may be also true everywhere in it (if nothing else is known). A landscape analysis toy example illustrates the approach.
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