A method for the formalization and integration of geographical categorizations

Kavouras, Marinos; Kokla, Margarita

doi:10.1080/13658810210129120

Cited by 69 publications

(44 citation statements)

References 12 publications

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“…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

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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.

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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

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“…Their distribution identifies many-to-many inter-vocabulary relations, whose special cases are one-to-many, many-to-one and one-to-one relations [32]. This means that comprehensive interpretation of an OAMTRX can be very challenging, complex and time consuming [37,[41][42][43][44][45][46][47], which is not the case for a traditional (square and sorted) CMTRX, whose interpretation is simple and intuitive because it is guided by the main diagonal [36,50,51].…”

Section: Spectral Category Bbs Range/mp Vl-m Nir H-vh Nir Watermentioning

confidence: 99%

“…In the rest of this section, the aforementioned probability sampling procedure is instantiated for accuracy assessment of twelve pre-classification maps (refer to Section 1), generated from the three test images, described in Section 2, by the SIAM™ three-granule software product (refer to the Part 1, Tables 3 and 4 [20]) and the ATCOR™-SPECL single-granule software secondary product (refer to the Part 1, [38][39][40][41][42][43] is equivalent to solving semantic heterogeneity in a hierarchical organization of ontologies, to guarantee their semantic interoperability, like in ontology-driven geographic information systems [41,42]. In practice, the development of ontologies (e.g., spatio-temporal ontologies of the 4-D world-through-time, refer to the Part 1, Section 2.3 [20]) can facilitate the capture of domain knowledge in such a way as to detect or prevent errors when semantic data sources must be integrated.…”

Section: (Vi)mentioning

confidence: 99%

Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results

2013

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This paper complies with the Quality Assurance Framework for Earth Observation (QA4EO) international guidelines to provide a metrological/statistically-based quality assessment of the Spectral Classification of surface reflectance signatures (SPECL) secondary product, implemented within the popular Atmospheric/Topographic Correction (ATCOR™) commercial software suite, and of the Satellite Image Automatic Mapper™ (SIAM™) software product, proposed to the remote sensing (RS) community in recent years. The ATCOR™-SPECL and SIAM™ physical model-based expert systems are considered of potential interest to a wide RS audience: in operating mode, they require neither user-defined parameters nor training data samples to map, in near real-time, a spaceborne/airborne multi-spectral (MS) image into a discrete and finite set of (pre-attentional first-stage) spectral-based semi-concepts (e.g., "vegetation"), whose informative content is always equal or inferior to that of target (attentional second-stage) land cover (LC) concepts (e.g., "deciduous forest"). For the sake of simplicity, this paper is split into two: Part 1-Theory and Part 2-Experimental results. The Part 1 provides the present Part 2 with an interdisciplinary terminology and a theoretical background. To comply with the principle of statistics and the QA4EO guidelines discussed in the Part 1, OPEN ACCESSRemote Sens. 2013, 5 5210 the present Part 2 applies an original adaptation of a novel probability sampling protocol for thematic map quality assessment to the ATCOR™-SPECL and SIAM™ pre-classification maps, generated from three spaceborne/airborne MS test images. Collected metrological/ statistically-based quality indicators (QIs) comprise: (i) an original Categorical Variable Pair Similarity Index (CVPSI), capable of estimating the degree of match between a test pre-classification map's legend and a reference LC map's legend that do not coincide and must be harmonized (reconciled); (ii) pixel-based Thematic (symbolic, semantic) QIs (TQIs) and (iii) polygon-based sub-symbolic (non-semantic) Spatial QIs (SQIs), where all TQIs and SQIs are provided with a degree of uncertainty in measurement. Main experimental conclusions of the present Part 2 are the following. (I) Across the three test images, the CVPSI values of the SIAM™ pre-classification maps at the intermediate and fine semantic granularities are superior to those of the ATCOR™-SPECL single-granule maps. (II) TQIs of both the ATCOR™-SPECL and the SIAM™ tend to exceed community-agreed reference standards of accuracy. (III) Across the three test images and the SIAM™'s three semantic granularities, TQIs of the SIAM™ tend to be significantly higher (in statistical terms) than the ATCOR™-SPECL's. Stemming from the proposed experimental evidence in support to theoretical considerations, the final conclusion of this paper is that, in compliance with the QA4EO objectives, the SIAM™ software product can be considered eligible for injecting prior spectral knowledge into the pre-attentive vision first stage ...

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“…The mathematical theory of Concept Lattices was used for managing multidimensional geographic categories and their overlapping relationships for integrating different land cover/land use categorizations [68]. The semantic content of a category was weighted to emphasize critical features in the categories definition.…”

Section: Semantic Interpretationmentioning

confidence: 99%

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

Hui

Chen³

et al. 2017

IJGI

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A number of national, regional and global land cover classification systems have been developed to meet specific user requirements for land cover mapping exercises, independent of scale, nomenclature and quality. However, this variety of land-cover classification systems limits the compatibility and comparability of land cover data. Furthermore, the current lack of interoperability between different land cover datasets, often stemming from incompatible land cover classification systems, makes analysis of multi-source, heterogeneous land cover data for various applications a very difficult task. This paper provides a critical review of the harmonization of land cover classification systems, which facilitates the generation, use and analysis of land cover maps consistently. Harmonization of existing land cover classification systems is essential to improve their cross-comparison and validation for understanding landscape patterns and changes. The paper reviews major land cover classification standards according to different scales, summarizes studies on harmonizing land cover mapping, and discusses some research problems that need to be solved and some future research directions.

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A method for the formalization and integration of geographical categorizations

Cited by 69 publications

References 12 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

Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

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