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Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures / Timofey Samsonov in International journal of cartography, Vol 6 n° 1 (March 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 1 (March 2020) . - pp 47 - 70 Titre : Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures Type de document : Article/Communication Auteurs : Timofey Samsonov, Auteur ; Olga P. Yakimova, Auteur Année de publication : 2020 Article en page(s) : pp 47 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] algorithme de Visvalingam [Termes IGN] modèle de régression [Termes IGN] niveau de détail [Termes IGN] précision cartographique [Termes IGN] représentation des détails topographiques [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) One of the important stages of map generalization is the selection of optimal simplification procedure for each spatial feature or feature type. Selected algorithms are then applied collaboratively to simplify the whole set of features. However, there is a lack of investigations that report a systematic approach of deriving a similar reduction in accuracy and detail by using different algorithms. In current paper we propose the solution to this problem on the basis of regression modeling between tolerance value of each algorithm and the value of some geometric measure which describes changes in accuracy and detail of the line. This allows fitting the regression model between tolerance values of the two selected algorithms which can be used to obtain similar simplification results. Regressions between Douglas-Peucker, Li-Openshaw and Visvalingam-Whyatt algorithm tolerance values are investigated. Application of methodology is illustrated on the example of three coastlines with significantly different spatial character. Results of the study show that regression coefficients depend highly both on the combination of the two algorithms, and on the character of the line. Finally, it is shown that a weighted combination of accuracy and detail regression models can be used to model the changes in level of detail of the line. Numéro de notice : A2020-071 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1615745 Date de publication en ligne : 29/07/2019 En ligne : https://doi.org/10.1080/23729333.2019.1615745 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94634 [article]

Shape-adaptive geometric simplification of heterogeneous line datasets / Timofey Samsonov in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1485 - 1520 Titre : Shape-adaptive geometric simplification of heterogeneous line datasets Type de document : Article/Communication Auteurs : Timofey Samsonov, Auteur ; Olga P. Yakimova, Auteur Année de publication : 2017 Article en page(s) : pp 1485 - 1520 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] données hétérogènes [Termes IGN] généralisation automatique de données [Termes IGN] généralisation cartographique [Termes IGN] limite administrative [Termes IGN] objet géographique linéaire [Termes IGN] simplification de contour [Termes IGN] traitement de données [Vedettes matières IGN] Généralisation Résumé : (Auteur) Line generalization is an essential data processing operation in geographic information systems and cartography. Many point reduction and simplification algorithms have been developed for this purpose. During the last three decades, several attempts have been made to develop approaches of generalization that adapt to the geometric properties of the processed lines. They typically incorporate line segmentation based on quantitative descriptions of the line shape. Little attention has been paid to the cases in which heterogeneous lines of different geometric character are mixed in one dataset. One common example is administrative borders, which often contain natural and artificial, smooth and sharp, schematic and non-schematic, and regular and irregular shapes. The tuning of the simplification algorithm based on a quantitative description of the shape would be ineffective here, since different algorithms must be applied to lines of different characters. In this article, we present a general method and generalization model for the simplification of such datasets. The properties of schematism, smoothness and regularity are used to differentiate various line characters. Three line characters, including irregular non-schematic, irregular schematic and regular orthogonal schematic, were selected for the current study. The developed generalization model consists of preprocessing, processing and postprocessing stages. Line segmentation based on the detection of different characters is performed during the preprocessing stage. Then, Li–Openshaw, Douglas–Peucker and orthogonal simplification are applied to the appropriate segments in the processing stage. Postprocessing enables the addition of extra regularity to the simplified shape. A visual and quantitative assessment of the results is provided and demonstrates the effectiveness of the developed approach in comparison with the global application of a single simplification algorithm. Numéro de notice : A2017-309 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1306864 En ligne : http://dx.doi.org/10.1080/13658816.2017.1306864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85356 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017042	RAB	Revue	Centre de documentation	En réserve L003	Disponible