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An artificial bee colony-based algorithm to automatically create colour schemes for geovisualizations / Mingguang Wu in Cartographic journal (the), Vol 56 n° 2 (May 2019)  

Public [article]  inCartographic journal (the) > Vol 56 n° 2 (May 2019) . - pp 161 - 174 Titre : An artificial bee colony-based algorithm to automatically create colour schemes for geovisualizations Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; Taisheng Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 161 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] base de règles [Termes IGN] carte sur mesure [Termes IGN] couleur à l'écran [Termes IGN] optimisation par colonie de fourmis [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Creating appropriate colour schemes is challenging for both novice and experienced cartographers. This paper introduces an artificial bee colony (ABC) algorithm to automatically create various colour schemes. Colour scheme creation is treated as a constrained search problem in a continuous colour space. We considered the gamut of the target device and a series of cartographic rules, such as convention, discrimination, contrast, perceptual uniformity and brightness mirror, in the ABC algorithm and presented detailed initialization, fitness definition, local exploration, and global exploration methods for creating qualitative, sequential and diverging colour schemes. The proposed method is evaluated with a case study, and the results indicate that compared with the brute force search method, the proposed method can create satisfying colour schemes of similar quality but significantly improved efficiency. Numéro de notice : A2019-240 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2018.1507182 Date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1080/00087041.2018.1507182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92933 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Fictive motion extraction and classification / Ekaterina Egorova in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2247 - 2271 Titre : Fictive motion extraction and classification Type de document : Article/Communication Auteurs : Ekaterina Egorova, Auteur ; Ludovic Moncla , Auteur ; Mauro Gaio, Auteur ; Christophe Claramunt, Auteur ; Ross S. Purves, Auteur Année de publication : 2018 Article en page(s) : pp 2247 - 2271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Alpes [Termes IGN] base de règles [Termes IGN] corpus [Termes IGN] extraction automatique [Termes IGN] traitement du langage naturel Résumé : (Auteur) Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse. Numéro de notice : A2018-524 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1498503 Date de publication en ligne : 30/07/2018 En ligne : https://doi.org/10.1080/13658816.2018.1498503 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91349 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A deep learning approach to DTM extraction from imagery using rule-based training labels / Caroline M. Gevaert in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 106 - 123 Titre : A deep learning approach to DTM extraction from imagery using rule-based training labels Type de document : Article/Communication Auteurs : Caroline M. Gevaert, Auteur ; Claudio Persello, Auteur ; M. George Vosselman, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] base de règles [Termes IGN] benchmark spatial [Termes IGN] Dar-es-Salam (Tanzanie) [Termes IGN] drone [Termes IGN] échantillonnage d'image [Termes IGN] extraction automatique [Termes IGN] Kigali (Rwanda) [Termes IGN] Lombardie [Termes IGN] modèle numérique de terrain [Termes IGN] photogrammétrie aérienne [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Existing algorithms for Digital Terrain Model (DTM) extraction still face difficulties due to data outliers and geometric ambiguities in the scene such as contiguous off-ground areas or sloped environments. We postulate that in such challenging cases, the radiometric information contained in aerial imagery may be leveraged to distinguish between ground and off-ground objects. We propose a method for DTM extraction from imagery which first applies morphological filters to the Digital Surface Model to obtain candidate ground and off-ground training samples. These samples are used to train a Fully Convolutional Network (FCN) in the second step, which can then be used to identify ground samples for the entire dataset. The proposed method harnesses the power of state-of-the-art deep learning methods, while showing how they can be adapted to the application of DTM extraction by (i) automatically selecting and labelling dataset-specific samples which can be used to train the network, and (ii) adapting the network architecture to consider a larger surface area without unnecessarily increasing the computational burden. The method is successfully tested on four datasets, indicating that the automatic labelling strategy can achieve an accuracy which is comparable to the use of manually labelled training samples. Furthermore, we demonstrate that the proposed method outperforms two reference DTM extraction algorithms in challenging areas. Numéro de notice : A2018-298 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.001 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90410 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data / Razieh Shojanoori in Geocarto international, vol 33 n° 4 (April 2018)  

Public [article]  inGeocarto international > vol 33 n° 4 (April 2018) . - pp 357 - 374 Titre : Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data Type de document : Article/Communication Auteurs : Razieh Shojanoori, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Shattri Bin Mansor, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 357 - 374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] arbre (flore) [Termes IGN] arbre urbain [Termes IGN] base de règles [Termes IGN] détection d'arbres [Termes IGN] image Worldview [Termes IGN] Malaisie [Termes IGN] traitement d'image [Termes IGN] zone urbaine Résumé : (Auteur) The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model. Numéro de notice : A2018-046 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265593 En ligne : https://doi.org/10.1080/10106049.2016.1265593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89268 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

European Forest Types: toward an automated classification / Francesca Giannetti in Annals of Forest Science, vol 75 n° 1 (March 2018)  

Public [article]  inAnnals of Forest Science > vol 75 n° 1 (March 2018) Titre : European Forest Types: toward an automated classification Type de document : Article/Communication Auteurs : Francesca Giannetti, Auteur ; Anna Barbati, Auteur ; Leone Davide Mancini, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de tri [Termes IGN] base de règles [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification pixellaire [Termes IGN] Europe (géographie physique) [Termes IGN] Fagus (genre) [Termes IGN] milieu naturel [Termes IGN] système d'information forestier [Termes IGN] système d'information géographique [Termes IGN] système expert [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Key message: The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping. Context: Forest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists. Aims: This work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature. Methods: A rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests. Results: The RBES successfully classified 94% of the plots, against a 92% obtained with RF. When applied to the mapped domain, the accuracy obtained with the RBES for the beech forest map classification was equal to 95%. Conclusion: The RBES algorithm successfully automatically classified field plots and map pixels on the basis of the EFT system of nomenclature. The EFT system of nomenclature can be now easily and objectively implemented in operative transnational European forest monitoring programs. Numéro de notice : A2018-318 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-017-0674-6 Date de publication en ligne : 03/01/2018 En ligne : https://doi.org/10.1007/s13595-017-0674-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90450 [article]

Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Une approche ontologique pour la structuration de données spatio-temporelles de trajectoires : Application à l’étude des déplacements de mammifères marins / W. Mefteh in Revue internationale de géomatique, vol 22 n° 1 (mars - mai 2012)  

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Des connaissances pour plus de créativité dans le choix des couleurs de la légende (outil COLorLEGend) / Sidonie Christophe in Cartes & Géomatique, n° 211 (mars 2012)  

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Raisonner sur une ontologie cartographique pour concevoir des légendes de cartes / Catherine Dominguès in Revue des Nouvelles Technologies de l'Information, E.23 ([08/02/2012]) 

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Provably correct and complete transaction rules for updating 3D city models / G. Groger in Geoinformatica, vol 16 n° 1 (January 2012)  

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Mise en place de règles cartographiques en relation avec une ontologie du domaine / Fayrouz Soualah-Alila (2011)

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Le modèle CollaGen / Guillaume Touya (2011)  

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Estimation of imprecision in length and area computation in vector databases including production processes description / Jean-François Girres (26/05/2010)    

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Classification des tissus urbains à partir de données vectorielles : application à Strasbourg / Anne Puissant (2010)

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Collaborative generalisation: formalisation of generalisation knowledge to orchestrate different cartographic generalisation processes / Guillaume Touya (2010)    

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