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Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 11 (November 2022) . - n° 560 Titre : Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments Type de document : Article/Communication Auteurs : Sercan Gülci, Auteur ; Afiz Hulusi Acar, Auteur ; Abdullah E. Akay, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 560 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] attribut géomètrique [Termes IGN] coefficient de corrélation [Termes IGN] courbe [Termes IGN] matrice de confusion [Termes IGN] montagne [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] tracé routier [Termes IGN] Turquie [Vedettes matières IGN] Généralisation Résumé : (auteur) Road curve attributes can be determined by using Geographic Information System (GIS) to be used in road vehicle traffic safety and planning studies. This study involves analyzing the GIS-based estimation accuracy in the length, radius and the number of small horizontal road curves on a two-lane rural road and a forest road. The prediction success of horizontal curve attributes was investigated using digitized raw and generalized/simplified road segments. Two different roads were examined, involving 20 test groups and two control groups, using 22 datasets obtained from digitized and surveyed roads based on satellite imagery, GIS estimates, and field measurements. Confusion matrix tables were also used to evaluate the prediction accuracy of horizontal curve geometry. F-score, Mathews Correlation Coefficient, Bookmaker Informedness and Balanced Accuracy were used to investigate the performance of test groups. The Kruskal–Wallis test was used to analyze the statistical relationships between the data. Compared to the Bezier generalization algorithm, the Douglas–Peucker algorithm showed the most accurate horizontal curve predictions at generalization tolerances of 0.8 m and 1 m. The results show that the generalization tolerance level contributes to the prediction accuracy of the number, curve radius, and length of the horizontal curves, which vary with the tolerance value. Thus, this study underlined the importance of calculating generalizations and tolerances following a manual road digitization. Numéro de notice : A2022-847 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11110560 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.3390/ijgi11110560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102083 [article]

GisGCN: a visual graph-based framework to match geographical areas through time / Margarita Khokhlova in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 97 Titre : GisGCN: a visual graph-based framework to match geographical areas through time Type de document : Article/Communication Auteurs : Margarita Khokhlova , Auteur ; Nathalie Abadie , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut géomètrique [Termes IGN] attribut sémantique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données étiquetées d'entrainement [Termes IGN] entité géographique [Termes IGN] image aérienne [Termes IGN] réseau sémantique Résumé : (auteur) Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Numéro de notice : A2022-156 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020097 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.3390/ijgi11020097 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100316 [article]

Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications / Benjamin Misiuk in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 327 - 385 Titre : Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications Type de document : Article/Communication Auteurs : Benjamin Misiuk, Auteur ; Vincent Lecours, Auteur ; M.F.J. Dolan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 327 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse multiéchelle [Termes IGN] artefact [Termes IGN] attribut géomètrique [Termes IGN] carte bathymétrique [Termes IGN] cartographie hydrographique [Termes IGN] fond marin [Termes IGN] géomorphométrie [Termes IGN] habitat animal [Termes IGN] pente [Termes IGN] réalité de terrain [Termes IGN] rugosité [Termes IGN] sondeur multifaisceaux [Termes IGN] Terre-Neuve, île de (Terre-Neuve-et-Labrador) Résumé : (auteur) The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales. Numéro de notice : A2021-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/01490419.2021.1925789 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925789 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97967 [article]

Automating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)  

Public [article]  inInternational journal of cartography > vol 7 n° 1 (March 2021) . - pp 100 - 115 Titre : Automating and utilising equal-distribution data classification Type de document : Article/Communication Auteurs : Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur ; Ibad Kureshi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 100 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse spatiale [Termes IGN] attribut géomètrique [Termes IGN] attribut sémantique [Termes IGN] carte choroplèthe [Termes IGN] classification [Termes IGN] exploration de données géographiques [Termes IGN] intervalle de classe [Termes IGN] répartition géographique Résumé : (Auteur) Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live. Numéro de notice : A2021-184 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1863000 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.1080/23729333.2020.1863000 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97114 [article]

Using geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data / Müslüm Hacar (2021)  

Public Titre : Using geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data Type de document : Article/Communication Auteurs : Müslüm Hacar, Auteur Editeur : Cardiff [Royaume-Uni] : Cardiff University Année de publication : 2021 Conférence : GISRUK 2021, 29th GIS research UK annual conference 14/04/2021 16/04/2021 Cardiff online Royaume-Uni OA Proceedings Importance : 6 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Ankara (Turquie) [Termes IGN] attribut géomètrique [Termes IGN] attribut sémantique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] loisir [Termes IGN] OpenStreetMap [Termes IGN] traitement de données localisées Résumé : (auteur) OpenStreetMap is one of the successful volunteered geographic al information projects. Participants contribute to this crowdsourced project by adding geometric and semantic data. However, both missing geometric and semantic data still cause complete ness problems. In this paper, a semi-automated approach is suggested to identify the values of leisure tag of polygon features. The approach uses geometric (rectangularity, density, area, and distances to bus stop and shop) and semantic (amenity) data and estimates the key values using random forest classifier. In short, the results show that tag identification was conducted in three districts of Ankara with f - score s 78%, 86%, and 87%. Numéro de notice : C2021-082 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication DOI : 10.5281/zenodo.4665518 Date de publication en ligne : 06/04/2021 En ligne : https://doi.org/10.5281/zenodo.4665518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101043

Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

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Open data, big data : quel renouveau du raisonnement cartographique ? / Emilie Lerond in Cartes & Géomatique, n° 235-236 (mars - juin 2018)

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Automatic registration of images to untextured geometry using average shading gradients / Tobias Plötz in International journal of computer vision, vol 125 n° 1-3 (December 2017)  

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A geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)  

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A novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Contributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)  

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Segmentation sémantique de données de télédétection multimodale : application aux peuplements forestiers / Clément Dechesne (2017)    

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Compilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)

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Spatial interpolation to predict missing attributes in GIS using semantic kriging / Shrutilipi Bhattacharjee in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)  

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Vers une approche pluridisciplinaire des réseaux enterrés / Lucile Gimenez in XYZ, n° 135 (juin - août 2013) 

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