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Exploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - n° 338 ; 21 p. Titre : Exploring the potential of deep learning segmentation for mountain roads generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Achraf El Ayedi, Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 338 ; 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] 1:25.000 [Termes descripteurs IGN] 1:250.000 [Termes descripteurs IGN] Alpes (France) [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données routières [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] généralisation automatique de données [Termes descripteurs IGN] montagne [Termes descripteurs IGN] route [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] symbole graphique [Termes descripteurs IGN] virage [Vedettes matières IGN] Généralisation Résumé : (auteur) Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Numéro de notice : A2020-295 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050338 date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.3390/ijgi9050338 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95131 [article]

Potential of crowdsourced traces for detecting updates in authoritative geographic data / Stefan Ivanovic (2020)  

Public Titre : Potential of crowdsourced traces for detecting updates in authoritative geographic data Type de document : Article/Communication Auteurs : Stefan Ivanovic , Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Sébastien Mustière , Auteur ; Thomas Devogele, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Geoinformation and Cartography, ISSN 1863-2246 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : AGILE 2019, 22nd conference on Geo-information science 17/06/2019 20/06/2019 Limassol Chypre Proceedings Springer Importance : pp 205 - 221 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] BD Topo [Termes descripteurs IGN] chemin rural [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] montagne [Termes descripteurs IGN] route [Termes descripteurs IGN] trace GPS Résumé : (auteur) Crowdsourced traces collected by GPS devices during sports activities are now widely available on different websites. The goal of this paper is to study the potential of crowdsourced traces coming from GPS devices to highlight updates in authoritative geographic data. To reach this goal, an approach based on two steps is proposed. First, a data matching method is applied to match authoritative data and crowdsourced traces. Second, for the non-matched crowdsourced segments composing a trace, different criteria are defined to decide if whether or not, non-matched segments should be considered as an alert for update in authoritative data. The proposed approach is tested on crowdsourced traces and on BDTOPO® authoritative road and path network in mountain area. The results are promising: 727, 1 km of missing paths were found in the test area, which corresponds to 7.7% of the total length of used traces. The discovered missing paths also represent a contribution of 2.4% of the total length of BDTopo® road and path network in the test area. Numéro de notice : C2019-008 Affiliation des auteurs : LaSTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-030-14745-7_12 date de publication en ligne : 16/04/2019 En ligne : http://dx.doi.org/10.1007/978-3-030-14745-7_12 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92911

Mapping urban fingerprints of odonyms automatically extracted from French novels / Ludovic Moncla in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 12 (December 2019) . - pp 2477 - 2497 Titre : Mapping urban fingerprints of odonyms automatically extracted from French novels Type de document : Article/Communication Auteurs : Ludovic Moncla, Auteur ; Mauro Gaio, Auteur ; Thierry Joliveau, Auteur ; Yves-François Le Lay, Auteur ; Pierre-Olivier Mazagol, Auteur Année de publication : 2019 Article en page(s) : pp 2477 - 2497 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] dix-neuvième siècle [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] Geoparsing [Termes descripteurs IGN] langage naturel (informatique) [Termes descripteurs IGN] littérature [Termes descripteurs IGN] odonymie [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] reconnaissance de noms [Termes descripteurs IGN] route [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) In this paper, we propose and discuss a methodology to map the spatial fingerprints of novels and authors based on all of the named urban roads (i.e., odonyms) extracted from novels. We present several ways to explore Parisian space and fictional landscapes by interactively and simultaneously browsing geographical space and literary text. Our project involves building a platform capable of retrieving, mapping and analyzing the occurrences of named urban roads in novels in which the action occurs wholly or partly in Paris. This platform will be used in several areas, such as cultural tourism, urban research, and literary analysis. The paper focuses on extracting named urban roads and mapping the results for a sample of 31 novels published between 1800 and 1914. Two approaches to the annotation of odonyms are compared. First, we describe a proof of concept using queries made via the TXM textual analysis platform. Then, we describe an automatic process using a natural language processing (NLP) method. Additionally, we mention how the geosemantic information annotated from the text (e.g., a structure combining verbs, spatial relations, named entities, adjectives and adverbs) can be used to automatically characterize the semantic content associated with named urban roads. Numéro de notice : A2019-427 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1584804 date de publication en ligne : 17/03/2019 En ligne : https://doi.org/10.1080/13658816.2019.1584804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93560 [article]

A time‐geographic approach to quantifying wildlife–road interactions / Rebecca W. Loraamm in Transactions in GIS, vol 23 n° 1 (February 2019)  

Public [article]  inTransactions in GIS > vol 23 n° 1 (February 2019) . - pp 70 - 86 Titre : A time‐geographic approach to quantifying wildlife–road interactions Type de document : Article/Communication Auteurs : Rebecca W. Loraamm, Auteur ; Joni A. Downs, Auteur ; David Lamb, Auteur Année de publication : 2019 Article en page(s) : pp 70 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] comportement [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] faune locale [Termes descripteurs IGN] gestion de la vie sauvage [Termes descripteurs IGN] interaction spatiale [Termes descripteurs IGN] migration animale [Termes descripteurs IGN] Mustelidae [Termes descripteurs IGN] prisme spatio-temporel [Termes descripteurs IGN] route [Termes descripteurs IGN] Time-geography [Termes descripteurs IGN] voxel Résumé : (auteur) Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed. Numéro de notice : A2019-090 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12497 date de publication en ligne : 17/12/2018 En ligne : https://doi.org/10.1111/tgis.12497 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92237 [article]

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm / Alireza Chehreghan in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 3 (May 2018) . - pp 255 - 269 Titre : A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm Type de document : Article/Communication Auteurs : Alireza Chehreghan, Auteur ; Rahim Ali Abbaspour, Auteur Année de publication : 2018 Article en page(s) : pp 255 - 269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] algorithme génétique [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] appariement géométrique [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] route Résumé : (Auteur) Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score. Numéro de notice : A2018-132 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1324823 date de publication en ligne : 06/06/2017 En ligne : https://doi.org/10.1080/15230406.2017.1324823 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89662 [article]

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Generative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Labelling hierarchy for street maps using centrality measures / Wasim Shoman in Cartographic journal (the), vol 55 n° 1 (February 2018)  

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Centrality-based hierarchy for street network generalization in multi-resolution maps / Wasim Shoman in Geocarto international, vol 32 n° 12 (December 2017)  

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La carte de la Route des Grandes Alpes / Anonyme in Géomatique expert, n° 116 (mai - juin 2017)

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Auscultation de l'état de surface de revêtements routiers par photogrammétrie automatisée / Gildas Allaz in Géomatique suisse, vol 114 n° 1 (janvier 2016)  

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Diffusion anisotrope et inpainting d’orthophotographies LiDAR mobile / Pierre Biasutti (2016) 

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A function-based linear map symbol building and rendering method using shader language / Songshan Yue in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

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A wildlife movement approach to optimally locate wildlife crossing structures / Rebecca W. Loraamm in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)  

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Street environment change detection from mobile laser scanning point clouds / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

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Road orthophoto/DTM generation from mobile laser scanning / Bruno Vallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3 W5 (October 2015)  

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