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Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in International Journal of Health Geographics, vol inconnu (2021)  

Public [article]  inInternational Journal of Health Geographics > vol inconnu (2021) Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Note générale : bibliographie 10.21203/rs.3.rs-128679/v1 DOI d'attente Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] adresse postale [Termes descripteurs IGN] anonymisation [Termes descripteurs IGN] carte sanitaire [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] surveillance sanitaire [Termes descripteurs IGN] traitement de données localisées Résumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2021-065 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.21203/rs.3.rs-128679/v1 En ligne : https://doi.org/10.21203/rs.3.rs-128679/v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857 [article]

OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier / Quy Thy Truong in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - n° 504 Titre : OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Guillaume Touya , Auteur ; Cyril de Runz, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données Résumé : (auteur) Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Numéro de notice : A2020-507 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090504 date de publication en ligne : 22/08/2020 En ligne : https://doi.org/10.3390/ijgi9090504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95682 [article]

A change of theme: the role of generalization in thematic mapping / Paulo Raposo in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - n° 371 ; 18 p. Titre : A change of theme: the role of generalization in thematic mapping Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur ; Guillaume Touya , Auteur ; Pia Bereuter, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 371 ; 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] cartogramme [Termes descripteurs IGN] cartographie thématique [Termes descripteurs IGN] couche thématique [Termes descripteurs IGN] fond cartographique [Termes descripteurs IGN] géovisualisation [Termes descripteurs IGN] histoire de la cartographie [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] style cartographique [Termes descripteurs IGN] visualisation cartographique [Vedettes matières IGN] Généralisation Résumé : (auteur) Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography and geovisualization. Generalization is not new to these areas of cartography, and has in fact always been involved in thematic geographic visualization, despite rarely being acknowledged. We illustrate this involvement with several examples of famous, public-audience thematic maps, noting the generalization procedures involved in drawing each, both across their basemap and thematic layers. We also consider, for each map example we note, which generalization operators were crucial to the formation of the map’s thematic message. The many incremental gains made by the cartographic generalization research community while treating reference data can be brought to bear on thematic cartography in the same way they were used implicitly on the well-known thematic maps we highlight here as examples. Numéro de notice : A2020-318 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060371 date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.3390/ijgi9060371 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95183 [article]

Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, vol 6 n° 3 (May 2020)    

Public [article]  inACM Transactions on spatial algorithms and systems > vol 6 n° 3 (May 2020) . - 21 p. Titre : Deep learning for enrichment of vector spatial databases: Application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base d'apprentissage [Termes descripteurs IGN] base de données vectorielles [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] échangeur routier [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%). Numéro de notice : A2020-365 Affiliation des auteurs : LaSTIG COGIT (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3382080 date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1145/3382080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95399 [article]

Documents numériques

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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]

Designing multi-scale maps: lessons learned from existing practices / Marion Dumont in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Editorial / Guillaume Touya in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Constraint based evaluation of generalized images generated by deep learning / Azelle Courtial (2020)  

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Inferring the scale and content of a map using deep learning / Guillaume Touya (2020)  

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MapGenOnto: A shared ontology for map generalisation and multi-scale visualisation / Guillaume Touya (2020)  

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Le vandalisme de l'information géographique volontaire : analyse exploratoire et proposition d'une méthodologie de détection automatique / Quy Thy Truong (2020)  

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Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)  

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Is deep learning the new agent for map generalization? / Guillaume Touya in International journal of cartography, vol 5 n° 2-3 (July - November 2019)  

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Automatic derivation of on-demand tactile maps for visually impaired people: first experiments and research agenda / Guillaume Touya in International journal of cartography, vol 5 n° 1 (March 2019)  

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CartAGen: an open source research platform for map generalization / Guillaume Touya (2019)    

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