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Map construction algorithms: a local evaluation through hiking data / David Duran in Geoinformatica [en ligne], vol 24 n° 3 (July 20202)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 3 (July 20202) . - pp 633 – 681 Titre : Map construction algorithms: a local evaluation through hiking data Type de document : Article/Communication Auteurs : David Duran, Auteur ; Vera Sacristan, Auteur ; Rodrigo I. Silveira, Auteur Année de publication : 2020 Article en page(s) : pp 633 – 681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie numérique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] artefact [Termes descripteurs IGN] cartographie automatique [Termes descripteurs IGN] conception cartographique [Termes descripteurs IGN] randonnée [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] rédaction cartographique [Termes descripteurs IGN] trajet Résumé : (auteur) We study five existing map construction algorithms, designed and tested with urban vehicle data in mind, and apply them to hiking trajectories with different terrain characteristics. Our main goal is to better understand the existing strategies and their limitations, in order to shed new light into the current challenges for map construction algorithms. We carefully analyze the results obtained by each algorithm focusing on the local details of the generated maps. Our analysis includes the characterization of 10 types of common artifacts, which occur in the results of more than one algorithm, and 7 algorithmic-specific artifacts, which are consequences of different algorithmic strategies. This allows us to extract systematic conclusions about the main challenges to fully automatize the construction of maps from trajectory data, to detect the strengths and weaknesses of the potential different strategies, and to suggest possible ways to design higher-quality map construction methods. We consider that this analysis will be of help for designing new and better methods that perform well in wider and more realistic contexts, not only for road map or hiking reconstruction, but also for other types of trajectory data. Numéro de notice : A2020-371 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00386-7 date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1007/s10707-019-00386-7 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95266 [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) . - 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 Article en page(s) : 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 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]

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 947 - 968 Titre : Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks Type de document : Article/Communication Auteurs : Mahmoud Saeedimoghaddam, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2020 Article en page(s) : pp 947 - 968 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] carte ancienne [Termes descripteurs IGN] carte numérisée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] numérisation automatique [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. Numéro de notice : A2020-205 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1696968 date de publication en ligne : 28/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1696968 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94882 [article]

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Evaluating the impact of visualization of risk upon emergency route-planning / Lisa Cheong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1022 - 1050 Titre : Evaluating the impact of visualization of risk upon emergency route-planning Type de document : Article/Communication Auteurs : Lisa Cheong, Auteur ; Christoph Kinkeldey, Auteur ; Ingrid Burfurd, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1022 - 1050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] cartographie d'urgence [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] inondation [Termes descripteurs IGN] itinéraire [Termes descripteurs IGN] planification [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] sémiologie graphique [Termes descripteurs IGN] symbole graphique Résumé : (auteur) This paper reports on a controlled experiment evaluating how different cartographic representations of risk affect participants’ performance on a complex spatial decision task: route planning. The specific experimental scenario used is oriented towards emergency route-planning during flood response. The experiment compared six common abstract and metaphorical graphical symbolizations of risk. The results indicate a pattern of less-preferred graphical symbolizations associated with slower responses and lower-risk route choices. One mechanism that might explain these observed relationships would be that more complex and effortful maps promote closer attention paid by participants and lower levels of risk taking. Such user considerations have important implications for the design of maps and mapping interfaces for emergency planning and response. The data also highlights the importance of the ‘right decision, wrong outcome problem’ inherent in decision-making under uncertainty: in individual instances, more risky decisions do not always lead to worse outcomes. Numéro de notice : A2020-206 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1701677 date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1701677 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94885 [article]

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Predictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 315 - 331 Titre : Predictive mapping with small field sample data using semi‐supervised machine learning Type de document : Article/Communication Auteurs : Fei Du, Auteur ; A - Xing Zhu, Auteur ; Jing Liu, Auteur ; Lin Yang, Auteur Année de publication : 2020 Article en page(s) : pp 315 - 331 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] covariance [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] représentation cartographique Résumé : (Auteur) Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small. Numéro de notice : A2020-174 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12598 date de publication en ligne : 04/12/2019 En ligne : https://doi.org/10.1111/tgis.12598 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94900 [article]

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)  

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Variable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs / Grégoire Guillet in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Deep learning for enrichment of vector spatial databases: application to highway interchange / Guillaume Touya (2020)    

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Mobile Mapping / Clancy Wilmott (2020)  

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La modélisation en géographie : villes et territoires, ch. 4. Modélisation territoriale incrémentale / Clémentine Cottineau (2020)

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Bertin’s graphic variables and online map makers: an empirical study of maps produced by prosumers and cartographers / Natalia Ipatow in Cartographica, vol 54 n° 4 (Winter 2019)  

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Empirical studies on the visual perception of spatial patterns in choropleth maps / Jochen Schiewe in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)  

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“Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap / Clancy Wilmott in Cartographic perspectives, n°92 (2019)  

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Graphically supported evaluation of mapping techniques used in presenting spatial accessibility / Lukasz Wielebski in Cartography and Geographic Information Science, Vol 46 n° 4 (July 2019)  

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