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Epistemological thoughts on the success of maps and the role of cartography / David Fairbairn in International journal of cartography, vol 7 n° 3 (October 2021)  

Public [article]  inInternational journal of cartography > vol 7 n° 3 (October 2021) . - pp 317-331 Titre : Epistemological thoughts on the success of maps and the role of cartography Type de document : Article/Communication Auteurs : David Fairbairn, Auteur ; Georg Gartner, Auteur ; Michael P. Peterson, Auteur Année de publication : 2021 Article en page(s) : pp 317-331 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] carte analytique [Termes IGN] carte thématique [Termes IGN] épistémologie [Termes IGN] information géographique [Termes IGN] représentation cartographique [Termes IGN] visualisation cartographique Résumé : (auteur) In the context of assertions that defining a map is an illogical and futile exercise, this paper examines the distinctiveness of the discipline of cartography and the success of the human endeavour that has produced ‘things’ called maps. We contend not only that cartography is a coherent and distinctive discipline (although we do consider the semantics involved in, and rationale for, referring to it by that name), but that human society cannot function without maps. The major reason for this is that each map is created for a human or societal purpose – and this purpose defines the nature and utility of the map. The role of a map defines the very nature of a map itself, and we reflect on the creation, functionality, and success of mapping activity. This leads us to conclude with pointers to functional definition of the map. Numéro de notice : A2021-746 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/23729333.2021.1972909 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/23729333.2021.1972909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98704 [article]

A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104 Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] altitude [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection des nuages [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image Gaofen [Termes IGN] information géographique [Termes IGN] latitude [Termes IGN] longitude [Termes IGN] modèle statistique [Termes IGN] neige [Termes IGN] Normalized Difference Snow Index Résumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187 [article]

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Reproducible research and GIScience: An evaluation using GIScience conference papers / Franck O. Ostermann (2021)  

Public Titre : Reproducible research and GIScience: An evaluation using GIScience conference papers Type de document : Article/Communication Auteurs : Franck O. Ostermann, Auteur ; Daniel Nüst, Auteur ; Carlos Granell, Auteur ; Barbara Hofer, Auteur ; Markus Konkol, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Importance : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes IGN] code source libre [Termes IGN] données ouvertes [Termes IGN] information géographique [Termes IGN] recherche scientifique [Termes IGN] reproductibilité Résumé : (auteur) GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875. Numéro de notice : C2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.II.2 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.II.2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100938

Mapping the fantastic great Southern continent, 1760–1777: A study in enlightenment geography / Vanessa Collingridge in Cartographic journal (the), Vol 57 n° 4 (November 2020)  

Public [article]  inCartographic journal (the) > Vol 57 n° 4 (November 2020) . - pp 335 - 352 Titre : Mapping the fantastic great Southern continent, 1760–1777: A study in enlightenment geography Type de document : Article/Communication Auteurs : Vanessa Collingridge, Auteur Année de publication : 2020 Article en page(s) : pp 335 - 352 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne [Termes IGN] Australes (îles) [Termes IGN] Australie [Termes IGN] carte ancienne [Termes IGN] Cook, James [Termes IGN] dix-huitième siècle [Termes IGN] information géographique [Termes IGN] Océanie (géographie politique) Résumé : (auteur) This paper examines the relationship between the press and the construction of ‘enlightened’ public knowledge about – and imaginaries for – the emerging south in the period 1760–1777. It analyses the construction, nature, and forms of the putative Great Southern Continent in contemporary British newspapers and magazines, and offers some suggestions for how knowledge from navigators such as James Cook was given credence in the public domain back home. It draws parallels between the ‘new media’ of the eighteenth-century Enlightenment and those of the twenty-first century with their similar debates over who controls public (geographical) information, whom and how to trust, and why. Numéro de notice : A2021-835 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00087041.2020.1884430 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1080/00087041.2020.1884430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97879 [article]

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Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1117 - 1136 Titre : Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data Type de document : Article/Communication Auteurs : Shivangi Srivastava, Auteur ; John E. Vargas-Muñoz, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2020 Article en page(s) : pp 1117 - 1136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] base de données urbaines [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] données localisées libres [Termes IGN] Ile-de-France [Termes IGN] image Streetview [Termes IGN] image terrestre [Termes IGN] information géographique [Termes IGN] méthode heuristique [Termes IGN] OpenStreetMap [Termes IGN] réseau social Résumé : (auteur) We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. Numéro de notice : A2020-269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542698 Date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542698 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95041 [article]

Ontology of core concept data types for answering geo-analytical questions / Simon Scheider in Journal of Spatial Information Science (JoSIS), n° 20 (2020)  

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The position of sound in audiovisual maps: an experimental study of performance in spatial memory / Nils Siepmann in Cartographica, vol 55 n° 2 (Summer 2020)  

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A Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale / Martin Fleischmann in Computers, Environment and Urban Systems, vol 80 (March 2020)  

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Composition of place: towards a compositional view of functional space / Emmanuel Papadakis in Cartography and Geographic Information Science, Vol 47 n° 1 (January 2020)  

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Extraction de connaissances pour la description de l'environnement maritime côtier à partir de textes d'aide à la navigation / Léa Lamotte in Revue des Nouvelles Technologies de l'Information, E.36 (2020)

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Georeferenced measurements of building objects with their simultaneous shape detection / Edward Osada in Survey review, Vol 52 n°370 (January 2020)  

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Perspective switch and spatial knowledge acquisition: effects of age, mental rotation ability and visuospatial memory capacity on route learning in virtual environments with different levels of realism / Ismini E. Lokka in Cartography and Geographic Information Science, Vol 47 n° 1 (January 2020)  

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Rapport d'activité 2019 de l'Institut National de l'Information Géographique et Forestière IGN / Institut national de l'information géographique et forestière (2012 -) (2020)  

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