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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 (November 2021)  

Public [article]  inInternational journal of cartography > vol 7 n° 3 (November 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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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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Ontology of core concept data types for answering geo-analytical questions / Simon Scheider in Journal of Spatial Information Science (JoSIS), n° 20 ([30/06/2020])  

Public [article]  inJournal of Spatial Information Science (JoSIS) > n° 20 [30/06/2020] Titre : Ontology of core concept data types for answering geo-analytical questions Type de document : Article/Communication Auteurs : Simon Scheider, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] concept [Termes IGN] données localisées [Termes IGN] information géographique [Termes IGN] modèle sémantique de données [Termes IGN] ontologie [Termes IGN] requête spatiale [Termes IGN] système d'information géographique Résumé : (auteur) In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources. Numéro de notice : A2020-850 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5311/JOSIS.2020.20.555 Date de publication en ligne : 30/06/2020 En ligne : https://josis.org/index.php/josis/article/view/125 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98665 [article]

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]

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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A conceptual framework for studying collective reactions to events in location-based social media / Alexander Dunkel in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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