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Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)
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Titre : Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network Type de document : Article/Communication Auteurs : Fengpeng Li, Auteur ; Jiabao Li, Auteur ; Wei Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 577 - 591 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] grande échelle
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] moyenne échelle
[Termes IGN] petite échelle
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set. Numéro de notice : A2021-670 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.8.577 Date de publication en ligne : 01/08/2021 En ligne : https://doi.org/10.14358/PERS.87.8.577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98806
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 8 (August 2021) . - pp 577 - 591[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021081 SL Revue Centre de documentation Revues en salle Disponible Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)
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Titre : Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata Type de document : Article/Communication Auteurs : Yaqian Zhai, Auteur ; Yao Yao, Auteur ; Qingfeng Guan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1475 - 1499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aide à la décision
[Termes IGN] automate cellulaire
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] milieu urbain
[Termes IGN] morphologie
[Termes IGN] parcelle cadastrale
[Termes IGN] petite échelle
[Termes IGN] planification urbaine
[Termes IGN] précision de la classification
[Termes IGN] Shenzhen
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning. Numéro de notice : A2020-307 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1711915 Date de publication en ligne : 14/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1711915 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95149
in International journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1475 - 1499[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020071 RAB Revue Centre de documentation En réserve 3L Disponible Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
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Titre : Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method Type de document : Article/Communication Auteurs : Zhenzhong Peng, Auteur ; Ru Wang, Auteur ; Lingbo Liu, Auteur ; Hao Wu, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] bati
[Termes IGN] densité de population
[Termes IGN] diagramme de Voronoï
[Termes IGN] distribution spatiale
[Termes IGN] données démographiques
[Termes IGN] espace urbain
[Termes IGN] modèle de régression
[Termes IGN] modèle dynamique
[Termes IGN] petite échelle
[Termes IGN] régression géographiquement pondérée
[Termes IGN] téléphone intelligentRésumé : (auteur) Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data. Numéro de notice : A2020-315 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060344 Date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060344 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95170
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 16 p.[article]Partial polygon pruning of hydrographic features in automated generalization / Alexander K. Stum in Transactions in GIS, vol 21 n° 5 (October 2017)
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Titre : Partial polygon pruning of hydrographic features in automated generalization Type de document : Article/Communication Auteurs : Alexander K. Stum, Auteur ; Barbara P. Buttenfield, Auteur ; Lauwrence V. Stanislawski, Auteur Année de publication : 2017 Article en page(s) : pp 1061–1078 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données hydrographiques
[Termes IGN] détection automatique
[Termes IGN] Etats-Unis
[Termes IGN] généralisation automatique de données
[Termes IGN] petite échelle
[Termes IGN] polygone
[Termes IGN] rendu (géovisualisation)
[Termes IGN] simplification de contour
[Termes IGN] traitement automatique de données
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features – such as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce – entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US. Numéro de notice : A2017-634 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12270 En ligne : http://dx.doi.org/10.1111/tgis.12270 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86953
in Transactions in GIS > vol 21 n° 5 (October 2017) . - pp 1061–1078[article]
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Titre : Des frontières mal définies Type de document : Article/Communication Auteurs : Françoise de Blomac, Auteur Année de publication : 2014 Article en page(s) : pp 8 - 9 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données localisées
[Termes IGN] cartographie numérique
[Termes IGN] échelle cartographique
[Termes IGN] petite échelle
[Termes IGN] système d'information géographiqueIndex. décimale : 37.00 Géomatique - information géographique - infrastructure de données Résumé : (Auteur) Peu sollicités pour travailler aux très petites échelles, les outils SIG ont pourtant leur utilité. D'où vient cette désaffection ? La géomatique ne répond pas à toutes les questions et les cartographes ont l'habitude de leurs propres outils. Quitte à assembler plusieurs composants afin de constituer de véritables systèmes d'information. Numéro de notice : A2014-584 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74814
in DécryptaGéo le mag > n° 162 (01/12/2014) . - pp 8 - 9[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 286-2014101 RAB Revue Centre de documentation En réserve 3L Disponible Big scanning on a small scale / Stephen Richardson in GEO: Geoconnexion international, vol 13 n° 6 (june 2014)
PermalinkLa carte de l'Afrique en dix feuilles de Hermann Habenicht, publiée à Gotha en 1885 / Wulf Bodenstein in Cartes & Géomatique, n° 210 (décembre 2011)
PermalinkAutomated reduction of visual complexity in small-scale relief shading / A. Leonowicz in Cartographica, vol 45 n° 1 (March 2010)
PermalinkA flexible multi-source spatial-data fusion system for environmental status assessment at continental scale / P. Carrara in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)
PermalinkTwo-scale surface deformation analysis using the SBAS-DInSAR technique: a case study of the city of Rome, Italy / M. Manunta in International Journal of Remote Sensing IJRS, vol 29 n° 6 (March 2008)
PermalinkTheory and reality of direct georeferencing in national coordinates / Jan Skaloud in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 2 (March - April 2008)
PermalinkRepresenting forested regions at small scales: automatic derivation from very large scale data / William A Mackaness in Cartographic journal (the), vol 45 n° 1 (February 2008)
PermalinkPermalinkThe discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades / Michael Batty in International journal of geographical information science IJGIS, vol 17 n° 7 (october 2003)
PermalinkUrban subsidence monitoring using radar interferometry: algorithms and validation / M. Crosetto in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 7 (July 2003)
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