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A typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, vol 36 n° 15 ([15/08/2021])
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Titre : A typification method for linear building groups based on stroke simplification Type de document : Article/Communication Auteurs : Xiao Wang, Auteur ; Dirk Burghardt, Auteur Année de publication : 2021 Article en page(s) : pp 1732 - 1751 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] alignement
[Termes IGN] bâtiment
[Termes IGN] généralisation du bâti
[Termes IGN] noeud
[Termes IGN] objet géographique linéaire
[Termes IGN] reconnaissance de formes
[Termes IGN] simplification de contour
[Termes IGN] triangulation de Delaunay
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Linear building groups are common patterns and important local structures in large scale maps, which should be carefully generalized. This paper uses the idea of line simplification to typify linear building groups. Firstly, based on the stroke idea, the linear building groups are detected that each building group is related by only one stroke; the collinear and curvilinear patterns are distinguished by calculating the overlap rate between the defined auxiliary polygon and its oriented bounding box. Secondly, the stroke is simplified by removing one node in each iterative step; and the remained nodes are reallocated to the new positions, which serves as the centroids location of the newly typified buildings. Third, the representation (size, shape, elongation, and orientation) of the newly typified buildings are calculated by the geometry information of their corresponding parent buildings. The typification method can be carried out as a progressive process, which iterates over the three steps to derive continuous typification results. The method is tested on two building datasets, and the experimental results demonstrate that the proposed method can achieve good performance by well preserving the original linear patterns in the generalized building groups. Numéro de notice : A2021-569 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1669725 Date de publication en ligne : 26/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1669725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98184
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1732 - 1751[article]Investigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)
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Titre : Investigating the application of artificial intelligence for earthquake prediction in Terengganu Type de document : Article/Communication Auteurs : Suzlyana Marhain, Auteur ; Ali Najah Ahmed, Auteur ; Muhammad Ary Murti, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 977 - 999 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] apprentissage automatique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] courbe de Pearson
[Termes IGN] données météorologiques
[Termes IGN] intelligence artificielle
[Termes IGN] Malaisie
[Termes IGN] prévention des risques
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] séisme
[Termes IGN] surveillance géologique
[Termes IGN] tsunamiRésumé : (auteur) Numéro de notice : A2021-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1007/s11069-021-04716-7 Date de publication en ligne : 04/04/2021 En ligne : https://doi.org/10.1007/s11069-021-04716-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98232
in Natural Hazards > vol 108 n° 1 (August 2021) . - pp 977 - 999[article]Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
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Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] Google Earth Engine
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 Date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Applying planetary mapping methods to submarine environments: onshore-offshore geomorphology of Christiana-Santorini-Kolumbo Volcanic Group, Greece / Alexandra E. Huff in Journal of maps, vol 17 n° 3 (July 2021)
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Titre : Applying planetary mapping methods to submarine environments: onshore-offshore geomorphology of Christiana-Santorini-Kolumbo Volcanic Group, Greece Type de document : Article/Communication Auteurs : Alexandra E. Huff, Auteur ; Paraskevi Nomikou, Auteur ; Lisa A. Thompson, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 111 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] 1:100.000
[Termes IGN] carte bathymétrique
[Termes IGN] carte géologique
[Termes IGN] Grèce
[Termes IGN] prévention des risques
[Termes IGN] relief sous-marin
[Termes IGN] surveillance géologique
[Termes IGN] système d'information géographique
[Termes IGN] volcanRésumé : (auteur) Geologic maps are foundational products for natural hazard assessments but developing them for submarine areas is challenging due to a lack of physical access to the study area. In response, submarine geomorphologic maps are used to provide geologic context and spatial information on landforms and related geo-hazards for risk management. These maps are generated from remotely sensed data, e.g. digital elevation models (DEMs), which introduce unique hurdles to submarine mapping. To address this issue, we produced a workflow for applying planetary geologic mapping methods to submarine data. Using this, we created an onshore-offshore geomorphologic map of the Christiana-Santorini-Kolumbo Volcanic Group, Greece. This product can be used to enhance hazard assessments on Santorini, which is a tourist hot-spot at high risk for volcanically- and seismically-induced hazards. We present this workflow as a tool for generating uniform geomorphologic map products that will aid natural hazard assessments of submarine environments. Numéro de notice : A2021-694 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2021.1880980 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1080/17445647.2021.1880980 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98557
in Journal of maps > vol 17 n° 3 (July 2021) . - pp 111 - 121[article]Design and development 3D RRR model for Turkish cadastral system using international standards / Mehmet Alkan in Survey review, Vol 53 n° 379 (July 2021)
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Titre : Design and development 3D RRR model for Turkish cadastral system using international standards Type de document : Article/Communication Auteurs : Mehmet Alkan, Auteur ; Hicret Gürsoy Sürmeneli, Auteur ; Zeynel Abidin Polat, Auteur Année de publication : 2021 Article en page(s) : pp 312 - 324 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger
[Termes IGN] base de données foncières
[Termes IGN] cadastre 3D
[Termes IGN] infrastructure nationale des données localisées
[Termes IGN] INSPIRE
[Termes IGN] norme ISO
[Termes IGN] standard OGC
[Termes IGN] TurquieRésumé : (auteur) The concepts of three-dimensional cadastre (3D) and property ownership led to increased interest in land use management and research towards the end of the 90s. Within the scope of these studies, international standards and definitions have been realised. In Turkey, there are some academic studies available. However, there are not many studies conducted on an institutional basis. Turkey cadastre carried out by the General Directorate of Land Registry, and Cadastre (GDLRC) are kept. In this context, a 3D RRR (Right, Restriction and Responsibility) for Turkey-based cadastral data model design and development is essential in terms of not constitute a base for the study. The fact that these studies are in the context of LADM and ISO standards and OGC is very important in terms of the fact that the cadastral system is related to international standards. Numéro de notice : A2021-521 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1758386 Date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.1080/00396265.2020.1758386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97954
in Survey review > Vol 53 n° 379 (July 2021) . - pp 312 - 324[article]Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
PermalinkDigital camera calibration for cultural heritage documentation: the case study of a mass digitization project of religious monuments in Cyprus / Evagoras Evagorou in European journal of remote sensing, vol 54 sup 1 (2021)
PermalinkDynamique contrastée de la compaction d’un ferralsol après une défriche mécanisée alternative en Guyane française / Xavier Guerrini in Bois et forêts des tropiques, n° 348 ([01/07/2021])
PermalinkFeux de forêts et technologies spatiales / Laurent Polidori in Géomètre, n° 2193 (juillet-août 2021)
PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)
PermalinkIdentifying home locations in human mobility data: an open-source R package for comparison and reproducibility / Qingqing Chen in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)
PermalinkMachine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices / Linchuan Yang in Annals of GIS, vol 27 n° 3 (July 2021)
PermalinkMulti-scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images / Yanyan Gao in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
PermalinkThe spread of the Mercator projection in Western European and United States cartography / Michele Abee in Cartographica, vol 56 n° 2 (Summer 2021)
PermalinkUsing machine learning to map Western Australian landscapes for mineral exploration / Thomas Albrecht in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
PermalinkA framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])
PermalinkDynamic optimization models for displaying outdoor advertisement at the right time and place / Meng Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)
PermalinkPrevention of erosion in mountain basins: A spatial-based tool to support payments for forest ecosystem services / Sandro Sacchelli in Journal of forest science, vol 67 n° 6 (July 2021)
PermalinkReconsidering movement and exposure: Towards a more dynamic health geography / Malcolm Campbell in Geography compass, vol 15 n° 6 (June 2021)
PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkCrowdsourcing of popular toponyms: How to collect and preserve toponyms in spoken use / Daniel Vrbik in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
PermalinkIntegration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
PermalinkA novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)
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