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GIS-based logic scoring of preference method for urban densification suitability analysis / Shuoge Shen in Computers, Environment and Urban Systems, vol 89 (September 2021)
[article]
Titre : GIS-based logic scoring of preference method for urban densification suitability analysis Type de document : Article/Communication Auteurs : Shuoge Shen, Auteur ; Suzana Dragićević, Auteur ; Jozo Dujmović, Auteur Année de publication : 2021 Article en page(s) : n° 101654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aide à la décision
[Termes IGN] analyse multicritère
[Termes IGN] croissance urbaine
[Termes IGN] démographie
[Termes IGN] densité de population
[Termes IGN] planification urbaine
[Termes IGN] système d'information géographique
[Termes IGN] Vancouver (Colombie britannique)
[Termes IGN] zone urbaineRésumé : (auteur) Urban Densification Suitability Analysis (UDSA) approach has been proposed using the GIS-based Logic Scoring of Preference (LSP) decision method. Our goal is to provide the methodology for the evaluation of suitability of locations for high density urban development to facilitate the spatial decision-making process and help justifiable urban planning. The main objectives of this research study are (1) to implement the GIS-based LSP method for high density urban growth suitability analysis, and (2) to perform an overall integrated suitability analysis for two key stakeholders, urban developer and urban planner. The LSP method is based on soft computing and used in the decision-making process to integrate large number of attribute criteria in a way that is consistent with human logic reasoning. The GIS-based LSP method is implemented using geospatial data for Metro Vancouver Region, Canada. Main criteria used to characterize the urban densification include recreation, transportation, existing development, economy, terrain, restrictions, and demography. The obtained results provide the distributions of suitability values and indicate that the opinions of two stakeholders differ. They select different locations as the most suitable for urban densifications and rather agree on the most unsuitable locations. The overall integrated suitability analysis has been performed with scenarios of possible decision-making outcomes between the two stakeholders. We introduce the concept of suitability agreement maps along with urban densification decision criteria. The analysis indicates that the GIS-based LSP method is a useful tool for measuring the similarities and differences in stakeholders' views and facilitating decision-making process for urban densification. Numéro de notice : A2021-534 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101654 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97996
in Computers, Environment and Urban Systems > vol 89 (September 2021) . - n° 101654[article]Investigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)
[article]
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)
[article]
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)
[article]
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)
[article]
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)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)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)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)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)Permalink