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Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / Niccolò Dematteis in Computers & geosciences, vol 159 (February 2022)  

Public [article]  inComputers & geosciences > vol 159 (February 2022) . - n° 104988 Titre : Fast local adaptive multiscale image matching algorithm for remote sensing image correlation Type de document : Article/Communication Auteurs : Niccolò Dematteis, Auteur ; Daniele Giordan, Auteur ; Bruno Crippa, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'appariement [Termes IGN] appariement d'images [Termes IGN] corrélation d'images [Termes IGN] données multiéchelles [Termes IGN] fonte des glaces [Termes IGN] glacier [Termes IGN] image Sentinel-MSI [Termes IGN] implémentation (informatique) [Termes IGN] Matlab [Termes IGN] Patagonie Résumé : (auteur) Various studies have shown that image correlation calculated in the space domain outperforms frequency-based methods. However, such an approach usually requires great computational efforts, making it challenging to adopt for surveying fast moving processes like glaciers, particularly over wide areas. We present a local adaptive multiscale image matching algorithm (LAMMA), which repeatedly applies image correlation on grids of increasing spatial resolution and adapts the size of the interrogation area according to the local range of displacements. LAMMA allows reducing the number of calculi of several orders of magnitude and limits the occurrence of displacement outliers. We show an example of LAMMA application on Sentinel-2 images to measure glaciers flow of the Southern Patagonian Icefield, where LAMMA's runtime was comparable to that of frequency-based correlation. LAMMA's Matlab code is freely available on GitHub. Numéro de notice : A2022-094 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104988 Date de publication en ligne : 19/11/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99528 [article]

Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)  

Public [article]  inSurvey review > Vol 53 n° 379 (July 2021) . - pp 349 - 365 Titre : Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI Type de document : Article/Communication Auteurs : Arif Cagdas Aydinoglu, Auteur ; Rabia Bovkir, Auteur ; Ismail Colkesen, Auteur Année de publication : 2021 Article en page(s) : pp 349 - 365 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] apprentissage automatique [Termes IGN] base de données foncières [Termes IGN] classification par forêts aléatoires [Termes IGN] coefficient de corrélation [Termes IGN] implémentation (informatique) [Termes IGN] infrastructure nationale des données localisées [Termes IGN] interopérabilité [Termes IGN] Istanbul (Turquie) [Termes IGN] métadonnées [Termes IGN] système d'information géographique Résumé : (auteur) The main purpose of this study is to propose an interoperable land valuation data model for residential properties as an extension of the national geographic data infrastructure (GDI) and to make mass valuation process applicable with the use of machine learning approach. As an example, random forest (RF) ensemble algorithm was implemented in Pendik district of Istanbul to evaluate the prediction performance by using thematic datasets compatible with the data model. This study provides a methodology for various urban applications and robustness of the algorithm increases the prediction of the real estate values with the use of qualified datasets. Numéro de notice : A2021-523 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1771967 Date de publication en ligne : 06/06/2020 En ligne : https://doi.org/10.1080/00396265.2020.1771967 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97960 [article]

GIS.LSP: A soft computing logic method and tool for geospatial suitability analysis / Shuoge Shen in Transactions in GIS, Vol 25 n° 3 (June 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1228-1248 Titre : GIS.LSP: A soft computing logic method and tool for geospatial 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) : pp 1228-1248 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse multicritère [Termes IGN] croissance urbaine [Termes IGN] densité du bâti [Termes IGN] gestion urbaine [Termes IGN] implémentation (informatique) [Termes IGN] interface utilisateur [Termes IGN] logique [Termes IGN] projet urbain [Termes IGN] système d'information géographique [Termes IGN] Vancouver (Colombie britannique) Résumé : (Auteur) This research study extends the Logic Scoring of Preference (LSP) as a general multicriteria evaluation (MCE) method by presenting and evaluating a new GIS.LSP method and software tool implemented within the geographic information systems (GIS) environment. For the evaluation and validation of the method and software tool, we describe a case study of urban densification suitability analysis using geospatial data for the Metro Vancouver Region, Canada. The criteria, LSP structures, and aggregators groups were developed from the perspective of urban developers who are key stakeholders in the densification process. We compare two group of aggregators and perform sensitivity and cost–suitability analysis of the LSP method by variation of input suitability scores, input attributes, and aggregators. The results indicate the GIS.LSP method is effective in providing a flexible and sensitive workflow to create realistic and justifiable outcomes from complex criteria that are bounded by stakeholders' goals and requirements. Numéro de notice : A2021-465 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12768 Date de publication en ligne : 16/05/2021 En ligne : https://doi.org/10.1111/tgis.12768 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98076 [article]

A Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([01/05/2021])  

Public [article]  inGeocarto international > vol 36 n° 9 [01/05/2021] . - pp 1007 - 1026 Titre : A Bayesian displacement field approach to accurate registration of SAR images Type de document : Article/Communication Auteurs : Mingtao Ding, Auteur ; Hongyan Wang, Auteur ; Lichun Sui, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1007 - 1026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] enregistrement de données [Termes IGN] estimation bayesienne [Termes IGN] image radar moirée [Termes IGN] implémentation (informatique) [Termes IGN] inférence [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] processeur graphique [Termes IGN] superposition d'images [Termes IGN] trajectoire [Termes IGN] transformation Résumé : (auteur) Precise registration of synthetic aperture radar (SAR) images is a nontrivial task since a change in radar-acquisition geometry generates image shifts. In existing system, either the transformation functions are oversimplified, or external measures such as digital elevation model and flight track are required to be precise. In this paper, we proposed a generative Bayesian approach to modelling the displacement vectors that map the position of each pixel in the image, thus avoiding degradation of the transformation function. Rather than providing a point estimate for the transformation function, the proposed method yields a full posterior density function of the transformation function. Especially, the Bayesian model learns all the parameters adaptively, and the procedure is fully automatic. The proposed model is comparable in accuracy to state-of-the-art optical flow methods on the challenging Sintel benchmarks, and outperforms currently published SAR image registration methods on some real SAR data with critical scenes. Numéro de notice : A2021-343 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1633418 Date de publication en ligne : 07/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1633418 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97584 [article]

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A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery / Seth Goodman in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 674 - 691 Titre : A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery Type de document : Article/Communication Auteurs : Seth Goodman, Auteur ; Ariel BenYishay, Auteur ; Daniel Runfola, Auteur Année de publication : 2021 Article en page(s) : pp 674 - 691 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] conflit [Termes IGN] image Landsat-8 [Termes IGN] implémentation (informatique) [Termes IGN] Nigéria [Termes IGN] prédiction [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN‐based approach leveraging Landsat 8 imagery to predict locations of conflict‐related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non‐fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low‐cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate‐resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN‐based methods of estimating development‐related indicators may be effective in applications beyond those explored in the literature. Numéro de notice : A2021-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12661 Date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1111/tgis.12661 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97625 [article]

Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)  

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Système de gestion des servitudes d’utilité publique du Bas-Rhin : développement "backend" / Patrick-Franck Namekong-Teulong (2021) 

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Florence: A web-based grammar of graphics for making maps and learning cartography / Ate Poorthuis in Cartographic perspectives, n° 96 (December 2020)  

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Building facade reconstruction using crowd-sourced photos and two-dimensional maps / Wu Jie in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)  

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CityJSON in QGIS: Development of an open‐source plugin / Stelios Vitalis in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Delivering time-evolving 3D city models for web visualization / Vincent Jaillot in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

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Rasterisation-based progressive photon mapping / Iordanis Evangelou in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

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Computational improvements to multi-scale geographically weighted regression / Ziqi Li in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

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IFC schemas in ISO/TC 211 compliant UML for improved interoperability between BIM and GIS / Knut Jetlund in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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A line integral approach for the computation of the potential harmonic coefficients of a constant density polyhedron / Olivier Jamet in Journal of geodesy, Vol 94 n°3 (March 2020)  

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