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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)
[article]
Titre : CNN-based dense image matching for aerial remote sensing images Type de document : Article/Communication Auteurs : Shunping Ji, Auteur ; Jin Liu, Auteur ; Meng Lu, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] couple stéréoscopique
[Termes IGN] image aérienne
[Termes IGN] Munich
[Termes IGN] réseau neuronal convolutif
[Termes IGN] Stuttgart
[Termes IGN] ville
[Termes IGN] zone urbaineRésumé : (Auteur) Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE. Numéro de notice : A2019-246 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.415 Date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.415 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93002
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019061 SL Revue Centre de documentation Revues en salle Disponible Deep mapping gentrification in a large Canadian city using deep learning and Google Street View / Lazar Ilic in Plos one, vol 14 n° 3 (March 2019)
[article]
Titre : Deep mapping gentrification in a large Canadian city using deep learning and Google Street View Type de document : Article/Communication Auteurs : Lazar Ilic, Auteur ; M. Sawada, Auteur ; Amaury Zarzelli, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Article en page(s) : n° e0212814 Note générale : bibliographie
This work was supported by and is a contribution to the Ottawa Neighbourhood Study (www.neighbourhoodstudy.ca).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse socio-économique
[Termes IGN] apprentissage profond
[Termes IGN] Canada
[Termes IGN] image Streetview
[Termes IGN] quartier
[Termes IGN] villeRésumé : (auteur) Gentrification is multidimensional and complex, but there is general agreement that visible changes to neighbourhoods are a clear manifestation of the process. Recent advances in computer vision and deep learning provide a unique opportunity to support automated mapping or ‘deep mapping’ of perceptual environmental attributes. We present a Siamese convolutional neural network (SCNN) that automatically detects gentrification-like visual changes in temporal sequences of Google Street View (GSV) images. Our SCNN achieves 95.6% test accuracy and is subsequently applied to GSV sequences at 86110 individual properties over a 9-year period in Ottawa, Canada. We use Kernel Density Estimation (KDE) to produce maps that illustrate where the spatial concentration of visual property improvements was highest within the study area at different times from 2007–2016. We find strong concordance between the mapped SCNN results and the spatial distribution of building permits in the City of Ottawa from 2011 to 2016. Our mapped results confirm those urban areas that are known to be undergoing gentrification as well as revealing areas undergoing gentrification that were previously unknown. Our approach differs from previous works because we examine the atomic unit of gentrification, namely, the individual property, for visual property improvements over time and we rely on KDE to describe regions of high spatial intensity that are indicative of gentrification processes. Numéro de notice : A2019-165 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1371/journal.pone.0212814 Date de publication en ligne : 13/03/2019 En ligne : https://doi.org/10.1371/journal.pone.0212814 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99693
in Plos one > vol 14 n° 3 (March 2019) . - n° e0212814[article]
Titre : Mesurer l'excess commuting à différentes échelles Type de document : Article/Communication Auteurs : Milo Monnier, Auteur ; Paul Chapron , Auteur ; Hadrien Commenges, Auteur ; Maxime Lenormand, Auteur Editeur : [s.l.] : [s.n.] Année de publication : 2019 Projets : NetCost / Lenormand, Maxime Conférence : Théo Quant 2019, 14es rencontres des nouvelles approches en géographie théorique et quantitative 06/02/2019 08/02/2019 Besançon France Open Access Proceedings Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] échelle cartographique
[Termes IGN] migration pendulaire
[Termes IGN] mise à l'échelle
[Termes IGN] origine - destination
[Termes IGN] villeRésumé : (auteur) Les déplacements en excès ("excess commuting") se définissent comme "des déplacements non optimisés dans une forme urbaine donnée". Le concept d’excess commuting permet d’analyser la dissociation spatiale entre domicile et lieu de travail à une échelle donnée. Il est généralement calculé en comparant les déplacements observés avec les déplacements optimisés obtenus en minimisant la distance totale parcourue tout en préservant l’emplacement des lieux de résidence et des lieux de travail. Une forme de programmation linéaire appelée "problème de transport" est souvent utilisée pour optimiser la matrice de déplacements domiciletravail. Beaucoup étudié ces dernières années, l’excess commuting permet de mieux comprendre le lien entre forme urbaine et efficacité du réseau de navettage. Cet indicateur est cependant sensible à la manière dont la matrice de déplacements domicile-travail est construite. Par exemple, la définition de la région d'étude et son découpage en unités spatiales sont des facteurs pouvant impacter l’excess commuting et ainsi biaiser la comparaison de différentes régions. Plusieurs études ont déjà été menées sur le sujet mais les effets combinés de ces facteurs restent cependant encore peu connus, particulièrement au niveau local. Durant cette présentation, nous proposerons un cadre méthodologique permettant de générer automatiquement des matrices de déplacements domicile-travail à différentes échelles ainsi que les matrices optimisées associées. Nous nous intéresserons en particulier à la sensibilité de la mesure d’excess commuting aux changements d’échelle et aux limites spatiales de la région d’étude. Nous étudierons ces effets pour plusieurs villes d’Europe et à différentes échelles : dans un premier temps à une échelle globale permettant de comparer nos différents cas d’études, puis à une échelle plus locale pour étudier la distribution spatiale des déplacements en excès. Numéro de notice : C2019-054 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans En ligne : https://hal.archives-ouvertes.fr/hal-02889649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96643 Multi-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)
[article]
Titre : Multi-scale object detection in remote sensing imagery with convolutional neural networks Type de document : Article/Communication Auteurs : Zhipeng Deng, Auteur ; Hao Sun, Auteur ; Shilin Zhou, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 3 - 22 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] aéroport
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] image Sentinel-SAR
[Termes IGN] réseau neuronal convolutif
[Termes IGN] villeRésumé : (Auteur) Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images. Numéro de notice : A2018-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.003 Date de publication en ligne : 02/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91224
in ISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 3 - 22[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018113 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project / Amin Mobasheri in Geo-spatial Information Science, vol 21 n° 3 (October 2018)
[article]
Titre : OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project Type de document : Article/Communication Auteurs : Amin Mobasheri, Auteur ; Alexander Zipf, Auteur ; Louise Francis, Auteur Année de publication : 2018 Article en page(s) : pp 234 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] accessibilité
[Termes IGN] données localisées des bénévoles
[Termes IGN] enrichissement sémantique
[Termes IGN] Europe (géographie politique)
[Termes IGN] exhaustivité des données
[Termes IGN] handicap
[Termes IGN] OpenStreetMap
[Termes IGN] qualité des données
[Termes IGN] segmentation sémantique
[Termes IGN] villeRésumé : (Auteur) Nowadays, several research projects show interest in employing volunteered geographic information (VGI) to improve their systems through using up-to-date and detailed data. The European project CAP4Access is one of the successful examples of such international-wide research projects that aims to improve the accessibility of people with restricted mobility using crowdsourced data. In this project, OpenStreetMap (OSM) is used to extend OpenRouteService, a well-known routing platform. However, a basic challenge that this project tackled was the incompleteness of OSM data with regards to certain information that is required for wheelchair accessibility (e.g. sidewalk information, kerb data, etc.). In this article, we present the results of initial assessment of sidewalk data in OSM at the beginning of the project as well as our approach in awareness raising and using tools for tagging accessibility data into OSM database for enriching the sidewalk data completeness. Several experiments have been carried out in different European cities, and discussion on the results of the experiments as well as the lessons learned are provided. The lessons learned provide recommendations that help in organizing better mapping party events in the future. We conclude by reporting on how and to what extent the OSM sidewalk data completeness in these study areas have benefited from the mapping parties by the end of the project. Numéro de notice : A2018-645 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2018.1493817 Date de publication en ligne : 21/09/2018 En ligne : https://doi.org/10.1080/10095020.2018.1493817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93315
in Geo-spatial Information Science > vol 21 n° 3 (October 2018) . - pp 234 - 246[article]Spatial discontinuities, health and mobility - What do the Google's POIs and tweets tell us about Bangkok's (Thailand) structures and spatial dynamics? / Alexandre Cebeillac in Revue internationale de géomatique, vol 28 n° 4 (octobre - décembre 2018)PermalinkIntegrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters / Jia Yu in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkAssessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets / Ming Li in Geoinformatica, vol 22 n° 3 (July 2018)PermalinkEvaluation of the cartographical quality of urban plans by eye-tracking / Jaroslav Burian in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)PermalinkThe national geographic characteristics of online public opinion propagation in China based on WeChat network / Chuan Ai in Geoinformatica, vol 22 n° 2 (April 2018)Permalink3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis / Benjamin Herfort in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkA new model for cadastral surveying using crowdsourcing / K. Apostolopoulos in Survey review, vol 50 n° 359 (March 2018)PermalinkRecognition of building group patterns in topographic maps based on graph partitioning and random forest / Xianjin He in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkUsing mobility data as proxy for measuring urban vitality / Patrizia Sulis in Journal of Spatial Information Science (JoSIS), n° 16 ([01/02/2018])PermalinkDetection and localization of traffic signals with GPS floating car data and Random Forest / Yann Méneroux (2018)PermalinkFrom Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)PermalinkVers une nouvelle approche pour calculer les indicateurs de la densité urbaine via l'imagerie de satellite Alsat-2A / Tarek Medjadj in Bulletin des sciences géographiques, n° 31 (2017 - 2018)PermalinkMonitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015) / Ronald C. Estoque in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkEnhancing plant diversity and mitigating BVOC emissions of urban green spaces through the introduction of ornamental tree species / Yuan Ren in Urban Forestry & Urban Greening, vol 27 (October 2017)Permalink3d roof model generation and analysis supporting solar system positioning / Filiberto Chiabrando in Geomatica, vol 71 n° 3 (September 2017)PermalinkEffects of using different sources of remote sensing and geographic information system data on urban stormwater 2D–1D modeling / Yi Hong in Applied sciences, vol 7 n° 9 (September 2017)PermalinkGeovisualisation as a process of creating complementary visualisations: static two-dimensional, surface three-dimensional, and interactive / Tymoteusz Horbiński in Geodesy and cartography, vol 66 n° 1 (June 2017)PermalinkPermalinkPermalinkPermalink