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Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Extraction of urban built-up areas from nighttime lights using artificial neural network Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Giovanni Coco, Auteur ; Jay Gao, Auteur Année de publication : 2020 Article en page(s) : pp 1049 - 1066 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] bati
[Termes IGN] cartographie urbaine
[Termes IGN] classification dirigée
[Termes IGN] développement durable
[Termes IGN] échantillonnage
[Termes IGN] éclairage public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] rayonnement lumineux
[Termes IGN] réseau neuronal artificiel
[Termes IGN] seuillage
[Termes IGN] température au sol
[Termes IGN] zone urbaineRésumé : (auteur) The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL. Numéro de notice : A2020-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559887 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559887 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95488
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1049 - 1066[article]Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
[article]
Titre : Planar polygons detection in lidar scans based on sensor topology enhanced Ransac Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Zoumana Mallé, Auteur ; Oussama Ennafii , Auteur ; Pascal Monasse, Auteur ; Bruno Vallet , Auteur Année de publication : 2020 Projets : BIOM / Vallet, Bruno Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 343 - 350 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] polygone
[Termes IGN] Ransac (algorithme)
[Termes IGN] segmentation en régions
[Termes IGN] semis de points
[Termes IGN] topologie capteur
[Termes IGN] traitement de semis de points
[Termes IGN] transformation de HoughRésumé : (auteur) Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation however is that these methods detect planes running across the whole scene instead of more localized planar patches. Moreover, they do not exploit the sensor information that often comes with Lidar point cloud (sensor topology and optical center position in particular). In this paper we address both issues: we aim at detecting planar polygons that have a limited spatial extent, and we exploit sensor topology. The latter is used to enhance a RANSAC framework on two aspects: to make seed points selection more local and to define more compact sets of inliers through sensor space region growing. Numéro de notice : A2020-502 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-343-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-343-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95643
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 343 - 350[article]Regionalization of flood magnitudes using the ecological attributes of watersheds / Bahman Jabbarian Amiri in Geocarto international, vol 35 n° 9 ([01/07/2020])
[article]
Titre : Regionalization of flood magnitudes using the ecological attributes of watersheds Type de document : Article/Communication Auteurs : Bahman Jabbarian Amiri, Auteur ; Bahareh Baheri, Auteur ; Nicola Fohrer, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 917 - 933 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] bassin hydrographique
[Termes IGN] Caspienne, mer
[Termes IGN] crue
[Termes IGN] débit
[Termes IGN] estimation quantitative
[Termes IGN] humidité du sol
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] prévention des risques
[Termes IGN] régionalisation (segmentation)
[Termes IGN] ressources en eau
[Termes IGN] utilisation du sol
[Termes IGN] zone inondableRésumé : (auteur) Estimating flood discharge at ungauged sites is a significant challenge facing water resources planners and engineers during the planning and design of hydraulic structures, managing flood prone zones, and operating artificial waterbodies. Developing more robust models to improve the reliability of flood discharge estimations is thus very useful. The role of ecological attributes including land use/land cover (LULC), hydrologic soil groups (HSG), and watershed physical characteristics (area, main stream length, average slope), and watershed shape coefficients (form, compactness, circularity, and elongation) in explaining the overall variation in flood magnitude in 39 watersheds, located in the southern basin of the Caspian Sea, was investigated. As the LULC and HSG were found to play a significant role in explaining total variation (40–89%) in flood magnitudes, their inclusion in the estimation of flood magnitudes can provide more reliable estimates of flood risk and magnitude. Numéro de notice : A2020-428 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552321 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95494
in Geocarto international > vol 35 n° 9 [01/07/2020] . - pp 917 - 933[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification / Yu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
[article]
Titre : Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification Type de document : Article/Communication Auteurs : Yu Li, Auteur ; Ting Lu, Auteur ; Shutao Li, Auteur Année de publication : 2020 Article en page(s) : pp 4976 - 4988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] échantillonnage
[Termes IGN] image hyperspectrale
[Termes IGN] image multiple
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (auteur) Active learning (AL) attempts to actively select the most representative or useful training samples in an iterative manner. The aim is to simultaneously improve the classification performance and reduce the manual labeling effort. In this article, a novel subpixel-pixel-superpixel-based multiview AL (MAL) (SPS-MAL) method is proposed for hyperspectral image (HSI) classification. Here, the multiple views are generated via extracting the subpixel-level, pixel-level, and superpixel-level information. The multiple views can reflect various characteristics of HSI, i.e., spectral mixture, spectral discrimination, and spectral–spatial structure. Therefore, the joint use of diverse and complementary information in multiple views will contribute to a better identification ability of different classes. In addition, a coarse-to-fine MAL algorithm is introduced to effectively select the most representative samples with the most uncertainty. Specifically, a disagreement analysis on multiple views and joint posterior probability estimation is used to query unlabeled samples. Along with the expansion of training samples, view-specific confidence scores are estimated to adaptively integrate the classification results of multiple views, according to their discrimination performance. In this way, the classification accuracy will be further boosted while the number of necessary training samples can be significantly reduced. The experimental classification results on three well-known HSIs demonstrate the effectiveness of the proposed SPS-MAL method. Numéro de notice : A2020-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2971081 Date de publication en ligne : 14/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2971081 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95388
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4976 - 4988[article]The image of subsurface geology / Ane Bang-Kittilsen in International journal of cartography, Vol 6 n° 2 (July 2020)
[article]
Titre : The image of subsurface geology Type de document : Article/Communication Auteurs : Ane Bang-Kittilsen, Auteur Année de publication : 2020 Article en page(s) : pp 222 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] analyse visuelle
[Termes IGN] carte géologique
[Termes IGN] communication cartographique
[Termes IGN] croquis topographique
[Termes IGN] enquête
[Termes IGN] géologie
[Termes IGN] langage cartographique
[Termes IGN] pictogramme
[Termes IGN] segmentation sémantique
[Termes IGN] sémiologie graphique
[Termes IGN] symbole graphique
[Termes IGN] utilisateur
[Termes IGN] zone urbaineRésumé : (auteur) There is an underuse of geological knowledge in society. Therefore, an unused potential of more informed decision-making and planning as well as improved solutions on societal challenges exist. The aim of this study was to better understand the geological map user and to improve the usability of geological map products. With the aim of improving graphical communication through maps and images, visual research methods are used. The sketch map method, which has been used since the 1960s, is used here to elicit information about people and their image subsurface geology of a city. The participants include students in area planning and experts within geology. Content, semiotic and visual analyses were performed on the sketches produced by the participants. The results show limited knowledge of geology and a lack of common geological language, both graphical and linguistic. Improved ways of representing the subsurface are identified, which can be used as input to more intuitive future designs. Adapting to the user’s image of subsurface geology, usability could be increased by using plain language, adding landmarks, pictographic symbols and patterns to geological visualizations. This could potentially lower the user threshold, trigger interest and raise the awareness of local urban geology. Numéro de notice : A2020-375 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1637489 Date de publication en ligne : 07/08/2019 En ligne : https://doi.org/10.1080/23729333.2019.1637489 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95309
in International journal of cartography > Vol 6 n° 2 (July 2020) . - pp 222 - 240[article]Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkCounting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkA hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)PermalinkModélisation d'une maquette sur la base de données LiDAR et intégration d'un projet 3D / Julien Brunner in Géomatique suisse, vol 118 n° 6 (juin 2020)PermalinkAssessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkComparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)PermalinkExploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkHow much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems / Ali Javidaneh in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkMethod for extraction of airborne LiDAR point cloud buildings based on segmentation / Maohua Liu in Plos one, vol 15 n° 5 (May 2020)PermalinkDirectionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkRecognizing linear building patterns in topographic data by using two new indices based on Delaunay triangulation / Xianjin He in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkAn improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)PermalinkClassifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)PermalinkSea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)PermalinkA breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 n° 1 (February 2020)PermalinkTransferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkTree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkAnalyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020)PermalinkApplication of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)PermalinkApplication of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)PermalinkCartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)PermalinkClassification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. 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Cromley in Transactions in GIS, Vol 23 n° 6 (November 2019)PermalinkUnsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkAccurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network / Yihua Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinkSpatially constrained regionalization with multilayer perceptron / Michael Govorov in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkDevelopment and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkA factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)PermalinkLearning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkModelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines / Kassel Hingee in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkPlace and sentiment-based life story analysis: From the Spanish republican army to the French resistance / Catherine Dominguès in Revue française des sciences de l'information et de la communication, vol 17 (2019)PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)Permalink“Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap / Clancy Wilmott in Cartographic perspectives, n° 92 (2019)PermalinkPyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkSemantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkAutomatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkDetecting and characterizing downed dead wood using terrestrial laser scanning / Tuomas Yrttimaa in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkVoxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods / Florent Poux in ISPRS International journal of geo-information, vol 8 n° 5 (May 2019)PermalinkJournées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkSegmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. 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