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How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data Type de document : Article/Communication Auteurs : Chaogui Kang, Auteur ; Li Shi, Auteur ; Fahui Wang, Auteur ; Yu Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1504 - 1525 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données spatiotemporelles
[Termes IGN] ethnographie
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] matrice de co-occurrence
[Termes IGN] production participative
[Termes IGN] réseau social
[Termes IGN] site urbain
[Termes IGN] téléphonie mobile
[Termes IGN] urbanismeRésumé : (Auteur) This research attempts to build a unified framework for distinguishing the spatiotemporal visit patterns of urban places by different social groups using mobile phone data in Harbin, China. Social groups are detected by their social ties in the ego‐to‐ego mobile phone call network and are embedded in physical space according to their home locations. Popular urban places are detected from user‐generated content as the basic spatial analysis unit. Coupling subscribers’ footprints and urban places in physical space, the spatiotemporal visit patterns of urban places by distinct social groups are uncovered and interpreted by non‐negative matrix factorization. The proposed framework enables us to answer several critical questions from three perspectives: (1) How to model popular urban places in terms of vague boundary, land use, and semantic features based on crowdsourcing data?; (2) How to evaluate interaction between individuals for inspecting the relationship between spatial proximity and social ties based on spatiotemporal co‐occurrence?; and (3) How to distinguish urban place visit preferences for social groups associated with different socio‐demographic characteristics? Our research could assist urban planners and municipal managers to identify critical urban places frequented by different population groups according to their roles and social/cultural characteristics for improvement of urban facility allocation. Numéro de notice : A2020-767 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12654 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1111/tgis.12654 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96658
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1504 - 1525[article]Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] analyse texturale
[Termes IGN] bati
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'imageRésumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087[article]Extracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)
[article]
Titre : Extracting impervious surfaces from full polarimetric SAR images in different urban areas Type de document : Article/Communication Auteurs : Sara Attarchi, Auteur Année de publication : 2020 Article en page(s) : pp 4644 - 4663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de données
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] polarimétrie radar
[Termes IGN] précision de la classification
[Termes IGN] radar à antenne synthétique
[Termes IGN] surface imperméable
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] texture d'image
[Termes IGN] zone urbaineRésumé : (auteur) Accurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited. Numéro de notice : A2020-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723178 Date de publication en ligne : 24/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723178 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95539
in International Journal of Remote Sensing IJRS > vol 41 n° 12 (20 - 30 March 2020) . - pp 4644 - 4663[article]Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
[article]
Titre : Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis Type de document : Article/Communication Auteurs : Matheus Pinheiro Ferreira, Auteur ; Fabien Hubert Wagner, Auteur ; Luiz E.O.C. Aragão, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] arbre (flore)
[Termes IGN] Brésil
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] houppier
[Termes IGN] image à très haute résolution
[Termes IGN] image infrarouge
[Termes IGN] image Worldview
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] matrice de co-occurrence
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] variation saisonnièreRésumé : (Auteur) Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images. Numéro de notice : A2019-117 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.019 Date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92444
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 119 - 131[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest / Qingxia Zhao in Forests, vol 9 n° 10 (October 2018)
[article]
Titre : Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest Type de document : Article/Communication Auteurs : Qingxia Zhao, Auteur ; Fei Wang, Auteur ; Jun Zhao, Auteur ; Jingjing Zhou, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'arbres
[Termes IGN] Enhanced vegetation index
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] loess
[Termes IGN] matrice de co-occurrence
[Termes IGN] plantation forestière
[Termes IGN] régression
[Termes IGN] Robinia pseudoacacia
[Termes IGN] Soil Adjusted Vegetation IndexRésumé : (Auteur) The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau. Numéro de notice : A2018-477 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100623 Date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.3390/f9100623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91178
in Forests > vol 9 n° 10 (October 2018)[article]Remote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkDiscovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkThe analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkAnalyzing relatedness by toponym co-occurrences on web pages / Yu Liu in Transactions in GIS, vol 18 n° 1 (February 2014)PermalinkInformation content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)PermalinkEstimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions / M. Cutler in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkClassification of very high spatial resolution imagery based on the fusion of edge and multispectral information / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)PermalinkUsing co-occurrence models for placename disambiguation / S. Overell in International journal of geographical information science IJGIS, vol 22 n° 3 (march 2008)PermalinkDetection, segmentation and characterisation of vegetation in high-resolution aerial images for 3D city modelling / Corina Iovan (2008)Permalink