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A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)
[article]
Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] arbre aléatoire minimum
[Termes IGN] distribution spatiale
[Termes IGN] noeud
[Termes IGN] Pékin (Chine)
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] réseau neuronal de graphes
[Termes IGN] taxinomie
[Termes IGN] trafic routier
[Termes IGN] triangulation de Delaunay
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-460 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622
in Computers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807[article]GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)
[article]
Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] distance de Fréchet
[Termes IGN] empreinte
[Termes IGN] morphologie urbaine
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] texture d'imageRésumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975
in International journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022071 SL Revue Centre de documentation Revues en salle Disponible Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)
[article]
Titre : Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis Type de document : Article/Communication Auteurs : Jinpei Chen, Auteur ; Nan Zhi, Auteur ; Haofan Liao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage profond
[Termes IGN] carte ionosphérique mondiale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] correction ionosphérique
[Termes IGN] modèle dynamique
[Termes IGN] positionnement par GNSS
[Termes IGN] temps de convergence
[Termes IGN] teneur verticale totale en électronsRésumé : (auteur) The widely used GNSS correction services for high precision positioning take advantage of accurate real-time TEC forecasting based on vertical total electron content (VTEC) maps. The methods for modeling and forecasting are mainly based on overly simplified assumptions, which in principle cannot reflect the real situations due to limitations of the mathematical formulations. Therefore, these methods cannot comprehensively capture the features of ionospheric TEC in spatial–temporal series. To overcome the problems caused by such assumptions, we combine ConvLSTM (convolutional long short-term memory) with spectrum analysis. The method allows the extraction of high-resolution spatial–temporal patterns of the ionospheric VTEC maps and accelerates the convergence time of neural networks. Extensive experiments have been carried out for short- and long-term forecasting and demonstrated that the performance of our method is better than other state-of-the-art models developed for various time series analysis methods. Based on the data from global ionospheric maps (GIMs) products, the results show that the root-mean-square error (RMSE) of global VTEC forecasting by our method substantially improves for two hours intervals over the years 2015, 2016, 2017 and 2019 compared to existing methods, specifically, 20–50% reduction on 1 or 2 h forecasting in terms of RMSE. In addition, the method is sufficient to support real-time forecasting since it takes less than one second to output global forecasting solutions. With these properties, we can facilitate real-time and highly accurate ionosphere correction services beneficial to numerous GNSS correct services and positioning terminals. Numéro de notice : A2022-378 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01253-z Date de publication en ligne : 13/04/2022 En ligne : https://doi.org/10.1007/s10291-022-01253-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100638
in GPS solutions > vol 26 n° 3 (July 2022) . - n° 69[article]Improving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)
[article]
Titre : Improving remote sensing classification: A deep-learning-assisted model Type de document : Article/Communication Auteurs : Tsimur Davydzenka, Auteur ; Pejman Tahmasebi, Auteur ; Mark Carroll, Auteur Année de publication : 2022 Article en page(s) : n° 105123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image à haute résolution
[Termes IGN] modèle stochastique
[Termes IGN] précision de la classificationRésumé : (auteur) In many industries and applications, obtaining and classifying remote sensing imagery plays a crucial role. The accuracy of classification, in particular the machine learning methods, mainly depends on a multitude of factors, among which one of the most important ones is the amount of training data. Obtaining sufficient amounts of training data, however, can be very difficult or costly, and one must find alternative ways to improve the accuracy of predictions. To this end, a possible solution that we provide in this study is to use a stochastic method for producing variations of the training images that will retain the important class-wide features and thereby enrich the machine learning's “understanding” of the variabilities. As such, we applied a stochastic algorithm to produce additional realizations of the limited input imagery and thereby significantly increase the final overall accuracy in a deep learning method. We found that by enlarging the initial training set by additional realizations, we are able to consistently improve classification accuracy, compared with generic image augmentation approaches. The results of this study show that there is a great opportunity to increase the accuracy of predictions when enough data are not available. Numéro de notice : A2022-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105123 Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100672
in Computers & geosciences > vol 164 (July 2022) . - n° 105123[article]Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling / Zhijia Yang in Survey review, vol 54 n° 385 (July 2022)
[article]
Titre : Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling Type de document : Article/Communication Auteurs : Zhijia Yang, Auteur ; Wujiao Dai, Auteur ; Wenkun Yu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 290 - 300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie
[Termes IGN] auscultation d'ouvrage
[Termes IGN] barrage
[Termes IGN] déformation d'édifice
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] régression géographiquement pondérée
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) When the regression coefficient of independent variable has both global stationarity and spatio-temporal non-stationarity properties, the deformation model based on the geographically and temporally weighted regression (GTWR) will no longer be applicable. In order to resolve this problem, we propose an improved method to establish the spatio-temporal deformation model using mixed geographically and temporally weighted regression (MGTWR). In this method, both the global regression coefficient and the variable regression coefficient are selected for regression coefficient hypothesis test, and the local linear two-step estimation method is used to fit the MGTWR model. A dam deformation modelling example shows that the MGTWR model improves the average prediction accuracy by 57.6% compared to the GTWR model when the regression coefficients have both global stationarity and spatio-temporal non-stationarity properties. Numéro de notice : A2022-534 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1935578 Date de publication en ligne : 10/06/2021 En ligne : https://doi.org/10.1080/00396265.2021.1935578 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101090
in Survey review > vol 54 n° 385 (July 2022) . - pp 290 - 300[article]Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)PermalinkPolyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)PermalinkA second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkStreet-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkEncoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)PermalinkAdversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)PermalinkArtificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkAssessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkBeyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkConstraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)PermalinkContext-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkCoupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDetecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkDiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)PermalinkExtracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkFeature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images / Hanwen Xu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkGIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkHyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkInvariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkLarge-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)Permalink