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A graph-based approach for representing addresses in geocoding / Chen Zhang in Computers, Environment and Urban Systems, vol 100 (March 2023)
[article]
Titre : A graph-based approach for representing addresses in geocoding Type de document : Article/Communication Auteurs : Chen Zhang, Auteur ; Biao He, Auteur ; Renzhong Guo, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101937 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] appariement d'adresses
[Termes IGN] base de données d'adresses
[Termes IGN] géocodage par adresse postale
[Termes IGN] graphe
[Termes IGN] stockage de données
[Termes IGN] toponymeRésumé : (auteur) Addresses, one of the most important geographical reference systems in natural languages, are usually used to search spatial objects in daily life. Geocoding concatenates text with georeferenced coordinates and is an essential middleware service in geographic information applications. Despite its importance, geocoding remains challenging with only text as input, hindering text matching in reference databases without the specific text. To optimize the storage and retrieval of addresses in databases, this work proposes a graph-based approach for representing addresses. The approach clarifies the characteristics of relative concepts, designs a graph structure and identifies modelling strategies. Furthermore, a schema is proposed to perform address matching and toponym disambiguation using an address graph. The model is implemented on a graph database, and experimental tasks are employed to demonstrate its effectiveness. The approach provides a new reference for developers when creating address databases. Numéro de notice : A2023-126 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101937 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101937 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102505
in Computers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101937[article]A geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)
[article]
Titre : A geometry-aware attention network for semantic segmentation of MLS point clouds Type de document : Article/Communication Auteurs : Jie Wan, Auteur ; Yongyang Xu, Auteur ; Qinjun Qiu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 138 - 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] corrélation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] figure géométrique
[Termes IGN] fonction de perte
[Termes IGN] graphe
[Termes IGN] Perceptron multicouche
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Semantic segmentation of mobile laser scanning (MLS) point clouds can provide meaningful 3 D semantic information of urban facilities for various applications. However, it still remains a challenge to extract accurate 3 D semantic information from MLS point cloud data due to its irregular 3 D geometric structure in a large-scale outdoor scene. To this end, this study develops a geometry-aware attention point network (GAANet) with geometric properties of the point cloud as a reference. Specifically, the proposed method first builds a graph-like region for each input point to establish the geometric correlation toward its neighbors for robustly descripting local geometry-aware features. Thereafter, the method introduces a novel multi-head attention mechanism to efficiently learn local discriminative features on the constructed graphs and a feature combination operation to capture both local and global geometric dependencies inside fused point features for significantly facilitating the segmentation of small or incomplete 3 D objects at point-level. Finally, an adaptive loss function is appended to handle class imbalance for the overall performance improvement. The validation experiments on two challenging benchmarks demonstrate the effectiveness and powerful generation ability of the proposed method, which achieves state-of-the-art performance with mean IoU of 65.09% and 95.20% in the Toronto-3D and Oakland 3-D MLS dataset, respectively. Numéro de notice : A2023-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658816.2022.2111572 Date de publication en ligne : 24/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2111572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102309
in International journal of geographical information science IJGIS > vol 37 n° 1 (January 2023) . - pp 138 - 161[article]A hierarchical multiview registration framework of TLS point clouds based on loop constraint / Hao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)
[article]
Titre : A hierarchical multiview registration framework of TLS point clouds based on loop constraint Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Li Yan, Auteur ; Hong Xie, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 65 - 76 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] appariement de points
[Termes IGN] approche hiérarchique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] recalage d'image
[Termes IGN] semis de points
[Termes IGN] superposition de données
[Termes IGN] traitement de semis de pointsRésumé : (auteur) Automatic registration of multiple point clouds is a significant preprocessing step for 3D computer vision tasks including semantic segmentation, 3D modelling, change detection, etc. Many methods were proposed to deal with this problem and yet most of them are not fully utilizing the redundant information offered by multiple common overlaps among point clouds. The existing methods are also inefficient when dealing with large-scale point clouds. In this paper, a novel automatic registration framework is presented to align point clouds efficiently and robustly. First, the overall number of scans is grouped into several scan-blocks by a proposed blocking strategy, and we build the pairwise relationship among scans through a fully connected graph in each scan-block. Second, perform loop-based coarse registration in each scan-block using a proposed false matches removal strategy. The proposed strategy can effectively identify grossly wrong scan-to-scan matches. Third, the minimum spanning tree is extracted from the graph, and ICP is applied along its edges. Moreover, the Lu–Milios algorithm is used to further optimize all poses at once by utilizing all redundant information in each scan-block. Finally, global block-to-block registration aligns all scan-blocks into a uniform coordinate reference. We test our framework on challenging WHU-TLS datasets, ETH datasets, and Robotic 3D Scan datasets to evaluate the efficiency, accuracy, as well as robustness. The experiment results show that our method achieves the state-of-the-art accuracy, while the time performance is improved by more than 30% compared with the state-of-the-art algorithms. Our source code is made available at https://github.com/WuHao-WHU/HL-MRF for benchmarking purposes. Numéro de notice : A2023-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.11.004 Date de publication en ligne : 19/11/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.11.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102112
in ISPRS Journal of photogrammetry and remote sensing > vol 195 (January 2023) . - pp 65 - 76[article]
Titre : Structured learning of geospatial data Type de document : Thèse/HDR Auteurs : Loïc Landrieu , Auteur Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2023 Importance : 179 p. Format : 21 x 30 cm Note générale : Bibliographie
Habilitation à Diriger des Recherches délivrée par l'Université Gustave Eiffel, Spécialité "Sciences et Technologies de l'Information Géographique"Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] apprentissage automatique
[Termes IGN] carte agricole
[Termes IGN] graphe
[Termes IGN] lasergrammétrie
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] vision par ordinateurRésumé : (auteur) This manuscript presents an overview of my work in the field of geospatial machine learning, a rapidly growing interdisciplinary field that poses many methodological challenges and has a wide range of impactful applications. Throughout my research, I have focused on developing bespoke approaches that leverage the unique properties of geospatial data to create more efficient, precise, and parsimonious models. This manuscript is divided into four main chapters, each covering a different property of geospatial data structures that can be leveraged algorithmically. The first chapter presents a versatile mathematical framework formalizing the concept of spatial regularity with graphs. We propose an efficient algorithm that tackles a broad family of spatial problems and provides novel convergence guarantees and significant speed-ups compared to generic approaches. The second chapter introduces a deep learning method that extends the idea of exploiting graph regularity to the case of massive 3D point clouds. We simplify the task of large-scale semantic segmentation by formulating it as as a small graph labelling problem. Our compact models reach high precision at a fraction of the computational cost of other approaches. In the third chapter, we present a collection of methods designed to take advantage of the data structure inherited from 3D sensors. By considering the sensors’ structure, we develop powerful networks with state-of-the-art accuracy, latency, and robustness for various applications and data types. The last chapter dives into the real-life challenge of automated satellite time series analysis for crop mapping. Recognizing the difference between such data and standard formats used in computer vision, we propose novel and streamlined architectures that achieve unprecedented precision while remaining efficient and economical in memory and preprocessing. We also introduce the task of panoptic segmentation for satellite time series and an efficient architecture to solve this problem at scale. In summary, this manuscript argues that geospatial problems represent a challenging and impactful venue for evaluating the newest machine learning and vision methods and a fertile source of inspiration for designing novel approaches. Note de contenu : 1- Introduction
2- Exploiting graph regularity
3- Exploiting the spatial regularity of 3D data
4- Exploiting the structure of 3D sensors
5- Exploiting the structure of satellite time series
6- Perspectives
7- Curriculum vitaeNuméro de notice : 24107 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : HDR Note de thèse : HDR: Sciences et Technologies de l’Information Geographique : UGE : 2023 Organisme de stage : LASTIG (IGN) DOI : sans En ligne : https://hal.science/tel-04095452v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103248 Geospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India / Nupur Rautela in Geocarto international, vol 37 n° 27 ([20/12/2022])
[article]
Titre : Geospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India Type de document : Article/Communication Auteurs : Nupur Rautela, Auteur ; Saurabh Shanu, Auteur ; Alok Agarwal, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 15114 - 15142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] chevauchement
[Termes IGN] corridor biologique
[Termes IGN] faune locale
[Termes IGN] graphe
[Termes IGN] habitat animal
[Termes IGN] Inde
[Termes IGN] modélisation spatiale
[Termes IGN] système complexeRésumé : (auteur) Wildlife corridors in a landscape include local vegetation, topography, prey base, water and are associated with isolated wildlife habitat patches. They facilitate maintenance of ecological structure and function as well as provide connectivity to faunal populations supporting genetic transfers, and are elements critical to wildlife management. In this work, habitat patches for tiger, both inside as well as outside of Protected Areas have been identified by developing a Habitat Suitability Index model utilizing Remote Sensing and Geographical Information System datasets for the Terai Arc landscape, India. By using a computational approach based on the framework of theory of complex networks, for exclusively pairwise interactions between the habitat patches, a potential tiger corridor network has been structurally identified and studied in this landscape. The interactions between these habitat patches on a spatial scale has been analyzed as a clique of the corridor network. Further, the Clique Percolation Method has been applied to detect overlapping communities of habitat patches in the landscape. The Cliques required for maintaining contiguity between the habitat patches in order to support tiger movement are validated using field observations of tiger communities within the landscape matrix. The model developed for identification of tiger corridors in this study could potentially be of a vital importance for wildlife stakeholders to better understand and manage tiger populations both within and outside of protected areas. The study also highlights Critical Habitat Patches and their importance in maintaining landscape connectivity for tiger dispersal in the landscape. Using a report published by the Government of India as a benchmark, the model presented in the work is found to have an accuracy of 90.73% in predicting tiger carrying patches and the corridor network in the focal landscape. Numéro de notice : A2022-933 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2095444 Date de publication en ligne : 14/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2095444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102670
in Geocarto international > vol 37 n° 27 [20/12/2022] . - pp 15114 - 15142[article]Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkA relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)PermalinkSpatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)PermalinkLocation-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)PermalinkA 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)PermalinkGeodesic geometry on graphs / Daniel Cizma in Discrete & computational geometry, vol 68 n° 1 (July 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)PermalinkGraph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (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)PermalinkTrue orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)PermalinkNavigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)PermalinkA graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkGraph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkUsing vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkTowards expressive graph neural networks : Theory, algorithms, and applications / Georgios Dasoulas (2022)PermalinkA topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkBinary space partitioning visibility tree for polygonal and environment light rendering / Hiroki Okuno in The Visual Computer, vol 37 n° 9 - 11 (September 2021)PermalinkA typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkConstrained shortest path problems in bi-colored graphs: a label-setting approach / Amin AliAbdi in Geoinformatica, vol 25 n° 3 (July 2021)PermalinkA scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)PermalinkA topology-preserving simplification method for 3D building models / Biao Wang in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkA Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])PermalinkA new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)PermalinkA BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkGraph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkIdentification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust / Kamil Kowalczyk in Journal of applied geodesy, vol 15 n° 2 (April 2021)PermalinkGraph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkA spatiotemporal structural graph for characterizing land cover changes / Bin Wu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)Permalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)PermalinkPermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkDétection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond / Victor Alteirac (2021)PermalinkFinding the most navigable path in road networks / Ramneek Kaur in Geoinformatica, vol 25 n° 1 (January 2021)PermalinkHyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition / Yuanyang Bu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkInferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)PermalinkIntroducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)PermalinkLearning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)PermalinkPermalinkObject detection using component-graphs and ConvNets with application to astronomical images / Thanh Xuan Nguyen (2021)PermalinkPermalinkNonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkNEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread / Taylor Anderson in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkRecognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)PermalinkA semantic graph database for the interoperability of 3D GIS data / Eva Savina Malinverni in Applied geomatics, vol 12 n° 3 (September 2020)PermalinkComment cartographier l’occupation du sol en vue de modéliser les réseaux écologiques ? Méthodologie générale et cas d’étude en Île-de-France / Chloé Thierry in Sciences, eaux & territoires, article hors-série n° 65 (mai 2020)PermalinkA point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkLearning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkPermalinkPermalinkGeographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)PermalinkImage processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)PermalinkPermalinkPermalinkA space-time varying graph for modelling places and events in a network / Ikechukwu Maduako in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)PermalinkAnalysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)PermalinkComputing and querying strict, approximate, and metrically refined topological relations in linked geographic data / Blake Regalia in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkDeeply integrating linked data with geographic information systems / Gengchen Mai in Transactions in GIS, vol 23 n° 3 (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)PermalinkA graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkPoint clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkPermalinkPermalinkSpatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)PermalinkPermalinkPermalinkVectorisation du cadastre ancien : restructuration de la chaîne de traitement, implémentation d’une nouvelle méthode de détection et utilisation de la théorie des graphes / Antony Chalais (2019)PermalinkUn algorithme pour battre le record du SwissTrainChallenge : poser le pied dans chacun des 26 cantons le plus rapidement possible en utilisant uniquement des transports publics / Emmanuel Clédat in XYZ, n° 157 (décembre 2018 - février 2019)PermalinkAn algorithm for on-the-fly K shortest paths finding in multi-storey buildings using a hierarchical topology model / Rosen Ivanov in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)PermalinkA context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkFrom hierarchy to networking: the evolution of the “twenty-first-century Maritime Silk Road” container shipping system / Liehui Wang in Transport reviews, vol 38 n° 4 ([01/07/2018])PermalinkL’opérateur de collage : Gestion de plusieurs points de vue dans un contexte spatial / Géraldine Del Mondo in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)PermalinkPré-estimation et analyse de la précision pour la cartographie par drone / Laurent Valentin Jospin in XYZ, n° 155 (juin - août 2018)PermalinkA voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkA novel orthoimage mosaic method using a weighted A∗ algorithm : Implementation and evaluation / Maoteng Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkSpace-time tree ensemble for action recognition and localization / Shugao Ma in International journal of computer vision, vol 126 n° 2-4 (April 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkGenerative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkProgressive registration of image features and 3D vector lines for orientation modelling / Wen-Chi Chang in Photogrammetric record, vol 33 n° 161 (March 2018)PermalinkLRAGE : learning latent relationships with adaptive graph embedding for aerial scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkNouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 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)PermalinkPermalinkCut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation / Hugo Raguet (2018)PermalinkPermalinkPermalinkPermalinkCentrality-based hierarchy for street network generalization in multi-resolution maps / Wasim Shoman in Geocarto international, vol 32 n° 12 (December 2017)PermalinkCut Pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs / Loïc Landrieu in SIAM Journal on Imaging Sciences, vol 10 n° 4 (November 2017)Permalink