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A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery / Zewei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 423 - 434 Titre : A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery Type de document : Article/Communication Auteurs : Zewei Xu, Auteur ; Kaiyu Guan, Auteur ; Nathan Casler, Auteur ; Bin Peng, Auteur ; Shaowen Wang, Auteur Année de publication : 2018 Article en page(s) : pp 423 - 434 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] Illinois (Etats-Unis) [Termes IGN] image Landsat [Termes IGN] image multitemporelle [Termes IGN] réseau neuronal convolutif [Termes IGN] semis de points Résumé : (Auteur) Terrestrial landscape has complex three-dimensional (3D) features that are difficult to extract using traditional methods based on 2D representations. These methods often relegate such features to raster or metric-based (two-dimensional) representations based on Digital Surface Models (DSM) or Digital Elevation Models (DEM), and thus are not suitable for resolving morphological and intensity features for fine-scale land cover mapping. Small-footprint LiDAR provides an ideal way for capturing these 3D features. This research develops a novel method of integrating airborne LiDAR derived features and multi-temporal Landsat images to classify land cover types. We tested our approach in Williamson County, Illinois, which has diverse and mixed landscape features. Specifically, our method applied a 3D convolutional neural network (CNN) approach to extract features from LiDAR point clouds by (1) creating an occupancy grid, an intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into the 3D CNN. The extracted features (e.g., morphological and intensity features) from the 3D CNN were finally combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. Visual interpretation from both hyper-resolution photos and point clouds was used for training and preparation of testing data. The classification results show that our method outperforms a traditional method by 2.65% (from 81.52% to 84.17%) when solely using LiDAR and 2.19% (from 90.20% to 92.57%) when combining all available imageries. We demonstrate that our method can effectively extract LiDAR features and improve fine-scale land cover mapping through fusion of complementary types of remote sensing data. Numéro de notice : A2018-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.005 Date de publication en ligne : 22/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90859 [article]

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Spatial big data and machine learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 28 August 2018 / Martin Raubal (2018)    

Public Titre : Spatial big data and machine learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 28 August 2018 : Proceedings Type de document : Actes de congrès Auteurs : Martin Raubal, Éditeur scientifique ; Shaowen Wang, Éditeur scientifique ; Mengyu Guo, Éditeur scientifique ; David Jonietz, Éditeur scientifique ; Peter Kiefer, Éditeur scientifique Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2018 Conférence : Workshop 2018 on Spatial big data and machine learning 28/08/2018 28/08/2018 Melbourne Australie OA Proceedings Importance : 53 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] apprentissage automatique [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] interface homme-machine [Termes IGN] trajectoire (véhicule non spatial) Note de contenu : PART 1: TRAJECTORIES - Stay-Move Tree for Summarizing Spatiotemporal Trajectories / Eun-Kyeong Kim - Detection of Unsigned Ephemeral Road Incidents by Visual Cues / Alex Levering, Kourosh Khoshelham, Devis Tuia, and Martin Tomko - Convolutional Neural Network for Traffic Signal Inference based on GPS Traces / Y. Méneroux, V. Dizier, M. Margollé, M.D. Van Damme, H.Kanasugi, A. Le Guilcher, G. Saint Pierre, and Y. Kato - Using Stream Processing to Find Suitable Rides: An Exploration based on New York City Taxi Data / Roswita Tschümperlin, Dominik Bucher, and Joram Schito - Classification of regional dominant movement patterns in trajectories with a convolutional neural network / Can Yang and Gyozo Gidófalvi PART 2: COGNITION & HCI - Spatial Big Data for Human-Computer Interaction / Ioannis Giannopoulos - Unsupervised Clustering of Eye Tracking Data / Fabian Göbel and Henry Martin - Collections of Points of Interest: How to Name Them and Why it Matters / Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Rui Zhu, Bo Yan, Grant McKenzie, Anagha Uppal, and Blake Regalia PART 3: SPATIAL PATTERNS - A Multi-scale Spatio-temporal Approach to Analysing the changing inequality in the Housing Market during 2001-2014 / Yingyu Feng and Kelvyn Jones - Automated social media content analysis from urban green areas – Case Helsinki / Vuokko Heikinheimo, Henrikki Tenkanen, Tuomo Hiippala, Olle Järv, and Tuuli Toivonen - Long short-term memory networks for county-level corn yield estimation / Haifeng Li, Yudi Wang, Renhai Zhong, Hao Jiang, and Tao Lin - Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering / Torben Peters and Claus Brenner Numéro de notice : 17546 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Actes En ligne : http://spatialbigdata.ethz.ch/index.php/proceedings/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91338

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• Convolutional neural network for traffic signal inference based on GPS traces / Yann Méneroux (2018) 

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Depicting urban boundaries from a mobility network of spatial interactions : a case study of Great Britain with geo-located Twitter data / Junjun Yin in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1293 - 1313 Titre : Depicting urban boundaries from a mobility network of spatial interactions : a case study of Great Britain with geo-located Twitter data Type de document : Article/Communication Auteurs : Junjun Yin, Auteur ; Aiman Soliman, Auteur ; Dandong Yin, Auteur ; Shaowen Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1293 - 1313 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] comportement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données socio-économiques [Termes IGN] géographie humaine [Termes IGN] Grande-Bretagne [Termes IGN] interaction humain-espace [Termes IGN] limite administrative [Termes IGN] mobilité urbaine [Termes IGN] réseau social [Termes IGN] trace GPS [Termes IGN] urbanisation Résumé : (Auteur) Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space. Numéro de notice : A2017-303 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1282615 En ligne : http://dx.doi.org/10.1080/13658816.2017.1282615 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85350 [article]

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Parallel cartographic modeling: a methodology for parallelizing spatial data processing / Eric Shook in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 11-12 (November - December 2016) . - pp 2355 - 2376 Titre : Parallel cartographic modeling: a methodology for parallelizing spatial data processing Type de document : Article/Communication Auteurs : Eric Shook, Auteur ; Michael E. Hodgson, Auteur ; Shaowen Wang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 2355 - 2376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] langage de programmation [Termes IGN] Map Algebra [Termes IGN] modèle conceptuel de données localisées [Termes IGN] Python (langage de programmation) [Termes IGN] traitement de données localisées [Termes IGN] traitement parallèle [Termes IGN] WebSIG Résumé : (Auteur) This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML. Numéro de notice : A2016-755 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1172714 En ligne : http://dx.doi.org/10.1080/13658816.2016.1172714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82420 [article]

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TeraGrid GIScience Gateway: Bridging cyberinfrastructure and GIScience / Shaowen Wang in International journal of geographical information science IJGIS, vol 23 n° 5 (may 2009)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n° 5 (may 2009) . - pp 631 - 656 Titre : TeraGrid GIScience Gateway: Bridging cyberinfrastructure and GIScience Type de document : Article/Communication Auteurs : Shaowen Wang, Auteur ; Y. Liu, Auteur Année de publication : 2009 Article en page(s) : pp 631 - 656 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse spatiale [Termes IGN] architecture orientée services [Termes IGN] base de données répartie [Termes IGN] cyberinfrastructure [Termes IGN] découverte de connaissances [Termes IGN] géomatique web [Termes IGN] traitement parallèle [Termes IGN] traitement réparti [Termes IGN] web 2.0 [Termes IGN] WebSIG Résumé : (Auteur) Cyberinfrastructure (CI) represents the integrated information and communication technologies for distributed information processing and coordinated knowledge discovery, and is promising to revolutionize how science and engineering are conducted in the twenty-first century. The value of bridging CI and GIScience is significant to advance CI and benefit GIScience research and education, particularly in distributed geographic information processing (DGIP). This article presents a holistic framework that bridges CI and GIScience by integrating CI capabilities to empower GIScience research and education and establish generic DGIP services supported by CI. The framework, the TeraGrid GIScience Gateway, is based on a CI science gateway approach developed on the National Science Foundation (NSF) TeraGrid - a key element of US and world CI. This gateway develops a unifying service-oriented framework with respect to its architecture, design, and implementation as well as its integration with the TeraGrid. The functions of the gateway focus on enabling parallel and distributed processing for geographical analysis, managing the complexity of TeraGrid software environment, and establishing a Web-based GIS for the GIScience community to gain shared and collaborative access to TeraGrid-based geospatial processing services. The gateway implementation uses Web 2.0 technologies to create a highly configurable and interactive multiuser environment. Two case studies, Bayesian geostatistical modeling and a spatial statistic Gi(d) for detecting local clustering, are used to demonstrate the gateway functions and user environment. The service transformation for these analyses is applied to create a shared, decentralized, and collaborative geographical analysis environment in which GIScience community users can contribute new analysis services and reuse existing gateway services. Copyright Taylor & Francis Numéro de notice : A2009-301 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810902754977 En ligne : https://doi.org/10.1080/13658810902754977 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29931 [article]

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A theoretical approach to the use of cyberinfrastructure in geographical analysis / Shaowen Wang in International journal of geographical information science IJGIS, vol 23 n° 1-2 (january 2009)  

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