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Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)
[article]
Titre : Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata Type de document : Article/Communication Auteurs : Yaqian Zhai, Auteur ; Yao Yao, Auteur ; Qingfeng Guan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1475 - 1499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aide à la décision
[Termes IGN] automate cellulaire
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] milieu urbain
[Termes IGN] morphologie
[Termes IGN] parcelle cadastrale
[Termes IGN] petite échelle
[Termes IGN] planification urbaine
[Termes IGN] précision de la classification
[Termes IGN] Shenzhen
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning. Numéro de notice : A2020-307 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1711915 Date de publication en ligne : 14/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1711915 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95149
in International journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1475 - 1499[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020071 RAB Revue Centre de documentation En réserve L003 Disponible A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)
[article]
Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement d'adresses
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] géocodage par adresse postale
[Termes IGN] gestion urbaine
[Termes IGN] inférence sémantique
[Termes IGN] représentation vectorielle
[Termes IGN] réseau neuronal profond
[Termes IGN] Shenzhen
[Termes IGN] similitude sémantique
[Termes IGN] traitement du langage naturelRésumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 Date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702
in International journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020031 RAB Revue Centre de documentation En réserve L003 Disponible Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)
[article]
Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction automatique
[Termes IGN] gestion des itinéraires
[Termes IGN] jeu de données localisées
[Termes IGN] point de repère
[Termes IGN] précision de la classification
[Termes IGN] représentation mentale spatiale
[Termes IGN] saillance
[Termes IGN] Shenzhen
[Termes IGN] villeRésumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559
in International journal of geographical information science IJGIS > vol 33 n° 12 (December 2019) . - pp 2406 - 2423[article]From 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])
[article]
Titre : From hierarchy to networking: the evolution of the “twenty-first-century Maritime Silk Road” container shipping system Type de document : Article/Communication Auteurs : Liehui Wang, Auteur ; Yan Zhu, Auteur ; César Ducruet, Auteur ; Mattia Bunel , Auteur ; Yui-yip Lau, Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 416 - 435 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chine
[Termes IGN] delta de la rivière des perles
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] port
[Termes IGN] Shanghai (Chine)
[Termes IGN] Shenzhen
[Termes IGN] théorie des graphes
[Termes IGN] trafic maritime
[Termes IGN] transport maritime
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Container shipping gives a rise of international trade since the 1960s. Based on navigation data start from the mid-1990s to 2016, this paper empirically analyses the spatial pattern of China’s international maritime linkages along the “twenty-first-century Maritime Silk Road”. We interpret such evolutionary dynamics in terms of growth, hierarchical diffusion and networking phases. Networking is a new stage of the evolution of the port system, which is approached based on the graph theory, complex network methods and geomatics, the paper discusses the networking’s basic characteristics: multi-hub spatial agglomeration, the connection of the network develops across space, functional differentiation and a division of labour appear among ports. Our results show that, while the scope of China’s maritime linkages had expanded overtime, more foreign ports become connected to the “Maritime Silk Road”. In addition, the external linkages of domestic ports tend to be dispersed, reflecting upon the decline of Pearl River Delta ports and the rise of Yangtze River Delta ports, with mixed evidence for the Bohai Rim region. Lastly, the analysis underlines the emergence of a polycentric shipping system, from the Hong Kong dominance to the more diversified Shanghai/Ningbo/Shenzhen configuration. Academic and managerial implications are included. Numéro de notice : A2018-660 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01441647.2018.1441923 Date de publication en ligne : 25/02/2018 En ligne : https://doi.org/10.1080/01441647.2018.1441923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93839
in Transport reviews > vol 38 n° 4 [01/07/2018] . - pp 416 - 435[article]A new scheme for urban impervious surface classification from SAR images / Hongsheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)
[article]
Titre : A new scheme for urban impervious surface classification from SAR images Type de document : Article/Communication Auteurs : Hongsheng Zhang, Auteur ; Hui Lin, Auteur ; Yunpeng Wang, Auteur Année de publication : 2018 Article en page(s) : pp 103 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification
[Termes IGN] Hong-Kong
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] Macao
[Termes IGN] polarimétrie radar
[Termes IGN] Shenzhen
[Termes IGN] surface imperméable
[Termes IGN] zone urbaineRésumé : (Auteur) Urban impervious surfaces have been recognized as a significant indicator for various environmental and socio-economic studies. There is an increasingly urgent demand for timely and accurate monitoring of the impervious surfaces with satellite technology from local to global scales. In the past decades, optical remote sensing has been widely employed for this task with various techniques. However, there are still a range of challenges, e.g. handling cloud contamination on optical data. Therefore, the Synthetic Aperture Radar (SAR) was introduced for the challenging task because it is uniquely all-time- and all-weather-capable. Nevertheless, with an increasing number of SAR data applied, the methodology used for impervious surfaces classification remains unchanged from the methods used for optical datasets. This shortcoming has prevented the community from fully exploring the potential of using SAR data for impervious surfaces classification. We proposed a new scheme that is comparable to the well-known and fundamental Vegetation-Impervious surface-Soil (V-I-S) model for mapping urban impervious surfaces. Three scenes of fully polarimetric Radsarsat-2 data for the cities of Shenzhen, Hong Kong and Macau were employed to test and validate the proposed methodology. Experimental results indicated that the overall accuracy and Kappa coefficient were 96.00% and 0.8808 in Shenzhen, 93.87% and 0.8307 in Hong Kong and 97.48% and 0.9354 in Macau, indicating the applicability and great potential of the new scheme for impervious surfaces classification using polarimetric SAR data. Comparison with the traditional scheme indicated that this new scheme was able to improve the overall accuracy by up to 4.6% and Kappa coefficient by up to 0.18. Numéro de notice : A2018-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89541
in ISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 103 - 118[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018051 RAB Revue Centre de documentation En réserve L003 Disponible