Centre dedocumentation
scientifique

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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1475 - 1499 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 descripteurs IGN] aide à la décision [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] morphologie [Termes descripteurs IGN] parcelle cadastrale [Termes descripteurs IGN] petite échelle [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] voisinage (topologie) 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 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1220 - 1244 Titre : Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data Type de document : Article/Communication Auteurs : Yao Yao, Auteur ; Xiaoping Liu, Auteur ; Xia Li, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1220 - 1244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] bâtiment [Termes descripteurs IGN] Canton (Kouangtoung) [Termes descripteurs IGN] cartographie statistique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données statistiques [Termes descripteurs IGN] habitat collectif [Termes descripteurs IGN] habitat urbain [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] répartition géographique Résumé : (auteur) Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p Numéro de notice : A2017-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1290252 En ligne : http://dx.doi.org/10.1080/13658816.2017.1290252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85188 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible