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Mining regional patterns of land use with adaptive adjacent criteria / Xinmeng Tu in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 418 - 431 Titre : Mining regional patterns of land use with adaptive adjacent criteria Type de document : Article/Communication Auteurs : Xinmeng Tu, Auteur ; Zhenjie Chen, Auteur ; Beibei Wang, Auteur ; changqing Xu, Auteur Année de publication : 2020 Article en page(s) : pp 418 - 431 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] adjacence [Termes IGN] analyse combinatoire (maths) [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] changement d'utilisation du sol [Termes IGN] Chine [Termes IGN] construction [Termes IGN] extraction de modèle [Termes IGN] filtrage spatiotemporel [Termes IGN] occupation du sol [Termes IGN] polygone [Termes IGN] région [Termes IGN] relation spatiale [Termes IGN] surface cultivée [Termes IGN] urbanisation [Termes IGN] utilisation du sol [Termes IGN] variogramme Résumé : (auteur) Land use/cover changes (LULC) are complicated and regionally diverse. When mining regional patterns, the use of a spatial relationship that is determined without considering the spatial correlation among geographical objects can lead to problematic results, e.g. mistakenly treating unrelated objects as adjacent. Additionally, traditional prevalence measures are unstable for uneven datasets such as LULC, wherein some land-use change types show small numbers and uneven quantities, and valuable rules for some land-use categories may be ignored. Therefore, we proposed a regional pattern mining method. First, we developed adaptive adjacent criteria, which can be automatically generated for each specific zone to define adjacency for better spatial-temporal mining. Then, a combinational decision model was built to improve the stability of the prevalence measure, which was used to filter out the insignificant spatial-temporal rules. Furthermore, we proposed two levels of land-use pattern mining, i.e. cluster-level mining and polygon-level mining, to first discover hot-spot areas where similar land-use change has occurred frequently and then to determine the location, frequency, and change time of rules related to different land-use activities. The proposed method was used for mining the dependence of land use and regional patterns on land-use changes. Results show that the proposed method can determine the spatial dependence between the land-use categories, as well as regional patterns of land-use changes. According to our research, the study area, Xinbei District, China, is undergoing land-use change involving rapid urbanization, extensive transportation construction, and losses of farmland. Numéro de notice : A2020-487 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1761452 Date de publication en ligne : 18/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1761452 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95655 [article]

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032-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 432 - 451 Titre : Comparing pedestrians’ gaze behavior in desktop and in real environments Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Hua Liao, Auteur ; Bing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 432 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] analyse visuelle [Termes IGN] comportement [Termes IGN] espace urbain [Termes IGN] lecture de carte [Termes IGN] monde virtuel [Termes IGN] navigation pédestre [Termes IGN] oculométrie [Termes IGN] piéton [Termes IGN] test statistique [Termes IGN] travail [Termes IGN] vision par ordinateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This research is motivated by the widespread use of desktop environments in the lab and by the recent trend of conducting real-world eye-tracking experiments to investigate pedestrian navigation. Despite the existing significant differences between the real world and the desktop environments, how pedestrians’ visual behavior in real environments differs from that in desktop environments is still not well understood. Here, we report a study that recorded eye movements for a total of 82 participants while they were performing five common navigation tasks in an unfamiliar urban environment (N = 39) and in a desktop environment (N = 43). By analyzing where the participants allocated their visual attention, what objects they fixated on, and how they transferred their visual attention among objects during navigation, we found similarities and significant differences in the general fixation indicators, spatial fixation distributions and attention to the objects of interest. The results contribute to the ongoing debate over the validity of using desktop environments to investigate pedestrian navigation by providing insights into how pedestrians allocate their attention to visual stimuli to accomplish navigation tasks in the two environments. Numéro de notice : A2020-488 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.176251 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/15230406.2020.1762513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95658 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Local terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 452 - 470 Titre : Local terrain modification method considering physical feature constraints for vector elements Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Junyan Liu, Auteur ; Junzhong Tan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 452 - 470 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] altitude [Termes IGN] analyse vectorielle [Termes IGN] contrainte d'intégrité [Termes IGN] déformation de surface [Termes IGN] données vectorielles [Termes IGN] interpolation [Termes IGN] processeur graphique [Termes IGN] rastérisation [Termes IGN] relief [Termes IGN] superposition de données [Termes IGN] surface du sol [Termes IGN] terrain [Termes IGN] traitement parallèle [Termes IGN] zone tampon Résumé : (auteur) Many studies have been focused on rendering 2D vector elements on 3D terrain, and a series of algorithms have been proposed. Most of these algorithms struggle to provide a seamless overlay between vector elements and an irregular terrain surface. Despite their importance, the physical characteristics of vector elements are often ignored, which distorts the surface of vector elements. For example, if vector elements that represent roads and rivers are simply overlaid on terrain, the phenomena of uneven surfaces and rivers going uphill may occur because of elevation fluctuation. To correct these deficiencies, terrain should be modified according to the physical characteristics of vectors. We propose a local terrain modification method: First, the elevation of terrain covered by vector elements is recalculated according to vectors’ physical characteristics. Second, the multigrid method is used to realize a smooth transition between the modified terrain and its surrounding area. Finally, by setting different transition ranges and comparing the visualization effects, rules are given for the selection of a suitable range. After modification, the terrain conforms to vectors’ physical characteristics, and the overall relief is undamaged. The proposed method was applied to a CPU–GPU parallel heterogeneous model and demonstrated a high level of performance. Numéro de notice : A2020-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1770128 Date de publication en ligne : 06/07/2020 En ligne : https://doi.org/10.1080/15230406.2020.1770128 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95660 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Recognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 400 - 417 Titre : Recognition of building group patterns using graph convolutional network Type de document : Article/Communication Auteurs : Rong Zhao, Auteur ; Tinghua Ai, Auteur ; Wenhao Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 400 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données topographiques [Termes IGN] espace urbain [Termes IGN] généralisation du bâti [Termes IGN] graphe [Termes IGN] modélisation du bâti [Termes IGN] reconnaissance de formes Résumé : (auteur) Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods. Numéro de notice : A2020-510 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1757512 Date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1757512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95663 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible