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Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos / Yu Feng in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Monika Sester, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage profond [Termes IGN] Berlin [Termes IGN] cartographie des risques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] inondation [Termes IGN] Londres [Termes IGN] Paris (75) [Termes IGN] protection civile [Termes IGN] risque naturel [Termes IGN] zone sinistrée [Termes IGN] zone urbaine Résumé : (Auteur) In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin. Numéro de notice : A2018-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020039 En ligne : https://doi.org/10.3390/ijgi7020039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89530 [article]

3D visualization of trees based on a sphere-board model / Jiangfeng She in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : 3D visualization of trees based on a sphere-board model Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Xingchen Guo, Auteur ; Xin Tan, Auteur ; Jianlong Liu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre (flore) [Termes IGN] modèle de croissance végétale [Termes IGN] rendu réaliste [Termes IGN] sphère [Termes IGN] visualisation 3D [Vedettes matières IGN] Foresterie Résumé : (Auteur) Because of the smooth interaction of tree systems, the billboard and crossed-plane techniques of image-based rendering (IBR) have been used for tree visualization for many years. However, both the billboard-based tree model (BBTM) and the crossed-plane tree model (CPTM) have several notable limitations; for example, they give an impression of slicing when viewed from the top side, and they produce an unimpressive stereoscopic effect and insufficient lighted effects. In this study, a sphere-board-based tree model (SBTM) is proposed to eliminate these defects and to improve the final visual effects. Compared with the BBTM or CPTM, the proposed SBTM uses one or more sphere-like 3D geometric surfaces covered with a virtual texture, which can present more details about the foliage than can 2D planes, to represent the 3D outline of a tree crown. However, the profile edge presented by a continuous surface is overly smooth and regular, and when used to delineate the outline of a tree crown, it makes the tree appear very unrealistic. To overcome this shortcoming and achieve a more natural final visual effect of the tree model, an additional process is applied to the edge of the surface profile. In addition, the SBTM can better support lighted effects because of its cubic geometrical features. Interactive visualization effects for a single tree and a grove are presented in a case study of Sabina chinensis. The results show that the SBTM can achieve a better compromise between realism and performance than can the BBTM or CPTM. Numéro de notice : A2018-106 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020039 En ligne : https://doi.org/10.3390/ijgi7020045 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89532 [article]

Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur ; Shihong Du, Auteur ; Chen-Chieh Feng, Auteur ; Xueying Zhang, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] langage naturel (informatique) [Termes IGN] relation sémantique [Termes IGN] relation topologique [Termes IGN] toponyme flou Résumé : (Auteur) Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts. Numéro de notice : A2018-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020058 En ligne : https://doi.org/10.3390/ijgi7020058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89533 [article]