ISPRS International journal of geo-information / International society for photogrammetry and remote sensing (1980 -) . vol 10 n° 12Paru le : 01/12/2021 |
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Ajouter le résultat dans votre panierAnalyzing contextual linking of heterogeneous information models from the domains BIM and UIM / Stefan F. Beck in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : Analyzing contextual linking of heterogeneous information models from the domains BIM and UIM Type de document : Article/Communication Auteurs : Stefan F. Beck, Auteur ; Jimmy Abualdenien, Auteur ; Ihab Hijazi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] CityGML
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] interopérabilité
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] système d'information géographique
[Termes IGN] système d'information urbainRésumé : (auteur) Information models from the domains Building Information Modeling (BIM) and Urban Information Modeling (UIM) are generally considered as information silos due to their heterogeneous character. These information silos can be bridged through linking where corresponding objects are identified and linked subsequently. However, whether two objects are considered as corresponding might depend on the scenario for which the links are created. The dependency of the link creation and the scenario refers to the term contextual linking and is analyzed in this paper with respect to building and city models. Therefore, different situational aspects influencing the link creation are discussed. Afterwards, the issue of contextual linking is demonstrated based on three different integration scenarios. In summary, this paper has three major outcomes: First, this paper introduces an application-oriented perspective on information integration and emphasize the role of the application when linking heterogeneous information models. Second, this paper shows that linking heterogeneous information models from the domains BIM and UIM at instance level depends on the scenario. Third, the results of the discourse about contextual linking serve as a framework supporting the design and development of artifacts for linking heterogeneous information models from the domains BIM and UIM. Numéro de notice : A2021-881 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120807 Date de publication en ligne : 28/11/2021 En ligne : https://doi.org/10.3390/ijgi10120807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99174
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 807[article]OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry Type de document : Article/Communication Auteurs : Xuan Fang, Auteur ; Jincheng Li, Auteur ; Ying Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 805 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] dommage matériel
[Termes IGN] données de terrain
[Termes IGN] érosion
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] pente
[Termes IGN] photogrammétrie aérienne
[Termes IGN] segmentation d'image
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China. Numéro de notice : A2021-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120805 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.3390/ijgi10120805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99178
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 805[article]Real-time web map construction based on multiple cameras and GIS / Xingguo Zhang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : Real-time web map construction based on multiple cameras and GIS Type de document : Article/Communication Auteurs : Xingguo Zhang, Auteur ; Xinyu Shi, Auteur ; Xiaoyue Luo, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 803 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] caméra vidéo numérique
[Termes IGN] connecteur logiciel
[Termes IGN] détection d'objet
[Termes IGN] homologie
[Termes IGN] système d'information géographique
[Termes IGN] temps réel
[Termes IGN] visualisation cartographique
[Termes IGN] web mapping
[Termes IGN] WebSIGRésumé : (auteur) Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers. Numéro de notice : A2021-883 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120803 Date de publication en ligne : 28/11/2021 En ligne : https://doi.org/10.3390/ijgi10120803 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99186
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 803[article]Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings / Hyunjung Kim in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
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Titre : Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings Type de document : Article/Communication Auteurs : Hyunjung Kim, Auteur ; Seongyong Kim, Auteur ; Kiyun Yu, Auteur Année de publication : 2021 Article en page(s) : n° 828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] indoorGML
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded. Numéro de notice : A2021-926 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120828 Date de publication en ligne : 10/12/2021 En ligne : https://doi.org/10.3390/ijgi10120828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99289
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 828[article]Deep learning for toponym resolution: Geocoding based on pairs of toponyms / Jacques Fize in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : Deep learning for toponym resolution: Geocoding based on pairs of toponyms Type de document : Article/Communication Auteurs : Jacques Fize, Auteur ; Ludovic Moncla , Auteur ; Bruno Martins, Auteur Année de publication : 2021 Article en page(s) : n° 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] échantillonnage
[Termes IGN] géocodage
[Termes IGN] matrice de co-occurrence
[Termes IGN] site wiki
[Termes IGN] toponyme
[Termes IGN] zone d'intérêtRésumé : (auteur) Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or reference database completeness. In this work, we propose a geocoding approach based on modeling pairs of toponyms, which returns latitude-longitude coordinates. One of the input toponyms will be geocoded, and the second one is used as context to reduce ambiguities. The proposed approach is based on a deep neural network that uses Long Short-Term Memory (LSTM) units to produce representations from sequences of character n-grams. To train our model, we use toponym co-occurrences collected from different contexts, namely textual (i.e., co-occurrences of toponyms in Wikipedia articles) and geographical (i.e., inclusion and proximity of places based on Geonames data). Experiments based on multiple geographical areas of interest—France, United States, Great-Britain, Nigeria, Argentina and Japan—were conducted. Results show that models trained with co-occurrence data obtained a higher geocoding accuracy, and that proximity relations in combination with co-occurrences can help to obtain a slightly higher accuracy in geographical areas with fewer places in the data sources. Numéro de notice : A2021-927 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article DOI : 10.3390/ijgi10120818 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3390/ijgi10120818 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99293
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 818[article]