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GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science / Jiaxin Du in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 873 - 897 Titre : GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science Type de document : Article/Communication Auteurs : Jiaxin Du, Auteur ; Shaohua Wang, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 873 - 897 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] exploration de données [Termes IGN] ingénierie des connaissances [Termes IGN] ontologie [Termes IGN] recherche d'information géographique [Termes IGN] réseau sémantique [Termes IGN] traitement du langage naturel Résumé : (auteur) An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes. Numéro de notice : A2022-341 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2005795 Date de publication en ligne : 26/11/2021 En ligne : https://doi.org/10.1080/13658816.2021.2005795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100515 [article]

A knowledge representation model based on the geographic spatiotemporal process / Kun Zheng in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 674 - 691 Titre : A knowledge representation model based on the geographic spatiotemporal process Type de document : Article/Communication Auteurs : Kun Zheng, Auteur ; Ming Hui Xie, Auteur ; Jin Biao Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 674 - 691 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] approche hiérarchique [Termes IGN] ontologie [Termes IGN] raisonnement spatiotemporel [Termes IGN] représentation des connaissances [Termes IGN] représentation du changement [Termes IGN] représentation géographique [Termes IGN] réseau sémantique Résumé : (auteur) Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. Numéro de notice : A2022-255 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1962527 Date de publication en ligne : 05/08/2021 En ligne : https://doi.org/10.1080/13658816.2021.1962527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100228 [article]

Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 645 - 668 Titre : Spatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran Type de document : Article/Communication Auteurs : Naeim Mijani, Auteur ; Davoud Shahpari Sani, Auteur ; Mohsen Dastaran, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 645 - 668 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] approche hiérarchique [Termes IGN] changement climatique [Termes IGN] coefficient de corrélation [Termes IGN] combinaison linéaire ponderée [Termes IGN] données démographiques [Termes IGN] données socio-économiques [Termes IGN] Iran [Termes IGN] migration humaine [Termes IGN] modélisation spatiale [Termes IGN] planification urbaine [Termes IGN] système d'information géographique Résumé : (auteur) Spatial modeling of migration and the identification of the effective parameters are imperative for planning and managing demographic, economic, social, and environmental changes on various geographical scales. The recent climate change stressors as well as inequality in terms of education and life quality have triggered internal mass migrations in Iran, causing pressure on housing, the job market, and potential slums around large cities. This study proposes a new approach to modeling migration patterns in Iran based on multi-criteria decision analysis. For this purpose, a total of 23 individual criteria embedded within four criteria groups (economic, socio-cultural, welfare, and environmental) affecting national migration were used. The analytic hierarchy process was employed to determine weights for the input factors and the weighted linear combination (WLC) model was used for the integration of criteria, based on which maps of migration potential were produced. The model applied was evaluated based on the correlation coefficient between migration potential values obtained from the WLC model and the actual net migration rate. Among the input individual criteria, unemployment, higher education centers, number of physicians, and dust storms were found to influence national migration. Furthermore, our findings reveal that the potential for migration across Iranian provinces is heterogeneous, with the spatial potential for emigration being the highest and lowest in the border and central provinces, respectively. The correlation coefficient calculated between outputs from the WLC model and the net migration rate from 2011 to 2016, was .81, indicating the relatively high performance of the proposed model in producing a migration spatial potential map. Our proposed approach, along with the results achieved, can be useful to decision-makers and planners in designing data-driven policies against inequality- and climate-induced stressors. Numéro de notice : A2022-363 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12873 Date de publication en ligne : 23/11/2021 En ligne : https://doi.org/10.1111/tgis.12873 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100582 [article]

CNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 7 (July 2021) . - pp 2076 - 2096 Titre : CNN-based RGB-D salient object detection: Learn, select, and fuse Type de document : Article/Communication Auteurs : Hao Chen, Auteur ; Yongjian Deng, Auteur ; Guosheng Lin, Auteur Année de publication : 2021 Article en page(s) : pp 2076 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image RVB [Termes IGN] profondeur [Termes IGN] saillance [Termes IGN] segmentation sémantique Résumé : (auteur) The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection, and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks. Numéro de notice : A2021-697 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-021-01452-0 Date de publication en ligne : 05/05/2021 En ligne : https://doi.org/10.1007/s11263-021-01452-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98532 [article]

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 38 - 56 Titre : A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases Type de document : Article/Communication Auteurs : Chun Yang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] Allemagne [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image aérienne [Termes IGN] jointure [Termes IGN] objet géographique [Termes IGN] occupation du sol [Termes IGN] optimisation (mathématiques) [Termes IGN] utilisation du sol Résumé : (Auteur) Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels. The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics. Numéro de notice : A2021-370 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.022 Date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97774 [article]

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