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Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation / Ruijing Li in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation Type de document : Article/Communication Auteurs : Ruijing Li, Auteur ; Jianzhong Guo, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 440 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] distance
[Termes IGN] filtrage d'information
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] point d'intérêt
[Termes IGN] réseau social géodépendant
[Termes IGN] Tokyo (Japon)Résumé : (auteur) With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. Numéro de notice : A2022-647 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080440 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.3390/ijgi11080440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101463
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 440[article]A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])
[article]
Titre : A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway Type de document : Article/Communication Auteurs : Reza Sanayeia, Auteur ; Alireza Vafaeinejad, Auteur ; Jalal Karami, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4141 - 4157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] accident de la route
[Termes IGN] autocorrélation
[Termes IGN] autoroute
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] Téhéran
[Termes IGN] transformation en ondelettesRésumé : (auteur) The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82. Numéro de notice : A2022-650 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1871669 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1080/10106049.2021.1871669 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101472
in Geocarto international > vol 37 n° 14 [20/07/2022] . - pp 4141 - 4157[article]Segmentation and sampling method for complex polyline generalization based on a generative adversarial network / Jiawei Du in Geocarto international, vol 37 n° 14 ([20/07/2022])
[article]
Titre : Segmentation and sampling method for complex polyline generalization based on a generative adversarial network Type de document : Article/Communication Auteurs : Jiawei Du ; Fang Wu, Auteur ; Ruixing Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4158 - 4180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage de données
[Termes IGN] implémentation (informatique)
[Termes IGN] polyligne
[Termes IGN] rastérisation
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate. Numéro de notice : A2022-651 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1878288 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1080/10106049.2021.1878288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101473
in Geocarto international > vol 37 n° 14 [20/07/2022] . - pp 4158 - 4180[article]PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan / Sajid Hussain in Geocarto international, vol 37 n° 13 ([15/07/2022])
[article]
Titre : PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan Type de document : Article/Communication Auteurs : Sajid Hussain, Auteur ; Sun Hongxing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3941 - 3962 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] aléa
[Termes IGN] effondrement de terrain
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] PakistanRésumé : (auteur) Northern Pakistan is a rugged mountainous area that is seismically active, high gradients, disintegrated lithology, and glaciers in the high peaks. District Ghizer lies among the most vulnerable areas and experience landslides every year due to different causative factors. This study has carried out to prepare a detailed landslide inventory and to develop a susceptibility model for the area. The most followed and probabilistic approach, Frequency Ratio (FR) model and a semi-qualitative Analytical Hierarchy Process (AHP) approach were applied to find the correlation between causative factors and mapped landslides. Persistent Scatterer Interferometry (PSI) Interferometric Synthetic Aperture Radar (InSAR) technique was applied to check deformation movement in the susceptible zones of extracted models, which showed the high Line of Sight (LOS) deformation velocity in high susceptible zones of both models. The extracted Landslide Susceptibility Index (LSI) models showed 82.82% and 73.43% of prediction accuracy for FR and AHP method calculated by Area Under Curve (AUC) of Receiver operating characteristic (ROC) method. The models revealed Slope, barrenness, and Geology are the main causative factors of landslide activities in the study area. Finally, both Landslide susceptibility index maps were classified into five susceptibility classes. As the study area is very prone to landslide disasters so these susceptibility models will be helpful to delineate hazardous zones for the medication of future landslides disasters in the area as well as it can be used as a tool in the planning strategies by decision-makers in development projects in the area. Numéro de notice : A2022-589 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1870165 Date de publication en ligne : 11/02/2021 En ligne : https://doi.org/10.1080/10106049.2020.1870165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101363
in Geocarto international > vol 37 n° 13 [15/07/2022] . - pp 3941 - 3962[article]Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)
[article]
Titre : Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners Type de document : Article/Communication Auteurs : Artu Ellmann, Auteur ; Kaia Kütimets, Auteur ; Sander Varbla, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 363 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arpentage
[Termes IGN] carrière souterraine
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] données lidar
[Termes IGN] Estonie
[Termes IGN] géoréférencement
[Termes IGN] industrie minière
[Termes IGN] mine
[Termes IGN] modélisation 3D
[Termes IGN] schiste
[Termes IGN] semis de points
[Termes IGN] système de numérisation mobile
[Termes IGN] télémètre laser terrestreRésumé : (auteur) Applicability of SLAM (simultaneous localization and mapping) technology for mine surveys and subsequent 3D modelling of post-extracted surfaces is assessed. The resulting surface geometry is validated via terrestrial laser scanner (TLS) acquired reference data. Typical discrepancies remained within 2 and 5 cm in horizontal and vertical directions, respectively. Discrepancies between TLS, SLAM-enabled handheld scanner and conventional surveying results are small and fully satisfy the contemporary accuracy requirements, yet evidence that the conventional mine survey results are affected by the subjectivity of the surveyors. The SLAM-enabled laser scanning hence appears to be the most suitable method for underground mining surveys. Numéro de notice : A2022-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1944545 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1944545 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101093
in Survey review > vol 54 n° 385 (July 2022) . - pp 363 - 374[article]Can machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkA comparison of three multi-criteria decision-making models in mapping flood hazard areas of Northeast Penang, Malaysia / Rofiat Bunmi Mudashiru in Natural Hazards, vol 112 n° 3 (July 2022)PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkDetection of GNSS no-line of sight signals using LiDAR sensors for intelligent transportation systems / Tarek Hassan in Survey review, vol 54 n° 385 (July 2022)PermalinkDiscriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition / Tiantian Yan in Pattern recognition, vol 127 (July 2022)PermalinkEstimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkEvaluation of the GSRM2.1 and the NUVEL1-A values in Europe using SLR and VLBI based geodetic velocity fields / Mina Rahmani in Survey review, vol 54 n° 385 (July 2022)PermalinkExploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkA framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkFusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California / Huineng Yan in Journal of geodesy, vol 96 n° 7 (July 2022)Permalink