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Pedestrian fowl prediction in open public places using graph convolutional network / Menghang Liu in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
[article]
Titre : Pedestrian fowl prediction in open public places using graph convolutional network Type de document : Article/Communication Auteurs : Menghang Liu, Auteur ; Luning Li, Auteur ; Qiang Li, Auteur Année de publication : 2021 Article en page(s) : n° 455 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] espace public
[Termes IGN] flux
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] planification urbaine
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Shenzhen
[Termes IGN] variation temporelleRésumé : (auteur) Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time. Numéro de notice : A2021-550 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10070455 Date de publication en ligne : 02/07/2021 En ligne : https://doi.org/10.3390/ijgi10070455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98073
in ISPRS International journal of geo-information > vol 10 n° 7 (July 2021) . - n° 455[article]Temperature and humidity effects on CG-6 gravity observations / P. I. A. Weerasinghe in Journal of applied geodesy, vol 15 n° 3 (July 2021)
[article]
Titre : Temperature and humidity effects on CG-6 gravity observations Type de document : Article/Communication Auteurs : P. I. A. Weerasinghe, Auteur ; Herath Mudiyanselage Indika Prasanna, Auteur Année de publication : 2021 Article en page(s) : pp 225 - 231 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] corrélation linéaire
[Termes IGN] force de gravitation
[Termes IGN] gravimètre
[Termes IGN] humidité de l'air
[Termes IGN] instrument de mesure
[Termes IGN] Sri Lanka
[Termes IGN] température de l'air
[Termes IGN] thermomètreRésumé : (Auteur) The CG6 is an automated gravity meter that has a worldwide measurement range of over 8000 mGals and a reading resolution of 0.0001 mGal. Some factors that may influence the gravity readings are corrected by the instrument’s software. In this paper, the effects of the ambient temperature and humidity changes on the CG-6’s gravity measurements were investigated with the aim of reducing the uncertainty which remains in gravity measurements in microgal level, and giving recommendations for more accurate results. A controlled heating experiment was used to determine the impact of ambient temperature on gravity and 12 hours of continuous gravity observations were used to identify the impact of humidity on gravity measurements. It was observed that the sensor temperature and the gravity reading were highly correlated with the ambient temperature. The linear correlation with R2 > 0.94 and R2 > 0.90 were found for the corrected gravity reading and the residual sensor temperature variations respectively with the ambient temperature when heating. It was demonstrated that the calculated ambient temperature correction, −0.0011 mGal/oC, is more stable than the correction given by the instrument in terms of standard deviation though the impact of humidity on gravity reading was not clear. Numéro de notice : A2021-468 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0047 Date de publication en ligne : 28/04/2021 En ligne : https://doi.org/10.1515/jag-2020-0047 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98106
in Journal of applied geodesy > vol 15 n° 3 (July 2021) . - pp 225 - 231[article]Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
[article]
Titre : Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes Type de document : Article/Communication Auteurs : Qingying Yu, Auteur ; Yonglong Luo, Auteur ; Dongxia Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1346 - 1373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] direction
[Termes IGN] données spatiotemporelles
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] mobilité urbaine
[Termes IGN] Pékin (Chine)
[Termes IGN] plan de déplacement urbain
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] segmentation
[Termes IGN] trajet (mobilité)
[Termes IGN] vitesse de déplacementRésumé : (auteur) Residents’ trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability. Numéro de notice : A2021-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1901904 Date de publication en ligne : 15/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1901904 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97860
in International journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1346 - 1373[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021071 SL Revue Centre de documentation Revues en salle Disponible Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image Type de document : Article/Communication Auteurs : Genki Takahashi, Auteur ; H. Masuda, Auteur Année de publication : 2021 Article en page(s) : pp 67 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement d'arbres
[Termes IGN] apprentissage automatique
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction d'arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] route
[Termes IGN] semis de points
[Termes IGN] Tokyo (Japon)
[Termes IGN] zone urbaineRésumé : (auteur) Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees. Numéro de notice : A2021-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-67-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-67-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97956
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 67 - 74[article]Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])
[article]
Titre : Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1217 - 1235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification bayesienne
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification pixellaire
[Termes IGN] fond marin
[Termes IGN] Google Earth
[Termes IGN] habitat d'espèce
[Termes IGN] image Quickbird
[Termes IGN] Malaisie
[Termes IGN] précision infrapixellaire
[Termes IGN] récif corallienRésumé : (auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. Numéro de notice : A2021-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637466 Date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803
in Geocarto international > vol 36 n° 11 [15/06/2021] . - pp 1217 - 1235[article]Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications / Min Guo in Advances in space research, vol 67 n° 12 (15 June 2021)PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkDetection of suitable sites for rainwater harvesting planning in an arid region using geographic information system / Hadeel Qays Hashim in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkDynamic optimization models for displaying outdoor advertisement at the right time and place / Meng Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkFractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkGNSS-based statistical analysis of ionospheric anomalies during typhoon landings in Taiwan/Japan / Hai Peng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkImpact of different sampling rates on precise point positioning performance using online processing service / Serdar Erol in Geo-spatial Information Science, vol 24 n° 2 (June 2021)PermalinkMulti-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation / Shengfeng Gu in Journal of geodesy, vol 95 n° 6 (June 2021)PermalinkOn the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])Permalink