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Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)
[article]
Titre : Pedestrian trajectory prediction with convolutional neural networks Type de document : Article/Communication Auteurs : Simone Zamboni, Auteur ; Zekarias Tilahun Kefato, Auteur ; Sarunas Girdzijauskas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] distance euclidienne
[Termes IGN] filtre de Gauss
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] prévision à court terme
[Termes IGN] réseau social
[Termes IGN] trajet (mobilité)Résumé : (auteur) Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction. Numéro de notice : A2022-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108252 Date de publication en ligne : 13/08/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99615
in Pattern recognition > vol 121 (January 2022) . - n° 108252[article]A prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : A prediction model for surface deformation caused by underground mining based on spatio-temporal associations Type de document : Article/Communication Auteurs : Min Ren, Auteur ; Guanwen Cheng, Auteur ; Wancheng Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 94 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse des risques
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déformation de la croute terrestre
[Termes IGN] déformation de surface
[Termes IGN] mine de fer
[Termes IGN] modèle de simulation
[Termes IGN] règle d'associationMots-clés libres : spatio-temporal association rule mining (STARM) Résumé : (auteur) Accurate predictions of the surface deformation caused by underground mining are crucial for the safe development of underground resources. Although surface deformation has been predicted by artificial intelligence (AI) methods, most AI models are established based on the relationships between surface deformation and influential factors. The lack of consideration of the deformation state transition often leads to errors in the prediction results of catastrophic deformation by conventional AI methods. In this respect, this study introduces a surface deformation prediction model based on spatio-temporal association rule mining (STARM). Surface deformation is classified as excessive deformation zone (EDZ) and hysteretic deformation zone (HDZ), representing different surface deformation stage or state. The spatio-temporal association rules between the monitored EDZ and HDZ data are then mined. A surface deformation prediction model is established according to the spatio-temporal relationship between monitored EDZ and HDZ data. The proposed model is verified based on a practical case study of the Chengchao Iron Mine in China. The data collection of the influential factors is not requisite for the proposed model. It can achieve accurate prediction of the catastrophic deformation that was characterized by deformation state transition. Numéro de notice : A2022-035 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2021.2015460 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/19475705.2021.2015460 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99359
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 94 - 122[article]A rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : A rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model Type de document : Article/Communication Auteurs : Yuqian Dai, Auteur ; Xianfu Bai, Auteur ; Gaozhong Nie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 222 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] effondrement de terrain
[Termes IGN] modèle de régression
[Termes IGN] régression logistique
[Termes IGN] secours d'urgence
[Termes IGN] séisme
[Termes IGN] système d'information géographiqueRésumé : (auteur) The accuracy of rapid earthquake assessment and the emergency assessment system for earthquake-induced damages could be substantially enhanced if the casualties triggered by earthquake-induced geological disasters, such as landslides, are subjected to comprehensive scientific evaluation. However, no credible solution for this purpose has been formulated yet. This study suggests a three-step rapid assessment method designed for earthquake-induced landslide casualties based on the GIS and an associated logistic regression model, as follows: (1) Partition of the region to be evaluated as a 1 km × 1 km grid in the GIS, with assignment of a certain amount of population to each of the grid cells as its population attribute. (2) Calculation of the death rate for each grid cell based upon its earthquake-induced landslide susceptibility attribute using the logistic regression model. (3) The earthquake-induced landslide casualties are first determined for each of the kilometer grid cells, and then for the entire region under evaluation. The proposed method was implemented to test the assessment of earthquake-induced landslide casualties in three earthquake-stricken regions. The study reveals the feasibility of the extensibility and applicability of the proposed rapid assessment method for earthquake-induced landslide casualties, and its suitability for similar assessments and calculations of other regions. Numéro de notice : A2022-036 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2021.2017022 Date de publication en ligne : 29/12/2021 En ligne : https://doi.org/10.1080/19475705.2021.2017022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99367
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 222 - 248[article]Replication and the search for the laws in the geographic sciences / Peter Kedron in Annals of GIS, vol 28 n° 1 (January 2022)
[article]
Titre : Replication and the search for the laws in the geographic sciences Type de document : Article/Communication Auteurs : Peter Kedron, Auteur ; Joseph Holler, Auteur Année de publication : 2022 Article en page(s) : pp 45 - 56 Note générale : bibliographe Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] géographie
[Termes IGN] hétérogénéité spatiale
[Termes IGN] ligne de base
[Termes IGN] phénomène géographique
[Termes IGN] réplication
[Termes IGN] reproductibilité
[Termes IGN] varianceRésumé : (auteur) Replication is a means of assessing the credibility and generalizability of scientific results, whereby subsequent studies independently corroborate the findings of initial research. In the study of geographic phenomena, a distinct form of replicability is particularly important – whether a result obtained in one geographic context applies in another geographic context. However, the laws of geography suggest that it may be challenging to use replication to assess the credibility of findings across space and to identify new laws. Many geographic phenomena are spatially heterogeneous, which implies they exhibit uncontrolled variance across the surface of the earth and lack a characteristic mean. When a phenomenon is spatially heterogeneous, it may be difficult or impossible to establish baselines or rules for study-to-study comparisons. At the same time, geographic observations are typically spatially dependent, which makes it difficult to isolate the effects of interest for cross-study comparison. In this paper, we discuss how laws describing the spatial variation of phenomena may influence the use of replication in geographic research. Developing a set of shared principles for replication assessment based on fundamental laws of geography is a prerequisite for adapting replication standards to meet the needs of disciplinary subfields while maintaining a shared analytical foundation for convergent spatial research. Numéro de notice : A2022-188 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/19475683.2022.2027011 Date de publication en ligne : 17/02/2022 En ligne : https://doi.org/10.1080/19475683.2022.2027011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99916
in Annals of GIS > vol 28 n° 1 (January 2022) . - pp 45 - 56[article]Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Road traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods Type de document : Article/Communication Auteurs : Sulaiman Yunus, Auteur ; Ishaq A. Abdulkarim, Auteur Année de publication : 2022 Article en page(s) : pp 1535 - 1555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accident de la route
[Termes IGN] allocation
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] distribution spatiale
[Termes IGN] données localisées
[Termes IGN] équipement sanitaire
[Termes IGN] itinéraire
[Termes IGN] Nigéria
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau routier
[Termes IGN] secours d'urgenceRésumé : (auteur) Increased occurrence of road traffic crashes in Kano metropolis has resulted in a steady loss of lives, injuries, and increased people's risk exposure. This study looked into the emergency response to road traffic crashes in Kano, with a view to improving efficiency by developing linkages and synergy between Emergency Healthcare Facilities (EHCF), ambulances, and crash hotspots. The geographical location and attributes of the major EHCF, crash hotspots along highway intersections, and the two existent ambulances at the Kano State Fire Service (KSFS) and Federal Road Safety Corp head offices (FRSC) were obtained using GPS surveying. Road traffic network data (vector format) was digitized from satellite image, from which two major road classes (highways and minor roads) were identified, as well as their respective speed limits. The length and speed constraints were used to calculate time distances. Nearest Neighbor and Network (closest facility, shortest route, and location-allocation) analyses were carried out. Location-allocation analysis was to determine based on defined criteria the best locations to allocate EHCF or ambulance for optimum coverage. The results demonstrated that EHCF, ambulances, and crash places have different distribution patterns with almost no linkages. Closest ambulance facility analysis revealed the FRSC ambulance takes 9.41 minutes to arrive to crash spot 18 (Maiduguri Road, following NNPC) and 7.52 minutes to arrive at AKTH, the nearest EHCF. Comparatively, getting to Court road incident scene (spot 16) and IRPH as the closest EHCF takes about 3 times the time it takes to get to spot 18 and 4 times the time it takes to get to AKTH. This means that practically almost all victims in the city suffocate before reaching to the hospital. This signifies that, in cases of demand for CPR at the incident scene, there are higher likelihood of dying as it is expected to be provided within the first four minutes after the crash. Based on a maximum of 4 minutes impedance cutoff from all directions towards the occurrences areas, location-allocation analysis found eight new locations to maximize coverage and improve efficiency. It is concluded that current road traffic crash emergency response system has been determined to be ineffective. As a result, more ambulances should be strategically placed to improve emergency response times. Numéro de notice : A2022-884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2086829 Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1080/19475705.2022.2086829 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102209
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 1535 - 1555[article]Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model / Mingwei Liu in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkSpatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic / Nicolas Francos in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkTowards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkBuilding fuzzy areal geographical objects from point sets / Jifa Guo in Transactions in GIS, vol 25 n° 6 (December 2021)PermalinkA comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkIncorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment / Ali Azareh in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkModeling transit-assisted hurricane evacuation through socio-spatial networks / Yan Yang in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkA topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)PermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)PermalinkUsing textual volunteered geographic information to model nature-based activities: A case study from Aotearoa New Zealand / Ekaterina Egorova in Journal of Spatial Information Science, JoSIS, n° 23 (2021)Permalink