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Exploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)  

Public [article]  inSustainable Cities and Society > vol 79 (April 2022) . - n° 103656 Titre : Exploring the association between street built environment and street vitality using deep learning methods Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 103656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] apprentissage profond [Termes IGN] attractivité (aménagement) [Termes IGN] bati [Termes IGN] image Streetview [Termes IGN] Japon [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] piéton [Termes IGN] planification urbaine [Termes IGN] processus de hiérarchisation analytique [Termes IGN] régression linéaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] système d'information géographique [Termes IGN] urbanisme [Termes IGN] ville intelligente Résumé : (auteur) Street vitality has become an essential indicator for evaluating the attractiveness and potential of the sustainable development of urban blocks, and it can be reflected by the type and the frequency of people's pedestrian activities on the street. While it is recognized that street built environment features affect pedestrian behavior and street vitality, quantifying the impact of these characteristics remains inconclusive. This paper proposes an automated deep learning approach to quantitatively explore the association between the street built environment and street vitality. First, we established a deep learning model for street vitality classification for automatic evaluation of street vitality based on the volumes and activities of pedestrians in the street through multiple object tracking and scene classification. Then, we applied semantic segmentation to measure five selected vitality-related street built environment variables. Finally, a linear regression model was applied to evaluate the built environment variables’ significance and effects on street vitality. To verify our method's accuracy and applicability, we selected a commercial complex in Osaka as an illustrative example. The experimental results highlight that street width and transparency have significant positive effects on street vitality. Compared with traditional methods, our approach is feasible, reliable, transferable, and more efficient. Numéro de notice : A2022-266 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2021.103656 Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1016/j.scs.2021.103656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100271 [article]

LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 3 (March 2022) . - pp 476 - 495 Titre : LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland Type de document : Article/Communication Auteurs : Krystian Pyka, Auteur ; Radoslaw Piskorski, Auteur ; Aleksandra Jasińska, Auteur Année de publication : 2022 Article en page(s) : pp 476 - 495 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse visuelle [Termes IGN] canyon urbain [Termes IGN] Cracovie (Pologne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modèle numérique de surface [Termes IGN] paysage urbain [Termes IGN] piéton [Termes IGN] point de repère [Termes IGN] scène urbaine [Termes IGN] semis de points [Termes IGN] visibilité (optique) [Termes IGN] vision Résumé : (auteur) We propose a method for analysing landmark visibility from a pedestrian’s perspective. A case study is performed in Kraków, a city with many architectural monuments, where airborne LiDAR is used to model both buildings and urban greenery. The proposed method involves preliminary and detailed stages. The preliminary stage entails an inverse analysis (I–Vis) that departs from the typical visibility analysis to enable the use of landmarks as observers instead of targets. I–Vis results in paths with high landmark visibility. The detailed stage involves the use of a virtual panorama (V-Pan) to determine the visual exposure of the landmarks. Landmarks considered visible by I–Vis are generally consistent with landmarks identified by V-Pan. Discrepancies occur when trees appear in the near field-of-view. In addition, the accuracy of the skyline length and visible landmark surface area is evaluated against ground observations. The obtained results show that V-Pan can evaluate landmark visibility with an accuracy of approximately 75%. The key contributions of the work to visibility analysis of urban landmarks are in the inverse viewshed strategy and evaluation of the visual exposure parameters on LiDAR virtual panoramas. Numéro de notice : A2022-206 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2015600 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2015600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100021 [article]

Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)  

Public [article]  inPattern recognition > vol 121 (January 2022) . - n° 108252 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 [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)  

Public [article]  inComputers, Environment and Urban Systems > vol 91 (January 2022) . - n° 101725 Titre : Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model Type de document : Article/Communication Auteurs : Mingwei Liu, Auteur ; Tinggui Chen, Auteur ; Chiaki Matunaga, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101725 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agent (intelligence artificielle) [Termes IGN] bicyclette [Termes IGN] comportement [Termes IGN] cycliste [Termes IGN] direction [Termes IGN] interaction spatiale [Termes IGN] modèle de dispersion [Termes IGN] modèle orienté agent [Termes IGN] navigation pédestre [Termes IGN] piéton [Termes IGN] sécurité [Termes IGN] vitesse [Termes IGN] zone urbaine Résumé : (auteur) As the number of bicyclists in urban areas continues to increase, the need to realistically model the movement and interactions of bicyclists in mixed urban traffic is rapidly gaining importance. Therefore, this paper presents an agent space model (ASM) to elucidate the movements of bicyclists and pedestrians on shared roads. The ASM model, via simulation, particularly illustrates the dispersion phenomenon observed for non-motorized road users. The mutual interactions and diverse bicyclist and pedestrian properties were also incorporated into this model. The mutual interactions were realised through agent spaces of different sizes in conflict and overtaking behaviours for the following combinations: bicyclist-to-pedestrian, bicyclist-to-bicyclist, pedestrian-to-bicyclist, and pedestrian-to-pedestrian, which were obtained through experiments. The hypothesis test indicated that different agent spaces exist for different types of interactions. The experimental data were used to obtain several variables that describe the elements of road user agent spaces, including longitudinal and lateral distances and the dynamic relationship between the longitudinal distance and speed. The simulation results indicated that with an increase in the number of pedestrians, the maximum capacity decreased and the dispersion degree increased. The following psychological and physiological factors affect the degree of dispersion of bicyclists: travelling speed, reaction time, intensity, probability of selecting the head-on direction, and probability of selecting the right-hand direction. In addition, lane formation was observed in all simulations. The results also demonstrated that dedicated bicycle lanes will significantly reduce the dispersion degree. Moreover, the safety and efficiency effects of different forms of bicycle lanes were analysed from the perspective of the degree of dispersion. The simulation results can provide specific guidelines for understanding the causes of phenomena such as dispersion and lane formation, as well as for studying the traffic dynamics, effects of dedicated bicycle lanes, and macroscopic characteristics according to different bicyclist-pedestrian ratios. Numéro de notice : A2021-826 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101725 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98947 [article]

ComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6662 - 6673 Titre : ComNet: combinational neural network for object detection in UAV-borne thermal images Type de document : Article/Communication Auteurs : Minglei Li, Auteur ; Xingke Zhao, Auteur ; Jiasong Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6662 - 6673 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] image thermique [Termes IGN] piéton [Termes IGN] saillance [Termes IGN] véhicule Résumé : (auteur) We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks. Numéro de notice : A2021-632 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029945 Date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98288 [article]

3D object detection using lidar point clouds and 2D image object detection / Topi Miekkala (2021)  

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Modélisation numérique des paysages sonores urbains / Jonathan Siliézar (2021)

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Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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Generation of crowd arrival and destination locations/times in complex transit facilities / Brian Ricks in The Visual Computer, vol 36 n° 8 (August 2020)  

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An agent-based model of public space use / Kostas Cheliotis in Computers, Environment and Urban Systems, Vol 81 (May 2020)  

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Analyse spatio-temporelle des mobilités de randonneurs dans le PNR du Massif des Bauges / Colin Kerouanton (2020)  

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Pedestrian network information extraction based on VGI / Xuejing Xie in Geomatica [en ligne], vol 72 n° 3 (September 2018)  

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Élaboration d’un modèle de données spatiales à inspiration cognitive pour assister la navigation des piétons non voyants / Reda Yaagoubi in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)  

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The characteristics of asymmetric pedestrian behavior : A preliminary study using passive smartphone location data / Nick Malleson in Transactions in GIS, vol 22 n° 2 (April 2018)  

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