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Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps / Izabela Karsznia in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 9 (September 2021) . - n° 576 Titre : Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps Type de document : Article/Communication Auteurs : Izabela Karsznia, Auteur ; Izabela Golebiowska, Auteur ; Jolanta Korycka-Skorupa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] carte choroplèthe [Termes IGN] carte thématique [Termes IGN] échelle cartographique [Termes IGN] enquête [Termes IGN] généralisation [Termes IGN] lecture de carte [Termes IGN] reconnaissance de formes [Termes IGN] répartition géographique [Termes IGN] représentation cartographique [Termes IGN] utilisateur [Termes IGN] visualisation cartographique Résumé : (auteur) Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations. Numéro de notice : A2021-686 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10090576 date de publication en ligne : 25/08/2021 En ligne : https://doi.org/10.3390/ijgi10090576 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98412 [article]

A typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, Vol 36 n° 15 ([10/08/2021])  

Public [article]  inGeocarto international > Vol 36 n° 15 [10/08/2021] . - pp 1732 - 1751 Titre : A typification method for linear building groups based on stroke simplification Type de document : Article/Communication Auteurs : Xiao Wang, Auteur ; Dirk Burghardt, Auteur Année de publication : 2021 Article en page(s) : pp 1732 - 1751 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] alignement [Termes IGN] bâtiment [Termes IGN] généralisation du bâti [Termes IGN] noeud [Termes IGN] objet géographique linéaire [Termes IGN] reconnaissance de formes [Termes IGN] simplification de contour [Termes IGN] triangulation de Delaunay [Vedettes matières IGN] Généralisation Résumé : (auteur) Linear building groups are common patterns and important local structures in large scale maps, which should be carefully generalized. This paper uses the idea of line simplification to typify linear building groups. Firstly, based on the stroke idea, the linear building groups are detected that each building group is related by only one stroke; the collinear and curvilinear patterns are distinguished by calculating the overlap rate between the defined auxiliary polygon and its oriented bounding box. Secondly, the stroke is simplified by removing one node in each iterative step; and the remained nodes are reallocated to the new positions, which serves as the centroids location of the newly typified buildings. Third, the representation (size, shape, elongation, and orientation) of the newly typified buildings are calculated by the geometry information of their corresponding parent buildings. The typification method can be carried out as a progressive process, which iterates over the three steps to derive continuous typification results. The method is tested on two building datasets, and the experimental results demonstrate that the proposed method can achieve good performance by well preserving the original linear patterns in the generalized building groups. Numéro de notice : A2021-569 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2019.1669725 date de publication en ligne : 26/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1669725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98184 [article]

The point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1374 - 1391 Titre : The point-descriptor-precedence representation for point configurations and movements Type de document : Article/Communication Auteurs : Amna Qayyum, Auteur ; Bernard De Baets, Auteur ; Muhammad Sulman Baig, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1374 - 1391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] courbe [Termes IGN] détection d'événement [Termes IGN] données spatiotemporelles [Termes IGN] mesurage de distances [Termes IGN] objet mobile [Termes IGN] reconnaissance de formes [Termes IGN] relation topologique [Termes IGN] trafic routier [Termes IGN] véhicule automobile Résumé : (auteur) In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations () acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis. Numéro de notice : A2021-453 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1864378 date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1864378 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97882 [article]

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Trajectory and image-based detection and identification of UAV / Yicheng Liu in The Visual Computer, vol 37 n° 7 (July 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 7 (July 2021) Titre : Trajectory and image-based detection and identification of UAV Type de document : Article/Communication Auteurs : Yicheng Liu, Auteur ; Luchuan Liao, Auteur ; Hao Wu, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] Aves [Termes IGN] caméra de surveillance PTZ [Termes IGN] cible mobile [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] drone [Termes IGN] forme caractéristique [Termes IGN] interférence [Termes IGN] reconnaissance de formes [Termes IGN] trajectoire Résumé : (auteur) Much more attentions have been attracted to the inspection and prevention of unmanned aerial vehicle (UAV) in the wake of increasing high frequency of security accident. Many factors like the interferences and the small fuselage of UAV pose challenges to the timely detection of the UAV. In our work, we present a system that is capable of detecting, recognizing, and tracking an UAV using single camera automatically. For our method, a single pan–tilt–zoom (PTZ) camera detects flying objects and gets their trajectories; then, the trajectory identified as a UAV guides the camera and PTZ to capture the detailed region image of the target. Therefore, the images can be classified into the UAV and interference classes (such as birds) by the convolution neural network classifier trained with our image dataset. For the target recognized as a UAV with the double verification, the radio jammer emits the interferential radio to disturb its control radio and GPS. This system could be applied in some complex environment where many birds and UAV appear simultaneously. Numéro de notice : A2021-541 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01937-y date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01937-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98020 [article]

Multiple convolutional features in Siamese networks for object tracking / Zhenxi Li in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 3 (May 2021) . - n° 59 Titre : Multiple convolutional features in Siamese networks for object tracking Type de document : Article/Communication Auteurs : Zhenxi Li, Auteur ; Guillaume-Alexandre Bilodeau, Auteur ; Wassim Bouachir, Auteur Année de publication : 2021 Article en page(s) : n° 59 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] poursuite de cible [Termes IGN] reconnaissance d'objets [Termes IGN] réseau neuronal siamois Résumé : (auteur) Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks. The source code and trained models are available at https://github.com/zhenxili96/MFST. Numéro de notice : A2021-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01185-7 date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1007/s00138-021-01185-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97903 [article]

Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)  

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Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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Building change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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A framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

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