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CDPS: Constrained DTW-Preserving Shapelets / Hussein El Amouri (2023)  

Public Titre : CDPS: Constrained DTW-Preserving Shapelets Type de document : Article/Communication Auteurs : Hussein El Amouri, Auteur ; Thomas Lampert, Auteur ; Pierre Gançarski, Auteur ; Clément Mallet , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2023 Collection : Lecture notes in Computer Science Sous-collection : Lecture Notes in Artificial Intelligence num. 13713 Projets : HIATUS / Giordano, Sébastien Conférence : ECML PKDD 2022, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 19/09/2022 23/09/2022 Grenoble France Proceedings Springer Projets : HERELLES / Gançarski, Pierre Importance : pp 21 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de données [Termes IGN] analyse de groupement [Termes IGN] classification [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] distance euclidienne [Termes IGN] jeu de données localisées [Termes IGN] série temporelle [Termes IGN] traitement de données localisées [Termes IGN] transformation Résumé : (auteur) The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, differences in sample duration, and/or compression and dilation of a signal means that Euclidean distance is unsuitable in many cases. As such, several similarity measures specific to time-series have been proposed, Dynamic Time Warping (DTW) being the most popular. Nevertheless, DTW does not respect the axioms of a metric and therefore Learning DTW-Preserving Shapelets (LDPS) have been developed to regain these properties by using the concept of shapelet transform. LDPS learns an unsupervised representation that models DTW distances using Euclidean distance in shapelet space. This article proposes constrained DTW-preserving shapelets (CDPS), in which a limited amount of user knowledge is available in the form of must link and cannot link constraints, to guide the representation such that it better captures the user’s interpretation of the data rather than the algorithm’s bias. Subsequently, any unconstrained algorithm can be applied, e.g. K-means clustering, k-NN classification, etc, to obtain a result that fulfils the constraints (without explicit knowledge of them). Furthermore, this representation is generalisable to out-of-sample data, overcoming the limitations of standard transductive constrained-clustering algorithms. CLDPS is shown to outperform the state-of-the-art constrained-clustering algorithms on multiple time-series datasets. Numéro de notice : C2022-052 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-031-26387-3_2 Date de publication en ligne : 17/03/2023 En ligne : https://doi.org/10.1007/978-3-031-26387-3_2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103157

Improving image segmentation with boundary patch refinement / Xiaolin Hu in International journal of computer vision, vol 130 n° 11 (November 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 11 (November 2022) . - pp 2571 - 2589 Titre : Improving image segmentation with boundary patch refinement Type de document : Article/Communication Auteurs : Xiaolin Hu, Auteur ; Chufeng Tang, Auteur ; Hang Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2571 - 2589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] détection de contours [Termes IGN] distance euclidienne [Termes IGN] masque [Termes IGN] segmentation d'image [Termes IGN] segmentation fondée sur les contours [Termes IGN] segmentation sémantique Résumé : (auteur) Tremendous efforts have been made on image segmentation but the mask quality is still not satisfactory. The boundaries of predicted masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework, termed BPR, to improve the boundary quality of the prediction of any image segmentation model. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted boundaries. The refinement is accomplished by a boundary patch refinement network at the higher resolution. The trained BPR model can be easily transferred to refine the results of other models as well. Extensive experiments show that the proposed BPR framework yields significant improvements on the semantic, instance, and panoptic segmentation tasks over a variety of baselines on the Cityscapes dataset. Numéro de notice : A2022-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01662-0 Date de publication en ligne : 12/08/2022 En ligne : https://doi.org/10.1007/s11263-022-01662-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101719 [article]

GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422 Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] apprentissage automatique [Termes IGN] bâtiment [Termes IGN] distance de Fréchet [Termes IGN] empreinte [Termes IGN] morphologie urbaine [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] texture d'image Résumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

An exact statistical method for analyzing co-location on a street network and its computational implementation / Wataru Morioka in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 773 - 798 Titre : An exact statistical method for analyzing co-location on a street network and its computational implementation Type de document : Article/Communication Auteurs : Wataru Morioka, Auteur ; Mei-Po Kwan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 798 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] co-positionnement [Termes IGN] distance euclidienne [Termes IGN] fonction K de Ripley [Termes IGN] implémentation (informatique) [Termes IGN] méthode statistique [Termes IGN] réseau routier [Termes IGN] Tokyo (Japon) [Termes IGN] zone tampon Résumé : (auteur) In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. Numéro de notice : A2022-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1976409 Date de publication en ligne : 16/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1976409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100230 [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]

Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners / Berit Schmitz in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)  

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Exploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)  

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Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)  

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Forest fragmentation assessment using field-based sampling data from forest inventories / Habib Ramezani in Scandinavian journal of forest research, vol 36 n° 4 ([01/05/2021])  

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Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Context-aware similarity of GPS trajectories / Radu Mariescu-Istodor in Journal of location-based services, vol 14 n° 4 ([01/11/2020])  

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Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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Dynamic floating stations model for emergency medical services with a consideration of traffic data / Chih-Hong Sun in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

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