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A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement d'occupation du sol [Termes IGN] classe d'objets [Termes IGN] classification par forêts aléatoires [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] milieu urbain [Termes IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-5 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-5 (August 2020) . - pp 109 - 116 Titre : SemCity Toulouse: a benchmark for building instance segmentation in satellite images Type de document : Article/Communication Auteurs : Ribana Roscher, Auteur ; Michele Volpi, Auteur ; Clément Mallet , Auteur ; Lukas Drees, Auteur ; Jan Dirk Wegner, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 5, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 5 Article en page(s) : pp 109 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage automatique [Termes IGN] bati [Termes IGN] données d'apprentissage [Termes IGN] instance [Termes IGN] Toulouse [Termes IGN] zone urbaine dense Résumé : (auteur) In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. Numéro de notice : A2020-503 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-5-2020-109-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95639 [article]

Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data / Citlalli Gamez Serna (2019)  

Public Titre : Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data Type de document : Thèse/HDR Auteurs : Citlalli Gamez Serna, Auteur ; Yassine Ruichek, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2019 Importance : 178 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université Bourgogne Franche-Comté préparée à l'Université de Technologie de Belfort-Montbéliard, Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] instance [Termes IGN] milieu urbain [Termes IGN] récepteur GPS [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] signalisation routière [Termes IGN] système de transport intelligent [Termes IGN] trajectoire [Termes IGN] véhicule sans pilote [Termes IGN] vision [Termes IGN] vision par ordinateur [Termes IGN] vision stéréoscopique [Termes IGN] vitesse Résumé : (auteur) This PhD thesis focuses on developing a path tracking approach based on visual perception and localization in urban environments. The proposed approach comprises two systems. The first one concerns environment perception. This task is carried out using deep learning techniques to automatically extract 2D visual features and use them to learn in order to distinguish the different objects in the driving scenarios. Three deep learning techniques are adopted: semantic segmentation to assign each image pixel to a class, instance segmentation to identify separated instances of the same class and, image classification to further recognize the specific labels of the instances. Here our system segments 15 object classes and performs traffic sign recognition. The second system refers to path tracking. In order to follow a path, the equipped vehicle first travels and records the route with a stereo vision system and a GPS receiver (learning step). The proposed system analyses off-line the GPS path and identifies exactly the locations of dangerous (sharp) curves and speed limits. Later after the vehicle is able to localize itself, the vehicle control module together with our speed negotiation algorithm, takes into account the information extracted and computes the ideal speed to execute. Through experimental results of both systems, we prove that, the first one is capable to detect and recognize precisely objects of interest in urban scenarios, while the path tracking one reduces significantly the lateral errors between the learned and traveled path. We argue that the fusion of both systems will ameliorate the tracking approach for preventing accidents or implementing autonomous driving. Note de contenu : I- Context and problems 1- Introduction II- Contribution 2- Proposed datasets 3- Traffic sign classification 4- Visual perception system for urban environments 5- Dynamic speed adaptation system for path tracking based on curvature information and speed limits III- Conclusions and future works 6- Conclusions and future works Numéro de notice : 25967 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : UBFC : 2019 Organisme de stage : CIAD Dijon nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02160966/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96587

Label propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances / Xiaomeng Wu in International journal of computer vision, vol 126 n° 7 (July 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 7 (July 2018) . - pp 689 - 713 Titre : Label propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances Type de document : Article/Communication Auteurs : Xiaomeng Wu, Auteur ; Kaoru Hiramatsu, Auteur ; Kunio Kashino, Auteur Année de publication : 2018 Article en page(s) : pp 689 - 713 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] étiquette [Termes IGN] instance [Termes IGN] méthode robuste [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] relation spatiale Résumé : (Auteur) Spatial verification methods permit geometrically stable image matching, but still involve a difficult trade-off between robustness as regards incorrect rejection of true correspondences and discriminative power in terms of mismatches. To address this issue, we ask whether an ensemble of weak geometric constraints that correlates with visual similarity only slightly better than a bag-of-visual-words model performs better than a single strong constraint. We consider a family of spatial verification methods and decompose them into fundamental constraints imposed on pairs of feature correspondences. Encompassing such constraints leads us to propose a new method, which takes the best of existing techniques and functions as a unified Ensemble of pAirwise GEometric Relations (EAGER), in terms of both spatial contexts and between-image transformations. We also introduce a novel and robust reranking method, in which the object instances localized by EAGER in high-ranked database images are reissued as new queries. EAGER is extended to develop a smoothness constraint where the similarity between the optimized ranking scores of two instances should be maximally consistent with their geometrically constrained similarity. Reranking is newly formulated as two label propagation problems: one is to assess the confidence of new queries and the other to aggregate new independently executed retrievals. Extensive experiments conducted on four datasets show that EAGER and our reranking method outperform most of their state-of-the-art counterparts, especially when large-scale visual vocabularies are used. Numéro de notice : A2018-411 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1063-9 date de publication en ligne : 31/01/2018 En ligne : https://doi.org/10.1007/s11263-018-1063-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90886 [article]

Labelling hierarchy for street maps using centrality measures / Wasim Shoman in Cartographic journal (the), vol 55 n° 1 (February 2018)  

Public [article]  inCartographic journal (the) > vol 55 n° 1 (February 2018) . - pp 68 - 84 Titre : Labelling hierarchy for street maps using centrality measures Type de document : Article/Communication Auteurs : Wasim Shoman, Auteur ; Fatih Gülgen, Auteur Année de publication : 2018 Article en page(s) : pp 68 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] axe médian [Termes IGN] Boston (Massachusetts) [Termes IGN] classe d'objets [Termes IGN] écriture cartographique [Termes IGN] généralisation cartographique automatisée [Termes IGN] lisibilité perceptive [Termes IGN] placement automatique des écritures [Termes IGN] processus d'analyse hiérarchisée floue [Termes IGN] représentation multiple [Termes IGN] réseau routier [Termes IGN] route [Termes IGN] Styled Layer Descriptor [Vedettes matières IGN] Généralisation Résumé : (Auteur) This paper proposes a compound hierarchy to be used in cartographic labelling of streets. The main purposes of this hierarchy are to ease navigation of street maps and to provide more understandability and legibility of street features for map users. The hierarchy uses centrality measures along with functional classes of the streets to order features according to their spatial importance in each used zoom level. The calculation of a proper radius, to be employed in the calculations of centrality measures, is based on extracting proper field of view in the map user’s focus status. The used measures are integrated using fuzzy-analytic hierarchy process, yielding proper coefficients to create the hierarchy. The hierarchy is applied for the thinning process and later for using the styled layer descriptor to label street features. Quality constrains are implemented to make the street labels more understandable and readable. Numéro de notice : A2018-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00087041.2017.1323151 date de publication en ligne : 19/05/2017 En ligne : https://doi.org/10.1080/00087041.2017.1323151 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90325 [article]

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A hydrological sensor web ontology based on the SSN ontology: A case study for a flood / Chao Wang in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)  

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Geometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1 ([30/05/2017])  

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Integrating social network data into GISystems / Clio Andris in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

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A survey on web data linking / Manel Achichi in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 5 - 6 (septembre - décembre 2016)  

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Carte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)  

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An integrated heterogeneous web service retrieval via combination of instance- and metadata-based schema matching method / Jie He in Geo-spatial Information Science, vol 18 n° 2 (August 2015)  

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Modalités de représentation en 3D de données issues du SIG2D, pour la conception et la simulation / Olivier Jest (2015) 

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Object-Oriented Analysis, Design and Implementation / Brahma Dathan (2015)  

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A geometry and texture coupled flexible generalization of urban building models / M. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)

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Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation / Patrick Taillandier in Applied soft computing, vol 11 n° 2 (March 2011)  

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