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A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878 Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2019 Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Bâti-3D [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection d'erreur [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] modèle d'erreur [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] taxinomie Résumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Numéro de notice : A2019-569 Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440 [article]

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Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)    

Public [article]  inRemote sensing > vol 11 n° 18 (September 2019) Titre : Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Arnaud Le Bris , Auteur ; Christiane Weber, Auteur Année de publication : 2019 Projets : HYEP / Weber, Christiane Note générale : bibliographie This paper constitutes a substantial extension of: https://doi.org/10.1109/IGARSS.2018.8518204 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] factorisation de matrice non-négative [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] panneau photovoltaïque [Termes descripteurs IGN] zone urbaine Résumé : (auteur) High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Numéro de notice : A2019-430 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11182164 date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.3390/rs11182164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93739 [article]

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A comparison of several spectral and spatial configuration for urban material classification / Arnaud Le Bris (2019)  

Public contenu dans JURSE 2019, Joint Urban Remote Sensing Event / T. Corpetti (2019)   Titre : A comparison of several spectral and spatial configuration for urban material classification Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur Congrès : JURSE 2019, Joint Urban Remote Sensing Event (22 - 24 mai 2019; Vannes, France) , Commanditaire Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Langues : Anglais (eng) Résumé : (auteur) Urban material maps are useful for several city modeling or monitoring applications and can be retrieved from remote sensing data. This study investigates the impact of spectral and spatial sensor configuration on urban material classification results, comparing several configurations corresponding to existing or envisaged airborne or space sensors. Images corresponding to such sensors were simulated out of an airborne hyperspectral acquisition. At the end, the relevance of an enhanced spectral configuration and especially providing bands from the SWIR domain was proven, as well as the need for a fine spatial resolution to retrieve urban objects. However, the (late) fusion of multispectral imagery at 2 m resolution with hyperspectral data at 8 m resolution was also proven to leads to good results. Numéro de notice : C2019-020 Thématique : IMAGERIE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans En ligne : http://jurse2019.org/poster-session-2/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92207

Multimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)  

Public Titre de série : Multimodal scene understanding: algorithms, applications and deep learning, ch. 11 Titre : Decision fusion of remote-sensing data for land cover classification Type de document : Chapitre/Contribution Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Walid Ouerghemmi , Auteur ; Cyril Wendl, Auteur ; Tristan Postadjian , Auteur ; Anne Puissant, Auteur ; Clément Mallet , Auteur Editeur : Londres, New York : Academic Press Année de publication : 2019 Importance : pp 341 - 382 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] fusion de données multisource [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Very high spatial resolution (VHR) multispectral imagery enables a fine delineation of objects and a possible use of texture information. Other sensors provide a lower spatial resolution but an enhanced spectral or temporal information, permitting one to consider richer land cover semantics. So as to benefit from the complementary characteristics of these multimodal sources, a decision late fusion scheme is proposed. This makes it possible to benefit from the full capacities of each sensor, while dealing with both semantic and spatial uncertainties. The different remote-sensing modalities are first classified independently. Separate class membership maps are calculated and then merged at the pixel level, using decision fusion rules. A final label map is obtained from a global regularization scheme in order to deal with spatial uncertainties while conserving the contrasts from the initial images. It relies on a probabilistic graphical model involving a fit-to-data term related to merged class membership measures and an image-based contrast-sensitive regularization term. Conflict between sources can also be integrated into this scheme. Two experimental cases are presented. In the first case one considers the fusion of VHR multispectral imagery with lower spatial resolution hyperspectral imagery for fine-grained land cover classification problem in dense urban areas. In the second case one uses SPOT 6/7 satellite imagery and Sentinel-2 time series to extract urban area footprints through a two-step process: classifications are first merged in order to detect building objects, from which a urban area prior probability is derived and eventually merged to Sentinel-2 classification output for urban footprint detection. Numéro de notice : H2019-002 Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-0-12-817358-9.00017-2 date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/B978-0-12-817358-9.00017-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93303

Scalable Evaluation of 3D City Models / Oussama Ennafii (2019)    

Public Titre : Scalable Evaluation of 3D City Models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Congrès : IGARSS 2019, IEEE International Geoscience And Remote Sensing Symposium (28 juillet - 2 août 2019; Pacifico Yokohama, Yokohama, Japon) , Commanditaire Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Importance : 4 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] modélisation 3D du bâti BIM [Termes descripteurs IGN] taxinomie Résumé : (Auteur) The generation of 3D building models from Very High Resolution geospatial data is now an automatized procedure. However, urban areas are very complex and practitioners still have to visually assess the correctness of these models and detect reconstruction errors. We proposed an approach for automatically evaluating the quality of 3D building models. It is cast as a supervised classification task based on a hierarchical taxonomy and multimodal handcrafted features (building geometry, optical images, height data). In this paper, we evaluate how the urban area composition impacts prediction transferability and scalability of our framework to unseen scenes. This allows to define minimal feature and training sets for a problem where no benchmark data has been released so far. Numéro de notice : C2019-006 Thématique : IMAGERIE/URBANISME Nature : Poster DOI : 10.1109/IGARSS.2019.8899337 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1109/IGARSS.2019.8899337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92592

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Sensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)    

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The necessary yet complex evaluation of 3D city models: a semantic approach / Oussama Ennafii (2019)    

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The ‘urban’ component of the French Land Data and Services Centre (Theia) / Anne Puissant (2019)  

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Urban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)  

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Classification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)

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Fusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)

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Vers une remise en géométrie automatique des prises de vue aériennes historiques photogrammétriques / Arnaud Le Bris in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)

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Toward automatic georeferencing of archival aerial photogrammetric surveys / Sébastien Giordano in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2 ([28/05/2018])  

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ANR HYEP ANR 14-CE22-0016-01 Hyperspectral imagery for Environmental urban Planning, Hyep, Programme Mobilité et systèmes urbains 2014 / Christiane Weber (2018)    

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Caméra SUSPECT (SUper-SPECTrale) du LOEMI : de la suspicion à la mise à l'épreuve / Olivier Martin (2018) 

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