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PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 32 - 44 Titre : PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes Type de document : Article/Communication Auteurs : Weixiao Gao, Auteur ; Liangliang Nan, Auteur ; Bas Boom, Auteur ; Hugo Ledoux, Auteur Année de publication : 2023 Article en page(s) : pp 32 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de scène 3D [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] contour [Termes IGN] maillage [Termes IGN] Perceptron multicouche [Termes IGN] réseau neuronal de graphes [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique Résumé : (Auteur) We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic classification. The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes. In the subsequent classification step, we construct a graph that encodes the geometric and photometric features of the segments in its nodes and the multi-scale contextual features in its edges. The final semantic segmentation is obtained by classifying the segments using a graph convolutional network. Experiments and comparisons on two semantic urban mesh benchmarks demonstrate that our approach outperforms the state-of-the-art methods in terms of boundary quality, mean IoU (intersection over union), and generalization ability. We also introduce several new metrics for evaluating mesh over-segmentation methods dedicated to semantic segmentation, and our proposed over-segmentation approach outperforms state-of-the-art methods on all metrics. Our source code is available at https://github.com/WeixiaoGao/PSSNet. Numéro de notice : A2023-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.020 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102399 [article]

Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)  

Public [article]  inJournal of Marine Systems > vol 237 (January 2023) Titre : Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model Type de document : Article/Communication Auteurs : Zhenyang Du, Auteur ; Xuefeng Zhang, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] assimilation des données [Termes IGN] estimation statistique [Termes IGN] modèle océanographique [Termes IGN] océanographie spatiale [Termes IGN] température de surface de la mer [Termes IGN] teneur en chaleur de l'océan Résumé : (auteur) Using observational information to tune uncertain physical parameters in an ocean model via a robust data assimilation method has great potential to reduce model bias and improve the quality of sea temperature analysis and prediction. However, how observational information should be used to optimize geographic-dependent parameters through four-dimensional variational (4DVAR) data assimilation, which is one of the most prevailing assimilation methods, has not been fully studied. In this study, a two-step 4DVAR method is proposed to enhance parameter correction when the assimilation model contains biased geographic-dependent parameters within a biased model framework. Here, the biased parameters are set to an oceanic eddy diffusion coefficient, Kv, that plays an important role in modulating synoptic, seasonal and long-term changes in ocean heat content. Within a twin assimilation experiment framework, the temperature “observations” generated from sampling a “truth” model are assimilated into a biased model to investigate to what extent Kv can be estimated using the 4DVAR method when Kv remains geographic-dependent. The results show that the geographic-dependent Kv distribution can be optimally estimated to further improve the sea temperature analysis performance compared with the state estimation only method. In addition, the model prediction performance is also discussed with optimally estimated parameters under various conditions of noisy and/or sparse ocean observations. These results provide some insights for the prediction of ocean temperature mixing and stratification in a 3D primitive ocean numerical model using 4DVAR data assimilation. Numéro de notice : A2023-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jmarsys.2022.103824 En ligne : https://doi.org/10.1016/j.jmarsys.2022.103824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102716 [article]

A survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)  

Public Titre : A survey and benchmark of automatic surface reconstruction from point clouds Type de document : Article/Communication Auteurs : Raphaël Sulzer , Auteur ; Loïc Landrieu , Auteur ; Renaud Marlet, Auteur ; Bruno Vallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : BIOM / Vallet, Bruno Importance : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] benchmark spatial [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points Résumé : (auteur) We survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and missing data. Traditionally, different handcrafted priors of the input points or the output surface have been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting priors to different acquisition defects can be a tedious task. To this end, the deep learning community has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep surface reconstruction methods can learn priors directly from a training set of point clouds and corresponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the robustness of methods to defect-laden input and their capability to generate geometric and topologically accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and learning-based methods on the same grounds. We show that learning-based methods can generalize to unseen shape categories, but their training and test sets must share the same point cloud characteristics. We also provide the code and data to compete in our benchmark and to further stimulate the development of learning-based surface reconstruction: https://github.com/raphaelsulzer/dsr-benchmark. Numéro de notice : P2023-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2301.13656 Date de publication en ligne : 31/01/2023 En ligne : https://hal.science/hal-03968453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102847

Hyperspectral imagery and urban areas: results of the HYEP project / Christiane Weber in Revue Française de Photogrammétrie et de Télédétection, n° 224 (2022)  

Public [article]  inRevue Française de Photogrammétrie et de Télédétection > n° 224 (2022) . - pp 75 - 92 Titre : Hyperspectral imagery and urban areas: results of the HYEP project Type de document : Article/Communication Auteurs : Christiane Weber, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Sébastien Gadal, Auteur ; Rahim Aguejdad, Auteur ; Yannick Deville, Auteur ; Mauro Dalla Mura, Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; et al., Auteur Année de publication : 2022 Projets : HYEP / Weber, Christiane Article en page(s) : pp 75 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] détection d'objet [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] Lituanie [Termes IGN] milieu urbain [Termes IGN] panneau photovoltaïque [Termes IGN] surface imperméable [Termes IGN] Toulouse Résumé : (Auteur) The HYEP project (ANR HYEP 14-CE22-0016-01 Hyperspectral imagery for Environmental urban Planning - Mobility and Urban Systems Programme - 2014) confirmed the interest of a global approach to the urban environment by remote sensing and in particular by using hyperspectral imaging (HI). The interest of hyperspectral images lies in the range of information provided over wavelengths from 0.4 to 4 μm; they thus provide access to spectral quantities of interest and to chemical or biophysical parameters of the surface. HYEP's objective was to specify this and to propose a panel of methods and treatments taking into account the characteristics of other existing sensors in order to compare their performance. The developments carried out were applied and evaluated on hyperspectral airborne images acquired in Toulouse and Kaunas (Lithuania), also used to synthesize space systems: Sentinel-2, Hypxim/Biodiversity and Pleiades. Among the locks identified were those related to improving the spatial capabilities of the sensors and spatial scale changes, which were partially overcome by fusion and sharpening approaches, which proved to be successful. After a description of our hyperspectral data set acquired over Toulouse, an analysis is conducted on several existing and accessible spectral databases. Then, the chosen methods are presented. They include extraction, fusion and classification methods, which are then applied on our dataset synthetized at different spatial resolution to evaluate the benefits and the complementarity of hyperspectral imagery in comparison with other traditional sensors. Some specific applications are investigated of interest for urban planners: impervious soil map, vegetation species cartography and detection of solar panels. Finally, discussion and perspectives are presented. Numéro de notice : A2022-941 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : Hal Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2022.589 Date de publication en ligne : 22/12/2022 En ligne : https://dx.doi.org/10.52638/rfpt.2022.589 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102831 [article]

There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles / Richard J. Hewitt in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 12 (December 2022) Titre : There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles Type de document : Article/Communication Auteurs : Richard J. Hewitt, Auteur ; Majid Shadman Roodposhti, Auteur ; Brett A. Bryan, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] automate cellulaire [Termes IGN] étalonnage de modèle [Termes IGN] étalonnage des données [Termes IGN] incertitude des données [Termes IGN] utilisation du sol Résumé : (auteur) Cellular automata models are popular tools for exploring future land change pathways. But simulation modelling approaches often focus too narrowly on calibration against historic reference maps, limiting the diversity of possible outcomes. We argue that, contrary to what is commonly believed, there is no ‘best model’, and that model specification and calibration accuracy depend on the objective of the research. We propose a multi-model ensemble approach, in which a wide range of models and calibration rules sets are systematically tested against multiple metrics. We apply our approach to a case study in Spain. No single model performed well for all statistics, illustrating the danger of cherry-picking statistics for best performance. In our case study, accounting for historic land changes in model design was useful for simulating compact urban development, but limited the variability of simulation outcomes. The accessibility model driver improved urban pattern replication, while suitability without accessibility was useful for simulating low-density development encroaching on natural areas. Rather than abandoning calibrations that show low agreement with reference maps based on a small number of metrics we should seek to understand what each metric is telling us and use this information to enrich the diversity of simulated outcomes. Numéro de notice : A2022-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2098299 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2098299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101372 [article]

Attention mechanisms in computer vision: A survey / Meng-Hao Guo in Computational Visual Media, vol 8 n° 3 (September 2022)  

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Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

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Synergistic use of the SRAL/MWR and SLSTR sensors on board Sentinel-3 for the wet tropospheric correction retrieval / Pedro Aguiar in Remote sensing, vol 14 n° 13 (July-1 2022)  

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An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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Exploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)  

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High-performance adaptive texture streaming and rendering of large 3D cities / Alex Zhang in The Visual Computer, vol 38 n° 4 (April 2022)  

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Procedural urban forestry / Till Niese in ACM Transactions on Graphics, TOG, Vol 41 n° 2 (April 2022)  

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Hierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification / Yuntao Liu in The Visual Computer, vol 38 n° 3 (March 2022)  

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Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)  

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GCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)  

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Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network / Feng-Ping An in The Visual Computer, vol 38 n° 2 (February 2022)  

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A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)  

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Siamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)  

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Deep learning architectures for onboard satellite image analysis / Gaétan Bahl (2022)  

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Deep learning-based point cloud compression / Maurice Quach (2022)  

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the EUROSDR time benchmark for historical aerial images / E.M. Farella (2022)  

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Fast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)  

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Connecting images through sources: Exploring low-data, heterogeneous instance retrieval / Dimitri Gominski in Remote sensing, vol 13 n° 16 (August-2 2021)  

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Deep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)  

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Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)  

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