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Généralisation

alerte-généralisation

Deriving map images of generalised mountain roads with generative adversarial networks / Azelle Courtial in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)

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[article]
inInternational journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 499 - 528

Titre :	Deriving map images of generalised mountain roads with generative adversarial networks
Type de document :	Article/Communication
Auteurs :	Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur
Année de publication :	2023
Article en page(s) :	pp 499 - 528
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Termes IGN] analyse comparative [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage non-dirigé [Termes IGN] carte routière [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] généralisation cartographique automatisée [Termes IGN] montagne [Termes IGN] réseau antagoniste génératif [Vedettes matières IGN] Généralisation
Résumé :	(auteur) Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals.
Numéro de notice :	A2023-073
Affiliation des auteurs :	UGE-LASTIG+Ext (2020- )
Thématique :	GEOMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/13658816.2022.2123488
Date de publication en ligne :	20/10/2022
En ligne :	https://doi.org/10.1080/13658816.2022.2123488
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101901

[article]

Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)

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[article]
inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211

Titre :	Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence
Type de document :	Article/Communication
Auteurs :	Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur
Année de publication :	2023
Article en page(s) :	pp 199 - 211
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte ancienne [Termes IGN] cartographie historique [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données anciennes [Termes IGN] matrice de co-occurrence [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] vision par ordinateur
Résumé :	(auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence.
Numéro de notice :	A2023-146
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2023.01.021
Date de publication en ligne :	14/02/2023
En ligne :	https://doi.org/10.1016/j.isprsjprs.2023.01.021
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804

[article]

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

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[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]

Where am I now? modelling disorientation in pan-scalar maps / Guillaume Touya in ISPRS International journal of geo-information, vol 12 n° 2 (February 2023)

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[article]
inISPRS International journal of geo-information > vol 12 n° 2 (February 2023) . - n° 62

Titre :	Where am I now? modelling disorientation in pan-scalar maps
Type de document :	Article/Communication
Auteurs :	Guillaume Touya , Auteur ; Maieul Gruget , Auteur ; Ian Muehlenhaus, Auteur
Année de publication :	2023
Article en page(s) :	n° 62
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Termes IGN] cognition [Termes IGN] données multiéchelles [Termes IGN] échelle cartographique [Termes IGN] interaction homme-machine [Termes IGN] lecture de carte [Termes IGN] représentation mentale [Termes IGN] représentation mentale spatiale [Termes IGN] représentation multiple [Vedettes matières IGN] Généralisation
Résumé :	(auteur) Disorientation is a common feeling for all users of zoomable multi-scale maps, even for those with good orientation and spatial skills. We make the assumption that this problem is mainly due to the desert fog effect, documented in human–computer interaction within multi-scale zoomable environments. Starting with a collection of reported experiences of disorientation, this paper explores this notion from the spatial cognition, philosophical and human–computer interaction perspectives and proposes a model of disorientation in the exploration of multi-scale maps. We argue that disorientation is a problem of reconciliation between the current map view and the mental map of the user, where landmarks visible on the map or memorised in the mental map play a key role. The causes for failed reconciliation are discussed and illustrated by our collected experiences of disorientation.
Numéro de notice :	A2023-130
Affiliation des auteurs :	UGE-LASTIG+Ext (2020- )
Thématique :	GEOMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.3390/ijgi12020062
En ligne :	https://doi.org/10.3390/ijgi12020062
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=102585

[article]

AnchorWhat : Décompositions de cartes pan-scalaires / Maieul Gruget (2023)

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Titre :	AnchorWhat : Décompositions de cartes pan-scalaires
Type de document :	Article/Communication
Auteurs :	Maieul Gruget , Auteur ; Guillaume Touya , Auteur
Editeur :	Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-)
Année de publication :	2023
Conférence :	Journée Recherche de l'UGE-IGN-ENSG 2023, 32e journée de la recherche, Jumeaux numérique et anthropocène : données de simulation pour aider à la prise de décision 30/03/2023 Champs-sur-Marne France programme
Langues :	Français (fre)
Descripteur :	[Vedettes matières IGN] Généralisation
Résumé :	(auteur) Poster de vulgarisation scientifique à la journée de la recherche IGN 2023. Inspiré de la théorie des ancres de Couclelis et collègues, ce poster présente une méthodologie d'analyse de présence et persistance d'éléments cartographiques à travers différentes explorations cartographiques.
Numéro de notice :	C2023-002
Affiliation des auteurs :	UGE-LASTIG (2020- )
Thématique :	GEOMATIQUE
Nature :	Poster
nature-HAL :	Poster-sans-CL
DOI :	sans
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=103185

Documents numériques

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Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)

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[article]
inRemote sensing of environment > vol 284 (January 2023) . - n° 113362

Titre :	Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar
Type de document :	Article/Communication
Auteurs :	Andrew W. Whelan, Auteur ; Jeffery B. Cannon, Auteur ; Seth W. Bigelow, Auteur ; et al., Auteur
Année de publication :	2023
Article en page(s) :	n° 113362
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] diagnostic foliaire [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Géorgie (Etats-Unis) [Termes IGN] modélisation de la forêt [Termes IGN] Pinus palustris [Termes IGN] structure d'un peuplement forestier [Termes IGN] surface forestière [Termes IGN] volume en bois [Termes IGN] voxel
Résumé :	(auteur) Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types.
Numéro de notice :	A2023-016
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE
Nature :	Article
DOI :	10.1016/j.rse.2022.113362
Date de publication en ligne :	23/11/2022
En ligne :	https://doi.org/10.1016/j.rse.2022.113362
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=102150

[article]

Incorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (2023)

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[article]
inInternational journal of cartography > vol inconnu (2023)

Titre :	Incorporating ideas of structure and meaning in interactive multi scale mapping environments
Type de document :	Article/Communication
Auteurs :	Guillaume Touya , Auteur ; Quentin Potié , Auteur ; William A Mackaness, Auteur
Année de publication :	2023
Projets :	LostInZoom / Touya, Guillaume
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Termes IGN] apprentissage automatique [Termes IGN] état de l'art [Termes IGN] généralisation cartographique automatisée [Termes IGN] lisibilité perceptive [Termes IGN] reconnaissance de formes [Termes IGN] web mapping [Vedettes matières IGN] Généralisation
Résumé :	(auteur) Web based, slippy, scalable maps are common place. Interacting with such digital maps at varying levels of detail is key to interpretation, and exploration of different geographies. The process of abstraction remains key to the immediate and successful interpretation of their many structures and geographical associations found at any given scale. Meaning is derived from such recognisable structures and map generalisation plays a critical role in communicating an entity's most characteristic and salient qualities. But what are these structures? How (and why) do they change over scale? Why are such questions relevant to automated mapping? In this paper we reflect on the value of perceptual studies and reconsider the context in which map generalisation now takes place. We review developments in pattern recognition techniques and the role played by machine learning techniques in identifying high level structures in abstracted maps. The benefits of their application include derivation of ontological descriptions of landscape, identification and preservation of salient landmarks across scales. We argue that a 'structuralist based approach' provides a more meaningful basis for measuring success and achieving more meaningful outputs. Ultimately the ambition is greater levels of automation in map generalisation, particularly in the context of web based solutions.
Numéro de notice :	A2023-099
Affiliation des auteurs :	UGE-LASTIG+Ext (2020- )
Autre URL associée :	vers HAL
Thématique :	GEOMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/23729333.2023.2215960
Date de publication en ligne :	01/06/2023
En ligne :	https://doi.org/10.1080/23729333.2023.2215960
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=103273

[article]

Missing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps / Maieul Gruget in International journal of cartography, vol inconnu (2023)

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[article]
inInternational journal of cartography > vol inconnu (2023)

Titre :	Missing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps
Type de document :	Article/Communication
Auteurs :	Maieul Gruget , Auteur ; Guillaume Touya , Auteur ; Ian Muehlenhaus, Auteur
Année de publication :	2023
Projets :	LostInZoom / Touya, Guillaume
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Termes IGN] carte interactive [Termes IGN] conception cartographique [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Généralisation
Résumé :	(auteur) Most of the maps used today are what we call pan-scalar maps, i.e. interactive zoomable applications comprised of numerous maps of a particular area at different zoom levels (i.e. scales). We argue that such maps require a pan-scalar map design, which may differ significantly from established map design axioms and standards. This review is twofold. First, it reviews current practices in pan-scalar map design. Second, it summarizes and synthesizes literature about pan-scalar map design, as well as human-computer interaction (HCI) best practices for pan-scalar maps. The review of practices is based on a ScaleMaster analysis of the design of three popular pan-scalar maps: Google Maps, OpenStreetMap, and France's IGN Classic. Discussion centers on both stellar and subpar contemporary pan-scalar map design practices to help guide future practical pan-scalar designs and research on pan-scalar maps broadly.
Numéro de notice :	A2023-077
Affiliation des auteurs :	UGE-LASTIG+Ext (2020- )
Thématique :	GEOMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/23729333.2022.2153467
Date de publication en ligne :	10/01/2023
En ligne :	https://doi.org/10.1080/23729333.2022.2153467
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=102515

[article]

Where do people look at during multi-scale map tasks? / Laura Wenclik (2023)

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Titre :	Where do people look at during multi-scale map tasks?
Type de document :	Article/Communication
Auteurs :	Laura Wenclik, Auteur ; Guillaume Touya , Auteur
Editeur :	Göttingen : Copernicus publications
Année de publication :	2023
Collection :	AGILE GIScience Series num. vol 4
Projets :	LostInZoom / Touya, Guillaume
Conférence :	AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings
Importance :	n° 51; 7 p.
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Termes IGN] carte interactive [Termes IGN] oculométrie [Termes IGN] point de repère [Termes IGN] translation [Termes IGN] visualisation multiéchelle [Termes IGN] zoom [Vedettes matières IGN] Géovisualisation
Résumé :	(Auteur) In order to design better pan-scalar maps, i.e. interactive, zoomable, multi-scale maps, we need to understand how they are perceived, understood, processed, manipulated by the users. This paper reports an experiment that uses an eye-tracker to analyse the gaze behaviour of users zooming and panning into a pan-scalar map. The gaze data from the experiment shows how people look at landmarks to locate the new map view after a zoom. We also identified different types of behaviours during a zoom when people stare at the mouse cursor, or during a pan where the gaze follows a landmark while the map translates.
Numéro de notice :	C2023-009
Affiliation des auteurs :	UGE-LASTIG (2020- )
Thématique :	GEOMATIQUE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/agile-giss-4-51-2023
Date de publication en ligne :	06/06/2023
En ligne :	https://doi.org/10.5194/agile-giss-4-51-2023
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=103303

Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])

Public

[article]
inGeocarto international > vol 37 n° 27 [20/12/2022] . - pp

Titre :	Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models
Type de document :	Article/Communication
Auteurs :	Saadia Sultan Wahlaa, Auteur ; Jamil Hasan Kazmi, Auteur ; Alireza Sharifi, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	pp
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] changement climatique [Termes IGN] classification par arbre de décision [Termes IGN] évapotranspiration [Termes IGN] Indice de précipitations antérieures [Termes IGN] modèle de simulation [Termes IGN] Pakistan [Termes IGN] prévision météorologique [Termes IGN] sécheresse
Résumé :	(auteur) Droughts may inflict significant damage to agricultural and water supplies, resulting in substantial financial losses as well as the death of people and livestock. This study intends to anticipate droughts by studying the changes of an acceptable index using appropriate climatic factors. This study was divided into three phases, first being the determination of the Standardized Precipitation Evapotranspiration (SPEI) index for the Cholistan, Punjab, Pakistan area based on a dataset spanning 1980 to 2020. The indices are calculated at different monthly intervals which could to predict short-term periods for the Cholistan in Pakistan, we selected two distinctive time periods of one month (SPEI–1) and three months (SPEI–3). The second phase involved dividing the data into three sample sizes, which were used for training data from 1980 to 2010, testing data from 2011 to 2015 and validation data from 2016 to 2020. The utilization of the random forest (RF) algorithm to train and evaluate the data using a variety of climate variables e.g. potential evapotranspiration, rainfall, vapor pressure cloud cover, and mean, minimum and maximum, temperature. The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Based on these considerations, statistical measures, such as the Coefficient of Determination (R2) and the Root Mean Square Error (RMSE) approach, were used to evaluate the performance of the test group throughout the testing period. The model's performance revealed the satisfactory results with R2 values of 0.80 and 0.78, for SPEI–1 and SPEI–3 situations, respectively. Following the data analysis, it was discovered that the validation period had a receiving operating curve and area under the Curve (ROC-AUC) of 0.87 for the SPEI–1 case and 0.85 for the SPEI–3 case. In this context, the results indicate that the SPEI may be useful as a prediction tool for drought prediction and the performances the RF model was suitable for both timescales. However, a more rigorous analysis with a larger dataset or a combination of datasets from different areas might be more beneficial for generalization over more extended time periods provide additional insights.
Numéro de notice :	A2022-934
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2022.2093411
Date de publication en ligne :	30/06/2022
En ligne :	https://doi.org/10.1080/10106049.2022.2093411
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=102672

[article]

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IGN / ENSG

L'IGN a pour vocation

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IGN

Centre de documentationscientifique

Accueil

Sélection de la langue

Adresse

Se connecter

Actualité

L'actu ! Horaires du CDOS

Informations pratiques

Généralisation

alerte-généralisation

Documents numériques

IGN / ENSG

L'IGN a pour vocation

Accès directs

2014 -2024 IGN

Centre de documentation
scientifique