Publications du LaSTIG
Les publications antérieures au LaSTIG sont celles des laboratoires qui ont formé le LaSTIG : COGIT, LOEMI et MATIS, à l'exception du LAREG - Vous pouvez affiner la recherche au sein des références
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Imprimer...](./images/print.gif)
Titre : Geovisualization: multidimensional exploration of the territory Type de document : Article/Communication Auteurs : Sidonie Christophe , Auteur
Editeur : Setúbal [Portugal] : Science and Technology Publications - Scitepress Année de publication : 2020 Projets : 1-Pas de projet / Conférence : IVAPP 2020, 11th International Conference on Information Visualization Theory and Applications 27/02/2020 29/02/2020 La Vallette Malte Proceedings ScitePress Importance : pp 325 - 332 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de données
[Termes IGN] analyse multidimensionnelle
[Termes IGN] cognition
[Termes IGN] données hétérogènes
[Termes IGN] données spatiotemporelles
[Termes IGN] immersion
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) The purpose of this position paper is to emphasize the remaining challenges for geovisualization in an evolutive context of data, users and spatio-temporal problems to solve in an interdisciplinary approach. Geovisualization is the visualization of spatio-temporal data, phenomena and dynamics on earth, based on the user interaction with heterogeneous data, and their capacities of perception and cognition. This implies to bring closer together knowledge, concepts and models from related scientific visualization domains, for a better understanding, interpretation and analysis of spatio-temporal phenomena on earth. We currently face and cross several types of complexities, regarding spaces, data, models and tools. Our position here, based on past and on-going works, as first proofs of concept, is to model a multidimensional exploration of the territory, because integrating explorations of uses, styles, interaction and immersion capacities, until various ’points of view’ on the represented spatio-temporal phenomenon. Numéro de notice : C2020-001 Affiliation des auteurs : LASTIG COGIT (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5220/0009355703250332 En ligne : https://doi.org/10.5220/0009355703250332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94611 Guided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
![]()
[article]
Titre : Guided feature matching for multi-epoch historical image blocks pose estimation Type de document : Article/Communication Auteurs : Lulin Zhang , Auteur ; Ewelina Rupnik
, Auteur ; Marc Pierrot-Deseilligny
, Auteur
Année de publication : 2020 Projets : DISRUPT / Klinger, Yann Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 127 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] appariement de points
[Termes IGN] bloc d'images
[Termes IGN] estimation de pose
[Termes IGN] Hérault (34)
[Termes IGN] image aérienne
[Termes IGN] mesure de similitude multidimensionnelle
[Termes IGN] modèle numérique de surface
[Termes IGN] point d'appui
[Termes IGN] points homologues
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) Historical aerial imagery plays an important role in providing unique information about evolution of our landscapes. It possesses many positive qualities such as high spatial resolution, stereoscopic configuration and short time interval. Self-calibration reamains a main bottleneck for achieving the intrinsic value of historical imagery, as it involves certain underdeveloped research points such as detecting inter-epoch tie-points. In this research, we present a novel algorithm to detecting inter-epoch tie-points in historical images which do not rely on any auxiliary data. Using SIFT-detected keypoints we perform matching across epochs by interchangeably estimating and imposing that points follow two mathematical models: at first a 2D spatial similarity, then a 3D spatial similarity. We import GCPs to quantitatively evaluate our results with Digital Elevation Models (DEM) of differences (abbreviated as DoD) in absolute reference frame, and compare the results of our method with other 2 methods that use either the traditional SIFT or few virtual GCPs. The experiments show that far more correct inter-epoch tie-points can be extracted with our guided technique. Qualitative and quantitative results are reported. Numéro de notice : A2020-411 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-127-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-127-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95081
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 127 - 134[article]Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)
![]()
[article]
Titre : Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Simon Bailly
, Auteur ; Loïc Landrieu
, Auteur ; Nesrine Chehata
, Auteur
Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 431 - 441 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alpes-de-haute-provence (04)
[Termes IGN] chaîne de traitement
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] parcelle agricole
[Termes IGN] photo-identification
[Termes IGN] Seine-et-Marne (77)
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (Auteur) Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identification system (LPIS) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear-chain conditional random field, trained with time series of multimodal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark positive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops. Numéro de notice : A2020-382 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.431 Date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.431 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95428
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 431 - 441[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2020071 SL Revue Centre de documentation Revues en salle Disponible Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)
![]()
[article]
Titre : Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series Type de document : Article/Communication Auteurs : Maylis Lopes, Auteur ; Pierre-Louis Frison , Auteur ; Merry Crowson, Auteur ; Eleanor Warren-Thomas, Auteur ; et al., Auteur
Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Mallet, Clément Article en page(s) : pp 532 - 541 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification
[Termes IGN] fusion d'images
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Indonésie
[Termes IGN] nébulosité
[Termes IGN] série temporelle
[Termes IGN] tourbière
[Termes IGN] zone intertropicaleRésumé : (auteur) The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel-1 and Sentinel-2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel-1, +2.5% for Sentinel-2 and +2% combining Sentinel-1 and Sentinel-2). Overall, combining Sentinel-2 and Sentinel-1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open-source tools satellite imagery. Numéro de notice : A2020-875 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/2041-210X.13359 Date de publication en ligne : 27/01/2020 En ligne : https://doi.org/10.1111/2041-210X.13359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99668
in Methods in ecology and evolution > vol 11 n° 4 (April 2020) . - pp 532 - 541[article]
Titre : Inferring the scale and content of a map using deep learning Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Florentin Brisebard, Auteur ; Félix Quinton
, Auteur ; Azelle Courtial
, Auteur
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B4 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 4 Importance : pp 17 - 24 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] carte numérisée
[Termes IGN] carte scolaire
[Termes IGN] carte tactile
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échelle cartographique
[Termes IGN] formation
[Termes IGN] généralisation
[Termes IGN] géographie physique
[Termes IGN] personne non-voyanteRésumé : (auteur) Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. "there are roads, cities and administrative boundaries"), and to infer the extent of the map (e.g. a map of France or of Europe). Numéro de notice : C2020-002 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B4-2020-17-2020 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-17-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95391 Information Géographique Volontaire, vers un usage conjoint avec l’information géographique institutionnelle / Ana-Maria Olteanu-Raimond (2020)
PermalinkInitiatives for Providing Data and Tools for Research and Education: EuroSDR survey / Bénédicte Bucher (2020)
PermalinkLightweight temporal self-attention for classifying satellite images time series / Vivien Sainte Fare Garnot (2020)
PermalinkMapGenOnto: A shared ontology for map generalisation and multi-scale visualisation / Guillaume Touya (2020)
PermalinkMapping the French green infrastructure – an exercise in homogenizing heterogeneous regional data / Lucille Billon in International journal of cartography, Vol 6 n° 2 (July 2020)
PermalinkPermalinkModalflow: cross-origin flow data visualization for urban mobility / Ignacio Pérez-Messina in Algorithms, vol 13 n° 11 (November 2020)
PermalinkLa modélisation en géographie : villes et territoires, ch. 4. Modélisation territoriale incrémentale / Clémentine Cottineau (2020)
PermalinkMonitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas / Nadia Ouaadi in Remote sensing of environment, Vol 251 (15 December 2020)
PermalinkMoving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)
Permalink