Détail de l'auteur
Auteur Arnaud Le Bris
Commentaire :
researcher in LaSTIG, STRUDEL team
Autorités liées :
idRef :
Google Scholar :
|
Documents disponibles écrits par cet auteur



A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol inconnu ([01/02/2021])
![]()
[article]
Titre : A data fusion-based framework to integrate multi-source VGI in an authoritative land use database Type de document : Article/Communication Auteurs : Lanfa Liu, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Laurence Jolivet
, Auteur ; Arnaud Le Bris
, Auteur ; Linda M. See, Auteur
Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes descripteurs IGN] base de données d'occupation du sol
[Termes descripteurs IGN] base de données localisées de référence
[Termes descripteurs IGN] données hétérogènes
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] fusion de données
[Termes descripteurs IGN] intégration de données
[Termes descripteurs IGN] mise à jour de base de données
[Termes descripteurs IGN] OCS GE
[Termes descripteurs IGN] théorie de Dempster-ShaferRésumé : (auteur) Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally. Numéro de notice : A2021-069 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17538947.2020.1842524 date de publication en ligne : 05/11/2020 En ligne : https://doi.org/10.1080/17538947.2020.1842524 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96522
in International Journal of Digital Earth > vol inconnu [01/02/2021][article]Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)
![]()
[article]
Titre : Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion Type de document : Article/Communication Auteurs : Olivier Stocker, Auteur ; Arnaud Le Bris , Auteur
Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 557 - 564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] fusion d'images
[Termes descripteurs IGN] image Sentinel-MSI
[Termes descripteurs IGN] image SPOT 7
[Termes descripteurs IGN] occupation du sol
[Termes descripteurs IGN] segmentation sémantiqueRésumé : (auteur) Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels. Numéro de notice : A2020-504 Affiliation des auteurs : LaSTIG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-557-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95644
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 557 - 564[article]CNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)
![]()
[article]
Titre : CNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Sébastien Giordano
, Auteur ; Clément Mallet
, Auteur
Année de publication : 2020 Projets : HIATUS / Giordano, Sébastien 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 1013 - 1019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] base de données historiques
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] occupation du sol
[Termes descripteurs IGN] orthoimage
[Termes descripteurs IGN] segmentation sémantiqueRésumé : (auteur) Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets. Numéro de notice : A2020-469 Affiliation des auteurs : LaSTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-1013-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-1013-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95637
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 1013 - 1019[article]Correction of systematic radiometric inhomogeneity in scanned aerial campaigns using principal component analysis / Lâmân Lelégard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)
![]()
[article]
Titre : Correction of systematic radiometric inhomogeneity in scanned aerial campaigns using principal component analysis Type de document : Article/Communication Auteurs : Lâmân Lelégard , Auteur ; Arnaud Le Bris
, Auteur ; Sébastien Giordano
, Auteur
Année de publication : 2020 Projets : HIATUS / Giordano, Sébastien 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 871 - 876 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] algorithme d'homogénéisation
[Termes descripteurs IGN] analyse en composantes principales
[Termes descripteurs IGN] correction radiométrique
[Termes descripteurs IGN] image numériséeRésumé : (auteur) Orthophotomosaic is defined as a single image that can be layered on a map. The term “mosaic” implies that it is produced from a set of images, usually aerial images. Even if these images are taken during cloudless period, they are impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as imaging device systematisms, like lens vignetting. These create some unsightly radiometric inhomogeneity in the orthophotomosaic that could be corrected by using a Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This work introduces an alternative to Wallis filter by considering some systematic radiometric behaviours in the images through a principal component analysis process. Numéro de notice : A2020-501 Affiliation des auteurs : LaSTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-871-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-871-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95642
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 871 - 876[article]Use of automated change detection and VGI sources for identifying and validating urban land use change / Ana-Maria Olteanu-Raimond in Remote sensing, vol 12 n° 7 (April 2020)
![]()
[article]
Titre : Use of automated change detection and VGI sources for identifying and validating urban land use change Type de document : Article/Communication Auteurs : Ana-Maria Olteanu-Raimond , Auteur ; L. See, Auteur ; M. Schultz, Auteur ; Giles M. Foody, Auteur ; M. Riffler, Auteur ; T. Gasber, Auteur ; Laurence Jolivet
, Auteur ; Arnaud Le Bris
, Auteur ; Yann Méneroux
, Auteur ; Lanfa Liu, Auteur ; Marc Poupée, Auteur ; Marie Gombert, Auteur
Année de publication : 2020 Projets : Landsense / Raimond, Ana-Maria Article en page(s) : n° 1186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] carte d'utilisation du sol
[Termes descripteurs IGN] cartographie collaborative
[Termes descripteurs IGN] changement d'utilisation du sol
[Termes descripteurs IGN] détection automatique
[Termes descripteurs IGN] détection de changement
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] estimation de précision
[Termes descripteurs IGN] science citoyenne
[Termes descripteurs IGN] zone urbaineRésumé : (Auteur) Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult. Numéro de notice : A2020-243 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12071186 date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/rs12071186 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95217
in Remote sensing > vol 12 n° 7 (April 2020) . - n° 1186[article]Classification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)
PermalinkPermalinkVers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)
PermalinkA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)
![]()
PermalinkPartial 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)
PermalinkArchival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)
PermalinkA comparison of several spectral and spatial configuration for urban material classification / Arnaud Le Bris (2019)
PermalinkGeographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)
PermalinkHYEP, HYperspectral imagery for Environmental urban Planning : principaux résultats / Christiane Weber (2019)
PermalinkInnovative Methods and Products of the " Urbanization and Artificialization" Scientific Expertise Centre / Anne Puissant (2019)
Permalink