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Classification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)  

Public Titre : Classification of time series of Sentinel-2 images for large scale mapping in Cameroon Type de document : Article/Communication Auteurs : Hermann Tagne, Auteur ; Arnaud Le Bris , Auteur ; David Monkam, Auteur ; Clément Mallet , 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. XLIII-B3 Projets : TOSCA Parcelle / Le Bris, Arnaud Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : pp 633 - 640 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Cameroun [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] série temporelle Résumé : (auteur) Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolution. These images are in particular of utter interest to map Land-Cover (LC) at large scale. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained, allowing to provide a first step towards finer-grained map retrieval. Numéro de notice : C2020-006 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-633-2020 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-633-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95656

Current challenges in operational very high resolution land-cover mapping / Clément Mallet (2020)  

Public Titre : Current challenges in operational very high resolution land-cover mapping Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Arnaud Le Bris , 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. XLIII-B2 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 Importance : pp 703 - 710 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] image à très haute résolution Résumé : (auteur) Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions. Numéro de notice : C2020-007 Affiliation des auteurs : LaSTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2020-703-2020 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-703-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95657

Inferring the scale and content of a map using deep learning / Guillaume Touya (2020)  

Public 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. XLIII-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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte numérisée [Termes descripteurs IGN] carte scolaire [Termes descripteurs IGN] carte tactile [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] échelle cartographique [Termes descripteurs IGN] formation [Termes descripteurs IGN] généralisation [Termes descripteurs IGN] géographie physique [Termes descripteurs IGN] individu non-voyant Résumé : (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 : 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

Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)  

Public Titre : Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Cédric Demonceaux, Auteur ; Jingbin Liu, 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. XLIII-B2 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 2 Importance : pp 507 - 514 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 2D [Termes descripteurs IGN] espace intérieur [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] reconstruction 3D Résumé : (auteur) Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system). Numéro de notice : C2020-008 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2020-507-2020 date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-507-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95659

On the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)  

Public Titre : On the joint exploitation of optical and SAR satellite imagery for grassland monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , 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. XLIII-B3-2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : pp 591 - 598 Format : 21 x 30 cm Note générale : bibliographie This research has been funded by the Agence pour le Développement Et la Maîtrise de l’Energie (ADEME) and the Centre National d’Etudes Spatiales (CNES). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] régression [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R 2 =0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution. Numéro de notice : C2020-004 Affiliation des auteurs : LaSTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-591-2020 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-591-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95664

Porting ardupilot to ESP32: towards a universal open-source architecture for agile and easily replicable multi-domains mapping robots / Laurent Beaudoin (2020)  

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A spatio-temporal web-application for the understanding of the formation of the Parisian metropolis / Emile Blettery (2020)  

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Towards SDG 4: trade-offs for geospatial open educational resources / Serena Coetzee (2020)  

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Visual analysis of inconsistencies in hydraulic simulation data / Octave Perrin (2020)  

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Visualizing 3D climate data in urban 3D models / Jacques Gautier (2020)  

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CIPA - Heritage documentation 50 years: Looking backwards / Efstratios Stylianidis (2019)  

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EuroSDR GeoBIM project a study in Europe on how to use the potentials of BIM and GEO data in practice / Francesca Noardo (2019)    

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Interactive image geolocalization in an immersive web application / Mouna Harrach (2019)  

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Evaluation of photogrammetric block orientation using quality descriptors from statistically filtered tie points / Alessio Calantropio (2018)  

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Underground visualization: Web-app, virtual reality, ex situ and in situ augmented reality / Alexandre Devaux (2018) 

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