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Historical Vltava River valley–various historical sources within web mapping environment / Jiří Krejčí in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 35 Titre : Historical Vltava River valley–various historical sources within web mapping environment Type de document : Article/Communication Auteurs : Jiří Krejčí, Auteur ; Jiří Cajthaml, Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] ArcGIS [Termes IGN] carte ancienne [Termes IGN] changement d'utilisation du sol [Termes IGN] données anciennes [Termes IGN] géoréférencement [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] République Tchèque [Termes IGN] rivière [Termes IGN] système d'information historique [Termes IGN] vectorisation [Termes IGN] web mapping Résumé : (auteur) The article deals with a comprehensive information system of the historic Vltava River valley. This system contains a number of resources, which are described. For old maps, which are the basis of the whole system, their georeferencing and potential problems in creating seamless mosaics are described. Other sources of data include old photographs, which are localized and stored in the system, along with the definition point of the place from which they were probably taken. The vectorization of data is described, not only for area features used for the analysis of land-use changes, but also for the vectorization of contours. These were vectorized from old maps and are substantial for the creation of historic DEM. Vectorized footprints of buildings and vectors of other functional areas subsequently serve as a basis for the procedural modeling of the virtual 3D landscape. The creation of such a complex and broad information system cannot be described in one article. The aim of this text is to draw attention to a possible approach to the presentation and visualization of the historic landscape, along with links to important documents. Numéro de notice : A2022-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010035 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/ijgi11010035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99380 [article]

Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission / Nicolas Gasnier (2022)  

Public Titre : Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission Type de document : Thèse/HDR Auteurs : Nicolas Gasnier, Auteur ; Florence Tupin, Directeur de thèse ; Loïc Denis, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Importance : 213 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de doctorat présentée à l’Institut Polytechnique de Paris, spécialité Images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] base de données localisées [Termes IGN] détection d'objet [Termes IGN] détection de changement [Termes IGN] données hydrographiques [Termes IGN] hauteurs de mer [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] image SWOT [Termes IGN] lac [Termes IGN] rivière [Termes IGN] série temporelle [Termes IGN] télédétection en hyperfréquence Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it. Note de contenu : 1. Introduction 1.1 Context 1.2 Contributions 1.3 Organization of the manuscript I BACKGROUND ON SAR REMOTE SENSING AND WATER SURFACE MONITORING WITH SAR IMAGES 2. SAR images 2.1 Physics and statistics of SAR images 2.2 The SWOT mission 2.3 Sentinel-1 3. SAR water detection and hydrological prior 3.1 Water detection in SAR images 3.2 SWOT processing and products 3.3 Prior water masks and databases 4. Methodological background 4.1 Markov random fields 4.2 Variational methods for image denoising PROPOSED APPROACHES 5. Guided extraction of narrow rivers on SAR images using an exogenous river database 5.1 Introduction 5.2 Proposed river segmentation pipeline 5.3 Experimental results 5.4 Conclusion 6. Adaptation of the GrabCut method to SAR images: lake detection from a priori polygon 6.1 Single-date GrabCut method for lake detection from a priori polygon 6.2 Multitemporal and multi-sensor adaptations of the method 6.3 2D+T GrabCut of SAR images with temporal regularization for lake detection within an a priori mask 6.4 Joint 2D+T segmentation of SAR and optical images 7. Denoising of the temporal geometric mean 7.1 Introduction 7.2 Statistics of the temporal geometric mean of SAR intensities 7.3 Denoising method 7.4 Experiments 7.5 Application to change detection 7.6 Application to ratio-based denoising of single SAR images within a time series 7.7 Conclusion 8 Conclusion and perspectives Numéro de notice : 26762 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Images : Palaiseau : 2022 Organisme de stage : Télécom Paris nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/02/2022 En ligne : https://tel.archives-ouvertes.fr/tel-03578831/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99823

Fluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)  

Public [article]  inEuropean journal of remote sensing > vol 54 sup 1 (2021) . - pp 31 - 46 Titre : Fluvial gravel bar mapping with spectral signal mixture analysis Type de document : Article/Communication Auteurs : Liza Stančič, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2021 Article en page(s) : pp 31 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] bassin hydrographique [Termes IGN] carte thématique [Termes IGN] gravier [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] précision infrapixellaire [Termes IGN] réflectance spectrale [Termes IGN] rivière [Termes IGN] signature spectrale [Termes IGN] Slovénie Résumé : (auteur) The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping. Numéro de notice : A2021-817 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2020.1811776 Date de publication en ligne : 30/08/2020 En ligne : https://doi.org/10.1080/22797254.2020.1811776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98906 [article]

Assessing historical maps for characterizing fluvial corridor changes at a regional network scale / Samuel Dunesme in Cartographica, vol 55 n° 4 (Winter 2020)  

Public [article]  inCartographica > vol 55 n° 4 (Winter 2020) . - pp 251 - 265 Titre : Assessing historical maps for characterizing fluvial corridor changes at a regional network scale Type de document : Article/Communication Auteurs : Samuel Dunesme , Auteur ; Hervé Piegay, Auteur ; Sébastien Mustière , Auteur Année de publication : 2020 Projets : EUR H20'Lyon / Article en page(s) : pp 251 - 265 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse diachronique [Termes IGN] base de données historiques [Termes IGN] base de données topographiques [Termes IGN] carte de base [Termes IGN] corridor biologique [Termes IGN] données hydrographiques [Termes IGN] géomorphologie [Termes IGN] rivière [Termes IGN] trame bleue [Termes IGN] vectorisation [Termes IGN] vingtième siècle Résumé : (Auteur) Fluvial corridor quality assessment requires that historical data be collected at a regional scale. In this article, our goal is to assess potential map resources to explore riverscape changes at a regional network scale and to define key issues in using an automated vectorization protocol to characterize such changes on such a large scale. We consider IGN’s Nouvelle Carte de France a potentially good resource for our objective of two-date (oldest + actual vector database) comparisons on 1:20,000–1:25,000 scale maps, notably when applied at a regional scale. The French IGN corpus is a good example of topographic maps that were produced in the twentieth century in Europe with fairly homogeneous data over a whole national territory. Moreover, the digitization and georeferencing processes applied by IGN are very accurate. The evolution of conventional features is not as significant for the hydrographic theme and should not be a problem for automatic vectorization. The potential temporal coverage is from 1922 to 1993, but the complexity of the sheet divisions, partial revisions, and the heterogeneity of coverage over time prevent multidate analysis. Numéro de notice : A2020-775 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2019-0025 Date de publication en ligne : 22/12/2020 En ligne : https://doi.org/10.3138/cart-2019-0025 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96689 [article]

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River ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7570 - 7579 Titre : River ice segmentation with deep learning Type de document : Article/Communication Auteurs : Abhineet Singh, Auteur ; Hayden Kalke, Auteur ; Mark Loewen, Auteur Année de publication : 2020 Article en page(s) : pp 7570 - 7579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] glace [Termes IGN] image captée par drone [Termes IGN] rivière [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique Résumé : (auteur) This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain. Numéro de notice : A2020-674 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2981082 Date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2981082 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96165 [article]

Can we characterize river corridor evolution at a continental scale from historical topographic maps? A first assessment from the comparison of four countries / J. Horacio Garcia in River Research and Applications, vol 36 n° 6 (July 2020)  

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Assessment of the accuracy of DTM river bed model using classical surveying measurement and LiDAR: a case study in Poland / Pawel Kotlarz in Survey review, vol 52 n° 372 (May 2020)  

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National scale identification and characterization of braided rivers in New Zealand using Google Earth Engine / Alexis Jean (2020) 

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sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October-1 2019)  

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Investigating the accuracy of a bathymetric refraction correction on Structure from Motion photogrammetric datasets / Aelaïg Cournez (2019) 

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Utilisation conjointe de trains d'ondes LiDAR vert et infrarouge pour la bathymétrie des eaux de très faibles profondeurs / Tristan Allouis in Revue Française de Photogrammétrie et de Télédétection, n° 213 - 214 (janvier - avril 2017)  

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Contribution au développement d'un outil destiné à la diffusion et l'analyse de données spatiales sur les grandes rivières d'Europe / Bruno Giusti (2016) 

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DEM measurements of a gravel-bed surface using two scales of images / Chi-Kuei Wang in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)  

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Acquisition par drone pour les relevés topographiques / Marie Grob in XYZ, n° 141 (décembre 2014 - février 2015) 

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Système multi-agent pour la modélisation des écoulements de surface sur un petit bassin versant viticole du Layon / Mahefa Mamy Rakotoarisoa in Revue internationale de géomatique, vol 24 n° 3 (septembre - novembre 2014)  

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