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Automated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)  

Public [article]  inPhotogrammetric record > vol 36 n° 173 (March 2021) . - pp 12 - 35 Titre : Automated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images Type de document : Article/Communication Auteurs : Luigi Parente, Auteur ; Jim H. Chandler, Auteur ; Neil Dixon, Auteur Année de publication : 2021 Article en page(s) : pp 12 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] algorithme ICP [Termes IGN] alignement [Termes IGN] Angleterre [Termes IGN] détection de changement [Termes IGN] données multisources [Termes IGN] données multitemporelles [Termes IGN] géoréférencement direct [Termes IGN] image aérienne oblique [Termes IGN] image captée par drone [Termes IGN] image oblique [Termes IGN] image terrestre [Termes IGN] modèle stéréoscopique [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] semis de points [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion Résumé : (auteur) Accurate alignment of 3D models is critical for valid change‐detection analysis from multitemporal photogrammetric datasets. This paper assesses an automated registration strategy which uses the scale‐invariant feature transform (SIFT) algorithm implemented in modern photogrammetric software. This registration solution, also known as “Time‐SIFT”, was tested at two study sites featuring vertical surfaces, including a sea cliff (~500 m2) and a quarry face (~50 000 m2). Tests demonstrated that the investigated registration strategy can achieve accurate alignments between multitemporal point clouds even when using multisource and multi‐perspective data, captured across widely varying spatial and temporal scales and under a range of weather and illumination conditions. The combination of the Time‐SIFT approach with an ICP algorithm produced moderate improvements in the alignment. Furthermore, the use of an innovative direct georeferencing technique, which used the tracking feature of a robotic total station, allowed for accurate georectification of 3D models. Numéro de notice : A2021-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1111/phor.12346 date de publication en ligne : 06/01/2021 En ligne : https://doi.org/10.1111/phor.12346 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97377 [article]

Digital surface model refinement based on projected images / Jiali Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 3 (March 2021) . - pp 181 - 187 Titre : Digital surface model refinement based on projected images Type de document : Article/Communication Auteurs : Jiali Wang, Auteur ; Yannan Chen, Auteur Année de publication : 2021 Article en page(s) : pp 181 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement d'images [Termes IGN] correction d'image [Termes IGN] modèle numérique de surface Résumé : (Auteur) Currently, the practical solution to remove the errors and artifacts in the digital surface models (DSM ) through stereo images is still manual or semiautomatic editing those affected patches. Although some degrees of semiautomation can be gained, the DSM refinement remains a labor consuming and expensive process. This paper proposes a new method to correct errors in DSM or/and refine an existing coarse DSM. The method employs the concept of projected images together with some image matching techniques to correct/ refine the affected regions in DSM. Since projected images are used, the proposed method can greatly simplify the complicated coordinate transformations and pixel resampling; therefore, the errors/artifacts in DSM can be amended more efficiently and accurately. Several experiments demonstrate the practical usefulness of the proposed method under some scenarios, and some potential improvements are also pointed out to accommodate the various needs during refining DSM. Numéro de notice : A2021-242 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.181 date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.14358/PERS.87.3.181 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97288 [article]

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Remote sensing and GIS / Basudeb Bhatta (2021)

Public Titre : Remote sensing and GIS Type de document : Guide/Manuel Auteurs : Basudeb Bhatta, Auteur Mention d'édition : 3ème édition Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 2021 Importance : 752 p. Format : 24 x 18 cm ISBN/ISSN/EAN : 978-0-19-949664-8 Note générale : Bibliographie additional reading material with Oxford areal Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] acquisition d'images [Termes IGN] airborne multispectral scanner [Termes IGN] analyse spatiale [Termes IGN] Global Navigation Satellite System [Termes IGN] image hyperspectrale [Termes IGN] image thermique [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Lidar [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation 3D [Termes IGN] orthorectification [Termes IGN] Passive and Active L and S band Sensor [Termes IGN] photographie aérienne [Termes IGN] Satellite Microwave Radiometer [Termes IGN] scène 3D [Termes IGN] stéréoscopie [Termes IGN] système d'information géographique [Termes IGN] traitement d'image [Termes IGN] visualisation 3D Index. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) Beginning with the history and basic concepts of remote sensing and GIS, the book gives an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (such as GPS and IRNSS), digital photogrammetry, visual image analysis, digital image processing, spatial and attribute data model, geospatial analysis, and planning, implementation, and management of GIS. It also presents the modern trends of remote sensing and GIS with an illustrated discussion on its numerous applications. Note de contenu : 1. Concept of Remote Sensing 1.1 Introduction 1.2 Distance of Remote Sensing 1.3 Definition of Remote Sensing 1.4 Remote Sensing: Art and/or Science 1.5 Data 1.6 Remote Sensing Process 1.7 Source of Energy 1.8 Interaction with Atmosphere 1.9 Interaction with Target 1.9.1 Hemispherical Absorptance, Transmittance, and Reflectan 1.10 Interaction with the Atmosphere Again 1.11 Recording of Energy by Sensor 1.12 Transmission, Reception, and Processing 1.13 Interpretation and Analysis 1.14 Applications of Remote Sensing 1.15 Advantages of Remote Sensing 1.16 Limitations of Remote Sensing 1.17 Ideal Remote Sensing System 2. Types of Remote Sensing and Sensor Characteristics 2.1 Introduction 2.2 Types of Remote Sensing 2.3 Characteristics of Images 2.4 Orbital Characteristics of Satellite 2.5 Remote Sensing Satellites 2.6 Concept of Swath 2.7 Concept of Nadir 2.8 Sensor Resolutions 2.9 Image Referencing System 2.9.1 Path 2.9.2 Row 2.9.3 Orbital Calendar 3. History of Remote Sensing and Indian Space Program 3.1 Introduction 3.2 The Early Age 3.3 The Middle Age 3.4 The Modern Age or Space Age 3.5 Indian Space Program 4. Photographic Imaging 4.1 Introduction 4.2 Camera Systems 4.3 Types of Camera 4.4 Filter 4.5 Film 4.6 Geometry of Aerial Photography 4.7 Ideal Time and Atmosphere for Aerial Remote Sensing 5. Digital Imaging 5.1 Introduction 5.2 Digital Image 5.3 Sensor 5.4 Imaging by Scanning Technique 5.5 Hyper-spectral Imaging 5.6 Imaging By Non-scanning Technique 5.7 Thermal Remote Sensing 5.8 Other Sensors 6. Microwave Remote Sensing 6.1 Introduction 6.2 Passive Microwave Remote Sensing 6.3 Active Microwave Remote Sensing 6.4 Radar Imaging 6.5 Airborne Versus Space-Borne Radars 6.6 Radar Systems 7. Ground-truth Data and Global Positioning System 7.1 Introduction 7.2 Requirements of Ground-Truth Data 7.3 Instruments for Ground Truthing 7.4 Parameters of Ground Truthing 7.5 Factors of Spectral Measurement 7.6 Global Navigation Satellite System 8. Photogrammetry 8.1 Introduction 8.2 Development of Photogrammetry 8.3 Classification of Photogrammetry 8.4 Photogrammetric Process 8.5 Acquisition of Imagery and its Support Data 8.6 Orientation and Triangulation 8.7 Stereo Model Compilation 8.8 Stereoscopic 3D Viewing 8.9 Stereoscopic Measurement 8.10 DTM/DEM Generation 8.11 Contour Map Generation 8.12 Orthorectification 8.13 3D Feature Extraction 8.14 3D Scene Modelling 8.15 Photogrammetry and LiDAR 8.16 Radargrammetry and Radar Interferometry 8.17 Limitations of Photogrammetry 9. Visual Image Interpretation 9.1 Introduction 9.2 Information Extraction by Human and Computer 9.3 Remote Sensing Data Products 9.4 Border or Marginal Information 9.5 Image Interpretation 9.6 Elements of Visual Image Interpretation 9.7 Interpretation Keys 9.8 Generation of Thematic Maps 9.9 Thermal Image Interpretation 9.10 Radar Image Interpretation 10. Digital Image Processing 10.1 Introduction 10.2 Categorization of Image Processing 10.3 Image Processing Systems 10.4 Digital Image 10.5 Media for Digital Data Recording, Storage, and Distribution 10.6 Data Formats of Digital Image 10.7 Header Information 10.8 Display of Digital Image 10.9 Pre-processing 10.10 Image Enhancement 10.11 Image Transformation 10.12 Image Classification 11. Data Integration, Analysis, and Presentation 11.1 Introduction 11.2 Multi-approach of Remote Sensing 11.3 Integration with Ground Truth and Other Ancillary Data 11.4 Integration of Transformed Data 11.5 Integration with GIS 11.6 Process of Remote Sensing Data Analysis 11.7 The Level of Detail 11.8 Limitations of Remote Sensing Data Analysis 11.9 Presentation 12. Applications of Remote Sensing 12.1 Introduction 12.2 Land Cover and Land Use 12.3 Agriculture 12.4 Forestry 12.5 Geology 12.6 Geomorphology 12.7 Urban Applications 12.8 Hydrology 12.9 Mapping 12.10 Oceans and Coastal Monitoring 12.11 Monitoring of Atmospheric Constituents PART II Geographic Information Systems and Geospatial Analysis 13. Concept of Geographic Information Systems 13.1 Introduction 13.2 Definitions of GIS 13.3 Key Components of GIS 13.4 GIS-An Integration of Spatial and Attribute Information 13.5 GIS-Three Views of Information System 13.6 GIS and Related Terms 13.7 GIS-A Knowledge Hub 13.8 GIS-A Set of Interrelated Subsystems 13.9 GIS-An Information Infrastructure 13.10 Origin of GIS 14. Functions and Advantages of GIS 14.1 Introduction 14.2 Functions of GIS 14.3 Application Areas of GIS 14.4 Advantages of GIS 14.5 Functional Requirements of GIS 14.6 Limitations of GIS 15. Spatial Data Model 15.1 Introduction 15.2 Spatial, Thematic, and Temporal Dimensions of Geographic Data 15.3 Spatial Entity and Object 15.4 Spatial Data Model 15.5 Raster Data Model 15.6 Vector Data Model 15.7 Raster versus Vector 15.8 Object-Oriented Data Model 15.9 File Formats of Spatial Data 16. Attribute Data Management and Metadata Concept 16.1 Introduction 16.2 Concept of Database and DBMS 16.3 Advantages of DBMS 16.4 Functions of DBMS 16.5 File and Data Access 16.6 Data Models 16.7 Database Models 16.8 Data Models in GIS 16.9 Concept of SQL 16.10 Concept of Metadata 17. Process of GIS 17.1 Introduction 17.2 Data Capture 17.3 Data Sources 17.4 Data Encoding Methods 17.5 Linking of Spatial and Attribute Data 17.6 Organizing Data for Analysis 18. Geospatial Analysis 18.1 Introduction 18.2 Geospatial Data Analysis 18.3 Integration and Modelling of Spatial Data 18.4 Geospatial Data Analysis Methods 18.5 Database Query 18.6 Geospatial Measurements 18.7 Overlay Operations 18.8 Network Analysis 18.9 Surface Analysis 18.10 Geostatistics 18.11 Geovisualization 19. Planning, Implementation, and Management of GIS 19.1 Introduction 19.2 Planning of Project 19.3 Implementation of Project 19.4 Management of Project 19.5 Keys for Successful GIS 19.6 Reasons for Unsuccessful GIS 20. Modern Trends of GIS 20.1 Introduction 20.2 Local to Global Concept in GIS 20.3 Increase in Dimensions in GIS 20.4 Linear to Non-linear Techniques in GIS 20.5 Development in Relation between Geometry and Algebra in GIS 20.6 Development of Common Techniques in GIS 20.7 Integration of GIS and Remote Sensing 20.8 Integration of GIS and Multimedia 20.9 3D GIS 20.9.1 Virtual Reality in GIS 20.10 Integration of 3D GIS and Web GIS 20.11 4D GIS and Real-time GIS 20.12 Mobile GIS 20.12.1 Mobile mapping 20.13 Collaborative GIS (CGIS) 21. Change Detection and Geosimulation 21.1 Visual change detection 21.2 Thresholding 21.3 Image difference 21.4 Image regression 21.5 Image ratioing 21.6 Vegetation index differencing 21.7 Principal component differencing 21.8 Multi-temporal image stock classification 21.9 Post classification comparison 21.10 Change vector analysis 21.12 Cellular automata simulation 21.13 Multi-agent simulation 21.14 ANN learning in simulation Appendix A - Concept of Map, Coordinate System, and Projection Appendix B - Concept on Mathematical Topics Numéro de notice : 26518 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97342

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Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 152 - 165 Titre : Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data Type de document : Article/Communication Auteurs : Wai Yeung Yan, Auteur ; Karin Y. Van Ewijk, Auteur ; Paul M. Treitz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 152 - 165 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] artefact [Termes IGN] classification par forêts aléatoires [Termes IGN] correction d'image [Termes IGN] correction radiométrique [Termes IGN] couvert forestier [Termes IGN] délignage [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] intensité lumineuse [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Ontario (Canada) [Termes IGN] peuplement mélangé [Termes IGN] restauration d'image [Termes IGN] semis de points Résumé : (auteur) In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual flight lines and overlapping regions of adjacent flight lines of a multispectral LiDAR dataset. We tested the effectiveness of these corrections in various land/forest cover types in a temperate mixed mature forest in Ontario, Canada. Subsequently, we predicted three plot level forest attributes, i.e., basal area (BA), quadratic mean diameter (QMD), and trees per hectare (TPH), using different combinations of height and intensity metrics derived from the multispectral LiDAR data to determine if LiDAR intensity data (corrected and uncorrected) improved predictions over models that utilize LiDAR height-derived information only. The results show that LSLC can reduce the intensity banding effect by 0.19–23.06% in channel 1 (1550 nm) and 4.79–66.87% in channel 2 (1064 nm) at the close-to-nadir region. The combined effect of LSLC and OIC is notable particularly at the swath edges. After implementing both methods, the intensity homogeneity is improved by 5.51–12% in channel 1, 6.37–42.93% in channel 2, and 6.48–33.77% in channel 3 (532 nm). Our results further demonstrate that BA and QMD predictions in our study area gained little from additional LiDAR intensity metrics. Intensity metrics from multiple LiDAR channels and intensity normalized difference vegetation index (NDVI) metrics did improve TPH predictions up to 7.2% in RMSE and 1.8% in Bias. However, our lowest TPH prediction errors (%RMSE) were still approximately 10% larger than for BA and QMD. We observed only minimal differences in plot level BA, QMD, and TPH predictions between models using original and corrected intensity. We attribute this to: (i) the lower effectiveness of radiometric correction in forest versus grassland, bare soil and road land cover types, and (ii) the effect of spatial resolution on intensity noise. Numéro de notice : A2020-640 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.001 date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96063 [article]

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A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 377 - 388 Titre : A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery Type de document : Article/Communication Auteurs : Niangang Jiao, Auteur ; Feng Wang, Auteur ; Hongjian You, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 377 - 388 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] chaîne de traitement [Termes IGN] correction géométrique [Termes IGN] étalonnage géométrique [Termes IGN] géolocalisation [Termes IGN] image Gaofen [Termes IGN] image Jilin [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image satellite [Termes IGN] point d'appui [Termes IGN] précision géométrique (imagerie) Résumé : (auteur) To date, numerous Earth observation datasets from different types of satellites have been widely used in photogrammetric fields, including urban 3D modelling and geographic information systems. The development of small satellites has provided a new way to obtain repeated observations in a short period. However, compared with that of standard satellite imagery, the geometric performance of imagery from small satellites is relatively poor, restricting their photogrammetric applications. Traditional methods can improve the accuracy of optical images with the addition of well-distributed ground control points (GCPs), which require considerable financial and human resources. The collection of multi-view datasets is an alternative method for geometric processing without GCPs, but relies heavily on the stability and revisit period of satellite platforms. Therefore, this paper presents a framework for improving the geopositioning accuracy of multi-source datasets obtained from optical and synthetic aperture radar (SAR) satellites, and a novel heterogeneous weight strategy is proposed based on an analysis of the geometric error sources of SAR and optical images. The geometric performance of multi-source optical imagery from the Jilin-1 (JL-1) small satellite constellation is evaluated and analysed first, and then Gaofen-3 (GF-3) SAR images are calibrated based on statistical analysis for the production of virtual control points (VCPs). Based on our proposed heterogeneous weight strategy, multi-source optical and SAR images are integrated to improve the geopositioning accuracy. Experimental results indicate that our proposed model can achieve the best performance compared with other popular models, producing an accuracy of approximately 3 m in planimetry and 2 m in height, thereby providing a generic way to synergistically use multi-source remote sensing data. Numéro de notice : A2020-642 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.017 date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96066 [article]

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Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases / Amin Alizadeh Naeini in Geocarto international, vol 35 n° 12 ([01/09/2020])  

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A spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)  

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Ambiguous use of geographical information systems for the rectification of large-scale geometric maps / Anders Wästfelt in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

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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)  

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Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing / Desta Ekaso in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Geomorphic Change Detection Using Cost-Effective Structure-from-Motion Photogrammetry: Evaluation of Direct Georeferencing from Consumer-Grade UAS at Orewa Beach (New Zealand) / Stéphane Bertin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 5 (May 2020)  

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Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Refractive two-view reconstruction for underwater 3D vision / François Chadebecq in International journal of computer vision, vol 128 n° 5 (May 2020)  

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Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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