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Titre : AI4GEO: LOD0 Generation for 3D building models Type de document : Article/Communication Auteurs : Pierre Lassalle, Auteur ; Bruno Vallet , Auteur ; Etienne Le Bihan, Auteur ; Pierre-Marie Brunet, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2023 Projets : AI4GEO / Conférence : JURSE 2023, Joint Urban Remote Sensing Event 17/05/2023 19/05/2023 Heraklion Grèce Proceedings IEEE Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] image satellite
[Termes IGN] niveau de détail
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] zone urbaineRésumé : (Auteur) Recent studies on Earth observation are improved by the proliferation of imaging sensors able to capture large datasets with a high spatial resolution. As a result, many approaches have been proposed for 3D modeling, remote sensing (RS), image processing and mapping. In this scope, three-dimensional (3D) mapping of urban areas has a great potential to provide the user with a precise scene understanding. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information with new added-value services. This paper will first introduce the AI4GEO initiative, context and overall objectives. It will then present the current status regarding 3D reconstruction of urban areas, in particular LOD0 building shape generation using satellite data. Numéro de notice : C2023-010 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE57346.2023.10144155 Date de publication en ligne : 08/06/2023 En ligne : https://doi.org/10.1109/JURSE57346.2023.10144155 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103311
Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cyclone
[Termes IGN] détection d'objet
[Termes IGN] iceberg
[Termes IGN] intelligence artificielle
[Termes IGN] océanographie
[Termes IGN] température de surface de la merRésumé : (éditeur) This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean
2- Forecasting tropical instability waves based on artificial intelligence
3- Sea surface height anomaly prediction based on artificial intelligence
4- Satellite data-driven internal solitary wave forecast based on machine learning techniques
5- AI-based subsurface thermohaline structure retrieval from remote sensing observations
6- Ocean heat content retrieval from remote sensing data based on machine learning
7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations
8- Tropical cyclone monitoring based on geostationary satellite imagery
9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network
10- Detection and analysis of mesoscale eddies based on deep learning
11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country
12- Sea ice detection from SAR images based on deep fully convolutional networks
13- Detection and analysis of marine green algae based on artificial intelligence
14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks
15- Extracting ship’s size from SAR images by deep learning
16- Benthic organism detection, quantification and seamount biology detection based on deep learningNuméro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058 A CNN based approach for the point-light photometric stereo problem / Fotios Logothetis in International journal of computer vision, vol 131 n° 1 (January 2023)
[article]
Titre : A CNN based approach for the point-light photometric stereo problem Type de document : Article/Communication Auteurs : Fotios Logothetis, Auteur ; Roberto Mecca, Auteur ; Ignas Budvytis, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 101 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] éclairement lumineux
[Termes IGN] effet de profondeur cinétique
[Termes IGN] intensité lumineuse
[Termes IGN] itération
[Termes IGN] reconstruction 3D
[Termes IGN] réflectivité
[Termes IGN] stéréoscopie
[Termes IGN] vue perspectiveRésumé : (auteur) Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world ’dataset for near-fieLd point light soUrCe photomEtric Stereo’ of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page. Numéro de notice : A2023-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01689-3 Date de publication en ligne : 07/10/2022 En ligne : https://doi.org/10.1007/s11263-022-01689-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102364
in International journal of computer vision > vol 131 n° 1 (January 2023) . - pp 101 - 120[article]Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan / Ahmad Ansari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan Type de document : Article/Communication Auteurs : Ahmad Ansari, Auteur ; Gökmen Tayfur, Auteur Année de publication : 2023 Article en page(s) : n° 2200890 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Afghanistan
[Termes IGN] bassin hydrographique
[Termes IGN] érosion
[Termes IGN] gradient de pente
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle RUSLE
[Termes IGN] système d'information géographiqueRésumé : (auteur) Slope length gradient (LS) is one of the crucial factors in the Universal Soil Loss Equations (USLE, RUSLE). This study aimed at estimating the slope-length and slope-steepness (LS) factor for the entire watersheds of Afghanistan by using three different methods, namely; (1) LS-TOOLMFD (Method 1); (2) The Method of Equations (Method 2); and (3) The approach of Moore and Burch (Method 3). The first method uses the digital elevation model (DEM) in the ASCII format, and the other two methods use the DEM in the spatial domain. The results show that the LS-factor of the study area ranges from 0.01 to 44.31, with a mean of 5.24 and standard deviation of 6.95, according to Method 1; 0.03 to 163.49, with a mean of 9.6 and standard deviation of 13.58, according to Method 2; and 0 to 3985, with a mean of 7.16 and standard deviation of 29.7, according to Method 3. The study reveals that Methods 1 and 2 are more appropriate than Method 3 because Method 3 yields high LS-factor values close to or at streamlines located near mountainous regions. The highest LS values are found to be in the northeast, north, and central regions of Afghanistan, which is consistent with the high mountains and deep valley geomorphology, indicating that these regions are particularly vulnerable to soil erosion by rainfall-runoff processes. The sediment delivery ratio (SDR) for the Upper-Helmand River Basin (Upper-HRB) is also estimated by the RUSLE, employing the LS factors produced by the three methods. The results revealed that the average annual soil loss is found to be, respectively, 9.3, 18.2, and 11.1 (ton/ha/year) by using the three methods, corresponding to SDR of 23.5%, 12.1%, and 19.9%. Numéro de notice : A2023-193 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2023.2200890 Date de publication en ligne : 18/04/2023 En ligne : https://doi.org/10.1080/19475705.2023.2200890 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103074
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2200890[article]Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation / Remzi Eker in Measurement, vol 206 (January 2023)
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Titre : Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation Type de document : Article/Communication Auteurs : Remzi Eker, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] analyse comparative
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] chemin forestier
[Termes IGN] déformation de surface
[Termes IGN] lidar mobile
[Termes IGN] positionnement cinématique
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] télémétrie laser terrestre
[Termes IGN] TurquieRésumé : (auteur) This study aimed to compare a handheld mobile laser scanning (HMLS), called TORCH that uses the SLAM algorithm, and a PPK-integrated close-range terrestrial photogrammetry (CRTP) to measure forest road surface deformation. The PPK-integrated CRTP includes a multiband GNSS-module and a camera mounted on a 5-m prism pole. 3D point-clouds were gathered/produced at three different dates with approximately 3-month intervals. And then road surface deformations were determined by applying the M3C2 algorithm. Each method was compared by considering some advantages and disadvantages. PPK-integrated CRTP, which could only be used in areas where the GPS signal is not blocked, provided highly denser 3D point clouds than HMLS. However, for the first period, the difference of mean deformation values between the two methods was not statistically significant, whereas it was statistically significant for the second period. Both methods can be suggested to use in forest road surface deformation yet considering their limitations. Numéro de notice : A2023-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.measurement.2022.112322 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1016/j.measurement.2022.112322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102330
in Measurement > vol 206 (January 2023)[article]Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkCross-supervised learning for cloud detection / Kang Wu in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkDecision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkPermalinkDetection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)PermalinkEstimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density / Luyen K. Bui in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)PermalinkForest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkGeneration of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkGeospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkA hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)Permalink