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Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)  

Public [article]  inRobotics and autonomous systems > vol 152 (June 2022) . - n° 104037 Titre : Summarizing large scale 3D mesh for urban navigation Type de document : Article/Communication Auteurs : Imeen Ben Salah, Auteur ; Sébastien Kramm, Auteur ; Cédric Demonceaux, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme ICP [Termes IGN] carte en 3D [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image hémisphérique [Termes IGN] image RVB [Termes IGN] information sémantique [Termes IGN] localisation basée vision [Termes IGN] maillage [Termes IGN] navigation autonome [Termes IGN] précision géométrique (imagerie) [Termes IGN] précision radiométrique [Termes IGN] profondeur [Termes IGN] Rouen [Termes IGN] saillance [Termes IGN] zone urbaine Résumé : (auteur) Cameras have become increasingly common in vehicles, smartphones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied: pedestrian detection, line crossing detection, navigation, …A major area of research currently focuses on mapping that is essential for localization and navigation. However, this step generates an important problem of memory management. Indeed, the memory space required to accommodate the map of a small city is measured in tens gigabytes. In addition, several providers today are competing to produce High-Definition (HD) maps. These maps offer a rich and detailed representation of the environment for highly accurate localization. However, they require a large storage capacity and high transmission and update costs. To overcome these problems, we propose a solution to summarize this type of map by reducing the size while maintaining the relevance of the data for navigation based on vision only. The summary consists in a set of spherical images augmented by depth and semantic information and allowing to keep the same level of visibility in every directions. These spheres are used as landmarks to offer guidance information to a distant agent. They then have to guarantee, at a lower cost, a good level of precision and speed during navigation. Some experiments on real data demonstrate the feasibility for obtaining a summarized map while maintaining a localization with interesting performances. Numéro de notice : A2022-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.robot.2022.104037 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1016/j.robot.2022.104037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100335 [article]

Monocular depth estimation in forest environments / Hristina Hristova (2022)  

Public Titre : Monocular depth estimation in forest environments Type de document : Article/Communication Auteurs : Hristina Hristova, Auteur ; Meinrad Abegg, Auteur ; Christoph Fischer, Auteur ; Nataliia Rehush, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2 Conférence : ISPRS 2022, XXIV ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 1017 - 1023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage dirigé [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] image isolée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] jeu de données localisées [Termes IGN] profondeur [Termes IGN] vision monoculaire Résumé : (auteur) Depth estimation from a single image is a challenging task, especially inside the highly structured forest environment. In this paper, we propose a supervised deep learning model for monocular depth estimation based on forest imagery. We train our model on a new data set of forest RGB-D images that we collected using a terrestrial laser scanner. Alongside the input RGB image, our model uses a sparse depth channel as input to recover the dense depth information. The prediction accuracy of our model is significantly higher than that of state-of-the-art methods when applied in the context of forest depth estimation. Our model brings the RMSE down to 2.1 m, compared to 4 m and above for reference methods. Numéro de notice : C2022-022 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2022-1017-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2022-1017-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100848

CNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 7 (July 2021) . - pp 2076 - 2096 Titre : CNN-based RGB-D salient object detection: Learn, select, and fuse Type de document : Article/Communication Auteurs : Hao Chen, Auteur ; Yongjian Deng, Auteur ; Guosheng Lin, Auteur Année de publication : 2021 Article en page(s) : pp 2076 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image RVB [Termes IGN] profondeur [Termes IGN] saillance [Termes IGN] segmentation sémantique Résumé : (auteur) The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection, and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks. Numéro de notice : A2021-697 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-021-01452-0 Date de publication en ligne : 05/05/2021 En ligne : https://doi.org/10.1007/s11263-021-01452-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98532 [article]

Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 259 - 286 Titre : Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur Année de publication : 2021 Article en page(s) : pp 259 - 286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle d'incidence [Termes IGN] apprentissage automatique [Termes IGN] bathymétrie laser [Termes IGN] classification barycentrique [Termes IGN] données lidar [Termes IGN] Extreme Gradient Machine [Termes IGN] Floride (Etats-Unis) [Termes IGN] lever bathymétrique [Termes IGN] profondeur [Termes IGN] semis de points Résumé : (auteur) To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML1) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space. Numéro de notice : A2021-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2021.1925790 Date de publication en ligne : 25/05/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97964 [article]

Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Apports de la télédétection des puits pastoraux à la cartographie des eaux souterraines du Sahel / Bernard Collignon in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)  

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Multi-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)  

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Deep-learning for 3D reconstruction / Fabio Tosi (2021)  

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A method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)  

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Shallow water bathymetry derived from green wavelength terrestrial laser scanner / Theodore Panagou in Marine geodesy, Vol 43 n° 5 (September 2020)  

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Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa / Priyadarshi Upadhyay in Geocarto international, vol 35 n° 9 ([01/07/2020])  

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Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations / Guanxu Chen in Journal of geodesy, vol 94 n° 6 (June 2020)  

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Learning stereo reconstruction with deep neural networks / Stepan Tulyakov (2020)  

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Un modèle spatio-temporel hybride de SIG temporel : application à la géomorphologie marine / Younes Hamdani (2020)  

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Time-lapse photogrammetry of distributed snow depth during snowmelt / Simon Filhol in Water resources research, vol 55 n° 9 (September 2019)  

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