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Automatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments / Daniela Craciun (2022)
Titre : Automatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments Type de document : Article/Communication Auteurs : Daniela Craciun , Auteur ; Arnaud Le Bris , 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 Projets : HIATUS / Giordano, Sébastien Conférence : ISPRS 2022, Commission 2, 24th ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 21 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] estimation de pose
[Termes IGN] géoréférencement
[Termes IGN] gradient
[Termes IGN] histogramme
[Termes IGN] image ancienne
[Termes IGN] milieu naturel
[Termes IGN] modèle numérique de surfaceRésumé : (auteur) Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of-the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to-nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span). Numéro de notice : C2022-020 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2022-21-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2022-21-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100846 CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica, vol 26 n° 1 (January 2022)
[article]
Titre : CIME: Context-aware geolocation of emergency-related posts Type de document : Article/Communication Auteurs : Gabriele Scalia, Auteur ; Chiara Francalanci, Auteur ; Barbara Pernici, Auteur Année de publication : 2022 Article en page(s) : pp 125 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] cartographie d'urgence
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de données
[Termes IGN] géolocalisation
[Termes IGN] géoréférencement
[Termes IGN] Grande-Bretagne
[Termes IGN] implémentation (informatique)
[Termes IGN] inondation
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] prise en compte du contexte
[Termes IGN] tempête
[Termes IGN] TwitterRésumé : (auteur) Information extracted from social media has proven to be very useful in the domain of emergency management. An important task in emergency management is rapid crisis mapping, which aims to produce timely and reliable maps of affected areas. During an emergency, the volume of emergency-related posts is typically large, but only a small fraction is relevant and help rapid mapping effectively. Furthermore, posts are not useful for mapping purposes unless they are correctly geolocated and, on average, less than 2% of posts are natively georeferenced. This paper presents an algorithm, called CIME, that aims to identify and geolocate emergency-related posts that are relevant for mapping purposes. While native geocoordinates are most often missing, many posts contain geographical references in their metadata, such as texts or links that can be used by CIME to filter and geolocate information. In addition, social media creates a social network and each post can be enhanced with indirect information from the post’s network of relationships with other posts (for example, a retweet can be associated with other geographical references which are useful to geolocate the original tweet). To exploit all this information, CIME uses the concept of context, defined as the information characterizing a post both directly (the post’s metadata) and indirectly (the post’s network of relationships). The algorithm was evaluated on a recent major emergency event demonstrating better performance with respect to the state of the art in terms of total number of geolocated posts, geolocation accuracy and relevance for rapid mapping. Numéro de notice : A2022-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00446-x Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1007/s10707-021-00446-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100011
in Geoinformatica > vol 26 n° 1 (January 2022) . - pp 125 - 157[article]Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)
[article]
Titre : Classification of mediterranean shrub species from UAV point clouds Type de document : Article/Communication Auteurs : Juan Pedro Carbonell-Rivera, Auteur ; Jesus Torralba, Auteur ; Javier Estornell, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] arbuste
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] Espagne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] forêt méditerranéenne
[Termes IGN] image captée par drone
[Termes IGN] incendie de forêt
[Termes IGN] indice de végétation
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de terrain
[Termes IGN] parc naturel
[Termes IGN] photogrammétrie aérienne
[Termes IGN] semis de pointsRésumé : (auteur) Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models. Numéro de notice : A2022-057 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14010199 En ligne : https://doi.org/10.3390/rs14010199 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99462
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 199[article]Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)
Titre : Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery Type de document : Thèse/HDR Auteurs : Calimanut-Ionut Cira, Auteur Editeur : Madrid [Espagne] : Universidad politécnica de Madrid Année de publication : 2022 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat en Topographie, Géodésie et cartographie, Universidad politécnica de MadridLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] route
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. Remote sensing experts have been actively using deep neural networks to solve object extraction tasks in high-resolution aerial imagery by means of supervised operations. However, the extraction operation is imperfect, due to the nature of remotely sensed images (noise, obstructions, etc.), the limitations of sensing resolution, or the occlusions often present in the scenes. The road network plays an important part in transportation and, nowadays, one of the main related challenges is keeping the existent cartographic support up to date. This task can be considered very challenging due to the complex nature of the geospatial object (continuous, with irregular geometry, and significant differences in width). We also need to take into account that secondary roads represent the largest part of the road transport network, but due to the absence of clearly defined edges, and the different spectral signatures of the materials used for pavement, monitoring, and mapping them represents a great effort for public administration, and their extraction is often omitted altogether. We believe that recent advancements in machine vision can enable a successful extraction of the road structures from high-resolution, remotely sensed imagery and a greater automation of the road mapping operation. In this PhD thesis, we leverage recent computer vision advances and propose a deep learning-based end-to-end solution, capable of efficiently extracting the surface area of roads at a large scale. The novel approach is based on a disjoint execution of three different image processing operations (recognition, semantic segmentation, and post-processing with conditional generative learning) within a common framework. We focused on improving the state-of-the-art results for each of the mentioned components, before incorporating the resulting models into the proposed solution architecture. For the recognition operation, we proposed two framework candidates based on convolutional neural networks to classify roads in openly available aerial orthoimages divided in tiles of 256×256 pixels, with a spatial resolution of 0.5 m. The frameworks are based on ensemble learning and transfer learning and combine weak classifiers to leverage the strengths of different state-of-the-art models that we heavily modified for computational efficiency. We evaluated their performance on unseen test data and compared the results with those obtained by the state-of-the-art convolutional neural networks trained for the same task, observing improvements in performance metrics of 2-3%. Secondly, we implemented hybrid semantic segmentation models (where the default backbones are replaced by neural network specialised in image segmentation) and trained them with high-resolution remote sensing imagery and their correspondent ground-truth masks. Our models achieved mean increases in performance metrics of 2.7-3.5%, when compared to the original state-of-the-art semantic segmentation architectures trained from scratch for the same task. The best-performing model was integrated on a web platform that handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format, and the generation of GeoTIFF results (compatible with geospatial databases). Thirdly, the road surface area extraction task is generally carried out via semantic segmentation over remotely sensed imagery—however, this supervised learning task can be considered very costly because it requires remote sensing images labelled at pixel level and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). We consider that unsupervised learning (not requiring labelled data) can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. For this reason, we also approached the post-processing of the road surface areas obtained with the best performing segmentation model to improve the initial segmentation predictions. In this line, we proposed two post-processing operations based on conditional generative learning for deep inpainting and image-to-image translation operations and trained the networks to learn the distribution of the road network present in official cartography, using a novel dataset covering representative areas of Spain. The first proposed conditional Generative Adversarial Network (cGAN) model was trained for deep inpainting operation and obtained improvements in performance metrics of maximum 1.3%. The second cGAN model was trained for image-to-image translation, is based on a popular model heavily modified for computational efficiency (a 92.4% decrease in the number of parameters in the generator network and a 61.3% decrease in the discriminator network), and achieved a maximum increase of 11.6% in performance metrics. We also conducted a qualitative comparison to visually assess the effectiveness of the generative operations and observed great improvements with respect to the initial semantic segmentation predictions. Lastly, we proposed an end-to-end processing strategy that combines image classification, semantic segmentation, and post-processing operations to extract containing road surface area extraction from high-resolution aerial orthophotography. The training of the model components was carried out on a large-scale dataset containing more than 537,500 tiles, covering approximately 20,800 km2 of the Spanish territory, manually tagged at pixel level. The consecutive execution of the resulting deep learning models delivered higher quality results when compared to state-of-the-art implementations trained for the same task. The versatility and flexibility of the solution given by the disjointed execution of the three separate sub-operations proved its effectiveness and economic efficiency and enables the integration of a web application that alleviates the manipulation of geospatial data, while allowing for an easy integration of future models and algorithms. Resuming, applying the proposed models resulted from this PhD thesis translates to operations aimed to check if the latest existing aerial orthoimages contains the studied continuous geospatial element, to obtain an approximation of its surface area using supervised learning and to improve the initial segmentation results with post-processing methods based on conditional generative learning. The results obtained with the proposed end-to-end-solution presented in this PhD thesis improve the state-of-the-art in the field of road extraction with deep learning techniques and prove the appropriateness of applying the proposed extraction workflow for a more robust and more efficient extraction operation of the road transport network. We strongly believe that the processing strategy can be applied to enhance other similar extraction tasks of continuous geospatial elements (such as the mapping of riverbeds, or railroads), or serve as a base for developing additional extraction workflows of geospatial objects from remote sensing images. Note de contenu : 1- Introduction
2- Methodology
3- Theoretical framework
4- Litterature review
5- Road recognition: A framework based on nestion of convolutional neuronal networks and transfer learning to regognise road elements
6- Road segmentation: An approach based on hybrid semantic segmentation models to extract the surface area of rod elements from aerial orthoimagery
7- Post-processing of semantic segmentation predictions I: A conditional generative adversial network to improve the extraction of road surface areas via deep inpainting operations
8- Post-processing of semantic segmentation predictions II: A lightweight conditional generative adversial network to improve the extraction of road surface areas via image-to-image translation
9- An end-to-end road extraction solution based on regonition, segmentation, and post-processing operations for a large-scale mapping of the road transport network from aerial orthophotography
10- ConclusionsNuméro de notice : 24069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Topographie, Géodésie et cartographie : Universidad politécnica de Madrid : 2022 DOI : 10.20868/UPM.thesis.70152 En ligne : https://doi.org/10.20868/UPM.thesis.70152 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102113
Titre : Deep learning based 3D reconstruction: supervision and representation Type de document : Thèse/HDR Auteurs : François Darmon, Auteur ; Pascal Monasse, Directeur de thèse ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 115 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l'Ecole des Ponts ParisTech, spécialité informatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] carte de profondeur
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction
[Termes IGN] géométrie épipolaire
[Termes IGN] maillage
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'intérêt
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motion
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) 3D reconstruction is a long standing problem in computer vision. Yet, state-of-the-art methods still struggle when the images used have large illumination changes, many occlusions or limited textures. Deep Learning holds promises of improving 3D reconstruction in such setups, but classical methods still produce the best results. In this thesis we analyse the specificity of deep learning applied to multiview 3D reconstruction and introduce new deep learning based methods.The first contribution of this thesis is an analysis of the possible supervision for training Deep Learning models for sparse image matching. We introduce a two-step algorithm that first computes low resolution matches using deep learning and then matches classical local features inside the matches regions. We analyze several levels of supervision and show that our new epipolar supervision leads to the best results.The second contribution is also a study of supervision for Deep Learning but applied to another scenario: calibrated 3D reconstruction in the wild. We show that existing unsupervised methods do not work on such data and we introduce a new training technique that solves this issue. We then exhaustively compare unsupervised approach and supervised approaches with different network architectures and training data.Finally, our third contribution is about data representation. Neural implicit representation were recently used for image rendering. We adapt this representation to the multiview reconstruction problem and we introduce a new method that, similar to classical 3D reconstruction techniques, optimizes photo-consistency between projections of multiple images. Our approach outperforms state-of-the-art by a large margin. Note de contenu : 1- Introduction
2- Background
3- Deep learning for guiding keypoint matching
4- Deep Learning based Multi-View Stereo in the wild
5- Multi-view reconstruction with implicit surfaces and patch warping
6- ConclusionNuméro de notice : 24085 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Ponts ParisTech : 2022 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge LIGM DOI : sans En ligne : https://www.theses.fr/2022ENPC0024 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102473 PermalinkDetection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks / Stefan Reder in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkDéveloppement d’outils et de méthodes permettant l’acquisition, le traitement et la diffusion de données issues de levés par drone / Guillaume Feuillatre (2022)PermalinkA GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkGlobal glacier mass change by spatiotemporal analysis of digital elevation models / Romain Hugonnet (2022)PermalinkHistorical 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)PermalinkLandslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)PermalinkLevé et numérisation du château de Lichtenberg en vue d’une proposition de visite virtuelle du site à des périodes remarquables / Maxime Rocha (2022)PermalinkPermalinkModalités et rythmes d'évolution des falaises des Vaches Noires (Normandie, France) : caractérisation et quantification des dynamiques hydrogravitaires par approches multi-scalaires / Thomas Roulland (2022)Permalink