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Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)
[article]
Titre : Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation Type de document : Article/Communication Auteurs : Zhimin Yuan, Auteur ; Ming Cheng, Auteur ; Wankang Zeng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5700613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement des données
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compression de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets. Numéro de notice : A2023-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234542 Date de publication en ligne : 05/01/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3234542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102489
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5700613[article]PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)
Titre : PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 6526 - 6535 Note générale : bibliographie
voir aussi https://openaccess.thecvf.com/content/CVPR2023W/PCV/supplemental/Wu_PSMNet-FusionX3_LiDAR-Guided_Deep_CVPRW_2023_supplemental.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne à axe vertical
[Termes IGN] scène 3D
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Dense image matching (DIM) and LiDAR are two complementary techniques for recovering the 3D geometry of real scenes. While DIM provides dense surfaces, they are often noisy and contaminated with outliers. Conversely, LiDAR is more accurate and robust, but less dense and more expensive compared to DIM. In this work, we investigate learning-based methods to refine surfaces produced by photogrammetry with sparse LiDAR point clouds. Unlike the current state-of-the-art approaches in the computer vision community, our focus is on aerial acquisitions typical in photogrammetry. We propose a densification pipeline that adopts a PSMNet backbone with triangulated irregular network interpolation based expansion, feature enhancement in cost volume, and conditional cost volume normalization, i.e. PSMNet-FusionX3. Our method works better on low density and is less sensitive to distribution, demonstrating its effectiveness across a range of LiDAR point cloud densities and distributions, including analyses of dataset shifts. Furthermore, we have made both our aerial (image and disparity) dataset and code available for public use. Further information can be found at https://github.com/ whuwuteng/PSMNet-FusionX3. Numéro de notice : C2023-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/PCV/papers/Wu_PSMNet-FusionX3_Li [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103277 Des relevés sur mesure pour la sentinelle des Pyrénées / Marielle Mayo in Géomètre, n° 2209 (janvier 2023)
[article]
Titre : Des relevés sur mesure pour la sentinelle des Pyrénées Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2023 Article en page(s) : pp 14 - 16 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cartographie 3D
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] infiltration
[Termes IGN] ouvrage d'art
[Termes IGN] Pyrénées-orientales (66)
[Termes IGN] surveillance d'ouvrageRésumé : (Auteur) A Villefranche-de-Conflent, une mission de diagnostic s’est appuyée sur les relevés d’un cabinet de géomètres-experts pour repérer les dégradions liées aux infiltrations d’eau subies par les fortifications de Vauban. Les restaurations vont pouvoir commencer... Numéro de notice : A2023-061 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/01/2023 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102353
in Géomètre > n° 2209 (janvier 2023) . - pp 14 - 16[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2023011 RAB Revue Centre de documentation En réserve L003 Disponible A survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)
Titre : A survey and benchmark of automatic surface reconstruction from point clouds Type de document : Article/Communication Auteurs : Raphaël Sulzer , Auteur ; Loïc Landrieu , Auteur ; Renaud Marlet, Auteur ; Bruno Vallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : BIOM / Vallet, Bruno Importance : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] benchmark spatial
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de pointsRésumé : (auteur) We survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and missing data. Traditionally, different handcrafted priors of the input points or the output surface have been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting priors to different acquisition defects can be a tedious task. To this end, the deep learning community has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep surface reconstruction methods can learn priors directly from a training set of point clouds and corresponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the robustness of methods to defect-laden input and their capability to generate geometric and topologically accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and learning-based methods on the same grounds. We show that learning-based methods can generalize to unseen shape categories, but their training and test sets must share the same point cloud characteristics. We also provide the code and data to compete in our benchmark and to further stimulate the development of learning-based surface reconstruction: https://github.com/raphaelsulzer/dsr-benchmark. Numéro de notice : P2023-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2301.13656 Date de publication en ligne : 31/01/2023 En ligne : https://hal.science/hal-03968453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102847 The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
[article]
Titre : The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Zhenfeng Shao, Auteur ; Akib Javed, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] automate cellulaire
[Termes IGN] carte thématique
[Termes IGN] classification semi-dirigée
[Termes IGN] détection de changement
[Termes IGN] données vectorielles
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] MNS SRTM
[Termes IGN] modèle dynamique
[Termes IGN] occupation du sol
[Termes IGN] Pakistan
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation. Numéro de notice : A2023-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.14358/PERS.22-00102R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.22-00102R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102357
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp 47 - 55[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2023011 SL Revue Centre de documentation Revues en salle Disponible Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkTree position estimation from TLS data using hough transform and robust least-squares circle fitting / Maja Michałowska in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkUAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)PermalinkAssessment of camera focal length influence on canopy reconstruction quality / Martin Denter in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkDecadal surface changes and displacements in Switzerland / Valentin Tertius Bickel in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)PermalinkHarvested area did not increase abruptly-how advancements in satellite-based mapping led to erroneous conclusions / Johannes Breidenbach in Annals of Forest Science, vol 79 n° 1 (2022)PermalinkModelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)PermalinkA novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkRelevé 2D & 3D du marégraphe de Marseille / Emmanuel Clédat in XYZ, n° 173 (décembre 2022)PermalinkSemantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)PermalinkSemantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkA semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkTesting of a new way of cadastral maps renewal in Slovakia / Peter Kyseľ in Geodetski vestnik, vol 66 n° 4 (December 2022 - February 2023)PermalinkA unified framework for automated registration of point clouds, mesh surfaces and 3D models by using planar surfaces / Yuan Zhao in Photogrammetric record, vol 37 n° 180 (December 2022)PermalinkVine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)PermalinkEvaluation of softwood timber quality: A case study on two silvicultural systems in Central Germany / Kristen Höwler in Forests, vol 13 n° 11 (November 2022)Permalink