Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques > détection de contours > détection du bâti
détection du bâtiSynonyme(s)extraction du bâtiVoir aussi |
Documents disponibles dans cette catégorie (205)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
[article]
Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] densité des points
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] données localisées 3D
[Termes IGN] extraction de données
[Termes IGN] filtrage de points
[Termes IGN] mur
[Termes IGN] séisme
[Termes IGN] semis de pointsRésumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022021 SL Revue Centre de documentation Revues en salle Disponible Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)
[article]
Titre : Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi Type de document : Article/Communication Auteurs : Yafei Jing, Auteur ; Yuhuan Ren, Auteur ; Yalan Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] extraction automatique
[Termes IGN] image captée par drone
[Termes IGN] orthoimage
[Termes IGN] séisme
[Termes IGN] Yunnan (Chine)Résumé : (auteur) Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable. Numéro de notice : A2022-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14020382 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.3390/rs14020382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99577
in Remote sensing > vol 14 n° 2 (January-2 2022) . - n° 382[article]Development of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)
Titre : Development of object detectors for satellite images by deep learning Type de document : Mémoire Auteurs : Alissa Kouraeva, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 57 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire d'ingénieur 3e année, Cycle PPMD Photogrammétrie, Positionnement et Mesure de DéformationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] angle d'incidence
[Termes IGN] apprentissage profond
[Termes IGN] détection du bâti
[Termes IGN] image aérienne
[Termes IGN] image Pléiades-HR
[Termes IGN] image Pléiades-Neo
[Termes IGN] jeu de données
[Termes IGN] OpenStreetMap
[Termes IGN] réalité de terrain
[Termes IGN] recalage d'imageMots-clés libres : Frame Field Learning algorithm Index. décimale : MPPMD Mémoires du mastère spécialisé Photogrammétrie, Positionnement et Mesures de Déformation Résumé : (auteur) With various uses cases in different sectors - marine, cartography, defense - object detection in satellite images is at the heart of image processing methods. This study aims to test existing building detection algorithms and improve them with the final goal being a precise cartography of buildings for 3D reconstruction with a high level of details. The Polygonization by Frame Field Learning algorithm is tested on different types of images: aerial images (50cm resolution), satellite images with 50cm (Pleiades) and 30cm (Pleiades Neo) resolutions. The ground truth is either already provided (Digitanie) or has to be retrieved from open access databases (OSM or BD TOPO IGN). Some problems of ground truth overlap appear in Pleiades neo images due to the relative precision in positioning of different data and also due to the incidence angle, that provides a greater revisiting capability. A re-implementation of the Frame Field Learning algorithm with the PyTorch Lightning framework is done in this study, with different experiments conducted concerning the configuration of the algorithm. Note de contenu : Introduction
1- Data
2- Methods
3- Results and discussion
ConclusionNuméro de notice : 24052 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Airbus Defence and Space Geo SA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101926
Titre : EuroSDR annual report 2021 Type de document : Rapport Auteurs : European Spatial Data Research EuroSDR, Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR annual report num. 2021 Importance : 42 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte d'occupation du sol
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3DNuméro de notice : 17729 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Rapport d'activité DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_annual_report_ [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100480 Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)
[article]
Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification automatique d'objets
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données cadastrales
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] orthoimage couleur
[Termes IGN] segmentation d'image
[Termes IGN] SlovénieRésumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409
in Geodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 139-2021041 RAB Revue Centre de documentation En réserve L003 Disponible MSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)PermalinkThe use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkA deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkA novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkAutomatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkThree-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkReview of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)PermalinkDomain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)Permalink