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An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)
[article]
Titre : An improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur Année de publication : 2022 Article en page(s) : pp 260 - 284 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] fusion de données
[Termes IGN] Norvège
[Termes IGN] Ransac (algorithme)
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] toitRésumé : (auteur) Roof plane segmentation is an essential step in the process of 3D building reconstruction from airborne laser scanning (ALS) point clouds. The existing approaches either rely on human intervention to select the appropriate input parameters for different data-sets or they are not automatic and efficient. To tackle these issues, an improved multi-task pointwise network is proposed to simultaneously segment instances (that is, individual roof planes) and semantics (that is, groups of roof planes with similar geometric shapes) in point clouds. PointNet++ is used as a backbone network to extract robust features in the first step. The features from semantics branch are then added to the instance branch to facilitate the learning of instance embeddings. After that, a feature fusion module is added to the semantics branch to acquire more discriminative features from the backbone network. To increase the accuracy of semantic predictions, fused semantic features of the points belonging to the same instance are aggregated together. Finally, a mean-shift clustering algorithm is employed on instance embeddings to produce the instance predictions. Furthermore, a new roof data-set (called RoofNTNU) is established by taking ALS point clouds as training data for automatic and more general segmentation. Experiments on the new roof data-set show that the method achieves promising segmentation results: the mean precision (mPrec) of 96.2% for the instance segmentation task and mean accuracy (mAcc) of 94.4% for the semantic segmentation task. Numéro de notice : A2022-936 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12420 Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1111/phor.12420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102682
in Photogrammetric record > vol 37 n° 179 (September 2022) . - pp 260 - 284[article]Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])
[article]
Titre : Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 5433 - 5452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] image multibande
[Termes IGN] OpenStreetMap
[Termes IGN] pixel
[Termes IGN] plateforme collaborative
[Termes IGN] texture d'image
[Termes IGN] WorldviewRésumé : (auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved. Numéro de notice : A2022-702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1917006 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1917006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561
in Geocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452[article]Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)
[article]
Titre : Deep image deblurring: A survey Type de document : Article/Communication Auteurs : Kaihao Zhang, Auteur ; Wenqi Ren, Auteur ; Wenhan Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2103 - 2130 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déconvolution
[Termes IGN] estimation par noyau
[Termes IGN] filtrage du bruit
[Termes IGN] image floue
[Termes IGN] qualité d'image
[Termes IGN] réseau antagoniste génératif
[Termes IGN] taxinomie
[Termes IGN] vision par ordinateurRésumé : (auteur) Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions. Numéro de notice : A2022-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01633-5 Date de publication en ligne : 25/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01633-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101444
in International journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2103 - 2130[article]Deep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)
[article]
Titre : Deep learning method for Chinese multisource point of interest matching Type de document : Article/Communication Auteurs : Pengpeng Li, Auteur ; Jiping Liu, Auteur ; An Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage profond
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] inférence sémantique
[Termes IGN] information sémantique
[Termes IGN] point d'intérêt
[Termes IGN] représentation vectorielle
[Termes IGN] traitement du langage naturelRésumé : (auteur) Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods. Numéro de notice : A2022-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101821 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101053
in Computers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101821[article]Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)
[article]
Titre : Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach Type de document : Article/Communication Auteurs : Quoc Bao Pham, Auteur ; Sk Ajim Ali, Auteur ; Elzbieta Bielecka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1081 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] prévention des risques
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] Varsovie (Pologne)
[Termes IGN] vulnérabilité
[Termes IGN] zone urbaine denseRésumé : (auteur) Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management. Numéro de notice : A2022-705 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-022-05336-5 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1007/s11069-022-05336-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101569
in Natural Hazards > vol 113 n° 2 (September 2022) . - pp 1043 - 1081[article]Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms / Yunhao Li in Transactions in GIS, vol 26 n° 6 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)Permalink3D building reconstruction from single street view images using deep learning / Hui En Pang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)Permalink3D semantic scene completion: A survey / Luis Roldão in International journal of computer vision, vol 130 n° 8 (August 2022)PermalinkDeep learning feature representation for image matching under large viewpoint and viewing direction change / Lin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkFiltering airborne LIDAR data by using fully convolutional networks / Abdullah Varlik in Survey review, vol 55 n° 388 (January 2023)PermalinkFull-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR / Xue Ji in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkGenerating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes / Christian Kruse in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)PermalinkHyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkMapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)Permalink