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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)
[article]
Titre : Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms Type de document : Article/Communication Auteurs : Yunhao Li, Auteur ; Chunxiao Zhang, Auteur ; Chang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2440 - 2454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] image virtuelle
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] paysage urbain
[Termes IGN] segmentation d'image
[Termes IGN] vision
[Termes IGN] vision par ordinateurRésumé : (auteur) Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people. Numéro de notice : A2022-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/tgis.12882 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1111/tgis.12882 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101698
in Transactions in GIS > vol 26 n° 6 (September 2022) . - pp 2440 - 2454[article]Learning indoor point cloud semantic segmentation from image-level labels / Youcheng Song in The Visual Computer, vol 38 n° 9 (September 2022)
[article]
Titre : Learning indoor point cloud semantic segmentation from image-level labels Type de document : Article/Communication Auteurs : Youcheng Song, Auteur ; Zhengxing Sun, Auteur ; Qian Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3253 - 3265 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] apprentissage dirigé
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] image RVB
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) The data-hungry nature of deep learning and the high cost of annotating point-level labels make it difficult to apply semantic segmentation methods to indoor point cloud scenes. Therefore, exploring how to make point cloud segmentation methods less rely on point-level labels is a promising research topic. In this paper, we introduce a weakly supervised framework for semantic segmentation on indoor point clouds. To reduce the labor cost in data annotation, we use image-level weak labels that only indicate the classes that appeared in the rendered images of point clouds. The experiments validate the effectiveness and scalability of our framework. Our segmentation results on both ScanNet and S3DIS datasets outperform the state-of-the-art method using a similar level of weak supervision. Numéro de notice : A2022-793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-022-02569-0 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1007/s00371-022-02569-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101917
in The Visual Computer > vol 38 n° 9 (September 2022) . - pp 3253 - 3265[article]Mapping 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)
[article]
Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage profond
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] croissance urbaine
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de régression
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049
in Remote sensing of environment > vol 278 (September 2022) . - n° 113088[article]Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)
[article]
Titre : Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery Type de document : Article/Communication Auteurs : Shengyuan Zou, Auteur ; Le Wang, Auteur Année de publication : 2022 Article en page(s) : n° 103018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'objet
[Termes IGN] image à très haute résolution
[Termes IGN] image Streetview
[Termes IGN] logement
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] zone urbaineRésumé : (auteur) Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges. Numéro de notice : A2022-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103018 Date de publication en ligne : 18/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101894
in International journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 103018[article]PKS: A photogrammetric key-frame selection method for visual-inertial systems built on ORB-SLAM3 / Arash Azimi in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)
[article]
Titre : PKS: A photogrammetric key-frame selection method for visual-inertial systems built on ORB-SLAM3 Type de document : Article/Communication Auteurs : Arash Azimi, Auteur ; Ali Hosseininaveh Ahmadabadian, Auteur ; Fabio Remondino, Auteur Année de publication : 2022 Article en page(s) : pp 18 - 32 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] alignement
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] centre de gravité
[Termes IGN] déformation d'image
[Termes IGN] géoréférencement direct
[Termes IGN] méthode heuristique
[Termes IGN] semis de points
[Termes IGN] seuillage d'image
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) Key-frame selection methods were developed in the past years to reduce the complexity of frame processing in visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms. Key-frames help increasing algorithm's performances by sparsifying frames while maintaining its accuracy and robustness. Unlike current selection methods that rely on many heuristic thresholds to decide which key-frame should be selected, this paper proposes a photogrammetric-based key-frame selection method built upon ORB-SLAM3. The proposed algorithm, named Photogrammetric Key-frame Selection (PKS), replaces static heuristic thresholds with photogrammetric principles, ensuring algorithm’s robustness and better point cloud quality. A key-frame is chosen based on adaptive thresholds and the Equilibrium Of Center Of Gravity (ECOG) criteria as well as Inertial Measurement Unit (IMU) observations. To evaluate the proposed PKS method, the European Robotics Challenge (EuRoC) and an in-house datasets are used. Quantitative and qualitative evaluations are made by comparing trajectories, point clouds quality and completeness and Absolute Trajectory Error (ATE) in mono-inertial and stereo-inertial modes. Moreover, for the generated dense point clouds, extensive evaluations, including plane-fitting error, model deformation, model alignment error, and model density and quality, are performed. The results show that the proposed algorithm improves ORB-SLAM3 positioning accuracy by 18% in stereo-inertial mode and 20% in mono-inertial mode without the use of heuristic thresholds, as well as producing a more complete and accurate point cloud up to 50%. The open-source code of the presented method is available at https://github.com/arashazimi0032/PKS. Numéro de notice : A2022-664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.07.003 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101525
in ISPRS Journal of photogrammetry and remote sensing > vol 191 (September 2022) . - pp 18 - 32[article]Point-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)PermalinkStructured binary neural networks for image recognition / Bohan Zhuang in International journal of computer vision, vol 130 n° 9 (September 2022)PermalinkDetection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])PermalinkComparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs / Douglas Stefanello Facco in Geocarto international, vol 37 n° 16 ([15/08/2022])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)PermalinkChange detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach / Joachim Gehrung in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (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)PermalinkEffective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol 81 n° 20 (August 2022)PermalinkHyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkIdentification of urban agglomeration spatial range based on social and remote-sensing data - For evaluating development level of urban agglomerations / Shuai Zhang in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)Permalink