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Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)
[article]
Titre : Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors Titre original : Assimilation de données de volume de bois à l’aide d’une séquence de données de télédétection provenant de différents capteurs Type de document : Article/Communication Auteurs : Niels Lindgren, Auteur ; Hakan Olsson, Auteur ; Kenneth Nyström, Auteur ; Mattias Nyström, Auteur ; Göran Stahl, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Betula (genre)
[Termes IGN] capital sur pied
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage des données
[Termes IGN] filtre de Kalman
[Termes IGN] forêt boréale
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Quercus (genre)
[Termes IGN] Suède
[Termes IGN] volume en boisRésumé : (auteur) Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58°27′N, 13°39′E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE). Numéro de notice : A2022-144 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/07038992.2021.1988542 Date de publication en ligne : 17/10/2021 En ligne : https://doi.org/10.1080/07038992.2021.1988542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99985
in Canadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp[article]Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)
[article]
Titre : Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights Type de document : Article/Communication Auteurs : Marco Fiorucci, Auteur ; Wouter Baernd Verschoof-van der Vaart, Auteur ; Paolo Soleni, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] site archéologiqueRésumé : (auteur) Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools. Numéro de notice : A2022-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14071694 En ligne : https://doi.org/10.3390/rs14071694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100298
in Remote sensing > vol 14 n° 7 (April-1 2022) . - n° 1694[article]Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)
[article]
Titre : Determination of building flood risk maps from LiDAR mobile mapping data Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Qing Xiao, Auteur ; Claus Brenner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] cartographie d'urgence
[Termes IGN] cartographie des risques
[Termes IGN] classification semi-dirigée
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] infiltration
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] segmentation sémantiqueRésumé : (auteur) With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner. This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations.
In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F1-score. After five hours of human supervision, it is possible to improve the model by another 6.2%. By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping.Numéro de notice : A2022-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101759 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99964
in Computers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101759[article]Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models / Ana de Lera Garrido in Silva fennica, vol 56 n° 2 (April 2022)
[article]
Titre : Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models Type de document : Article/Communication Auteurs : Ana de Lera Garrido, Auteur ; Terje Gobakken, Auteur ; Hans Ole Ørka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 10695 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] Norvège
[Termes IGN] parcelle forestière
[Termes IGN] placette d'échantillonnage
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory. Numéro de notice : A2022-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10695 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.14214/sf.10695 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100589
in Silva fennica > vol 56 n° 2 (April 2022) . - n° 10695[article]GeoRec: Geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes / Linxi Huan in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)
[article]
Titre : GeoRec: Geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes Type de document : Article/Communication Auteurs : Linxi Huan, Auteur ; Xianwei Zheng, Auteur ; Jianya Gong, Auteur Année de publication : 2022 Article en page(s) : pp 301 - 314 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] données localisées 3D
[Termes IGN] géométrie
[Termes IGN] image RVB
[Termes IGN] maillage
[Termes IGN] modélisation sémantique
[Termes IGN] objet 3D
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] scène intérieureRésumé : (auteur) Semantic indoor 3D modeling with multi-task deep neural networks is an efficient and low-cost way for reconstructing an indoor scene with geometrically complete room structure and semantic 3D individuals. Challenged by the complexity and clutter of indoor scenarios, the semantic reconstruction quality of current methods is still limited by the insufficient exploration and learning of 3D geometry information. To this end, this paper proposes an end-to-end multi-task neural network for geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes (termed as GeoRec). In the proposed GeoRec, we build a geometry extractor that can effectively learn geometry-enhanced feature representation from depth data, to improve the estimation accuracy of layout, camera pose and 3D object bounding boxes. We also introduce a novel object mesh generator that strengthens the reconstruction robustness of GeoRec to indoor occlusion with geometry-enhanced implicit shape embedding. With the parsed scene semantics and geometries, the proposed GeoRec reconstructs an indoor scene by placing reconstructed object mesh models with 3D object detection results in the estimated layout cuboid. Extensive experiments conducted on two benchmark datasets show that the proposed GeoRec yields outstanding performance with mean chamfer distance error for object reconstruction on the challenging Pix3D dataset, 70.45% mAP for 3D object detection and 77.1% 3D mIoU for layout estimation on the commonly-used SUN RGB-D dataset. Especially, the mesh reconstruction sub-network of GeoRec trained on Pix3D can be directly transferred to SUN RGB-D without any fine-tuning, manifesting a high generalization ability. Numéro de notice : A2022-235 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2022.02.014 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.02.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100139
in ISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 301 - 314[article]Réservation
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