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The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland / Ranjith Gopalakrishnan in Annals of Forest Science, vol 77 n° 4 (December 2020)
[article]
Titre : The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland Type de document : Article/Communication Auteurs : Ranjith Gopalakrishnan, Auteur ; Petteri Packalen, Auteur ; Veli-Pekka Ikonen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cartographie des risques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] forêt
[Termes IGN] forêt boréale
[Termes IGN] image multibande
[Termes IGN] modèle de simulation
[Termes IGN] risque naturel
[Termes IGN] tempête
[Termes IGN] vent
[Termes IGN] zone à risqueRésumé : (auteur) Key message: The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage.
Context: Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations.
Aims: (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects?
Methods: We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area.
Results: Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement.
Conclusion: Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.Numéro de notice : A2020-629 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00992-8 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00992-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96045
in Annals of Forest Science > vol 77 n° 4 (December 2020) . - 18 p.[article]Understanding the role of individual units in a deep neural network / David Bau in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 48 (1 December 2020)
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Titre : Understanding the role of individual units in a deep neural network Type de document : Article/Communication Auteurs : David Bau, Auteur ; Jun-Yan Zhu, Auteur ; Hendrik Strobelt, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 30071-30078 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cadre conceptuel
[Termes IGN] détection d'objet
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scèneRésumé : (auteur) Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. Numéro de notice : A2020-864 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1073/pnas.1907375117 En ligne : https://doi.org/10.1073/pnas.1907375117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99086
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 48 (1 December 2020) . - n° 30071-30078[article]Unsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
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Titre : Unsupervised deep joint segmentation of multitemporal high-resolution images Type de document : Article/Communication Auteurs : Sudipan Saha, Auteur ; Lichao Mou, Auteur ; Chunping Qiu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8780 - 8792 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image multitemporelle
[Termes IGN] itération
[Termes IGN] segmentation sémantiqueRésumé : (auteur) High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth’s surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento. Numéro de notice : A2020-744 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2990640 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2990640 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96375
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8780 - 8792[article]Visualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)
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Titre : Visualization of 3D property data and assessment of the impact of rendering attributes Type de document : Article/Communication Auteurs : Stefan Seipel, Auteur ; Martin Andrée, Auteur ; Karolina Larsson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] attribut non spatial
[Termes IGN] cadastre 3D
[Termes IGN] cadastre étranger
[Termes IGN] classification barycentrique
[Termes IGN] couleur (rédaction cartographique)
[Termes IGN] mesure de similitude
[Termes IGN] propriété foncière
[Termes IGN] rédaction cartographique
[Termes IGN] rendu (géovisualisation)
[Termes IGN] saillance
[Termes IGN] scène 3D
[Termes IGN] Stockholm (Suède)
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Visualizations of 3D cadastral information incorporating both intrinsically spatial and non-spatial information are examined here. The design of a visualization prototype is linked to real-case 3D property information. In an interview with domain experts, the functional and visual features of the prototype are assessed. The choice of rendering attributes was identified as an important aspect for further analysis. A computational approach to systematic assessment of the consequences of different graphical design choices is proposed. This approach incorporates a colour similarity metric, visual saliency maps, and k-nearest-neighbour (kNN) classification to estimate risks of confusing or overlooking relevant elements in a visualization. The results indicate that transparency is not an independent visual variable, as it affects the apparent colour of 3D objects and makes them inherently more difficult to distinguish. Transparency also influences visual saliency of objects in a scene. The proposed analytic approach was useful for visualization design and revealed that the conscious use of graphical attributes, like combinations of colour, transparency, and line styles, can improve saliency of objects in a 3D scene. Numéro de notice : A2020-796 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-020-00063-6 Date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s41651-020-00063-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96612
in Journal of Geovisualization and Spatial Analysis > vol 4 n° 2 (December 2020) . - n° 23[article]Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)
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Titre : Bretagne, la végétation cartographiée Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2020 Article en page(s) : pp 46 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] 1:25.000
[Termes IGN] acquisition d'images
[Termes IGN] aménagement régional
[Termes IGN] ArcGIS
[Termes IGN] BD ortho
[Termes IGN] Bretagne
[Termes IGN] carte de la végétation
[Termes IGN] classification orientée objet
[Termes IGN] données localisées
[Termes IGN] données publiques
[Termes IGN] IGN cité
[Termes IGN] image infrarouge couleur
[Termes IGN] image proche infrarouge
[Termes IGN] modèle orienté objet
[Termes IGN] phytoécologieRésumé : (Auteur) Une cartographie inédite de la végétation de Bretagne sera accessible en totalité en ligne en décembre. Produite par télédétection grâce à une méthode semi-automatisée innovante, elle répond aux nouveaux besoins des acteurs de la biodiversité et de l'aménagement du territoire. Numéro de notice : A2020-707 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96281
in Géomètre > n° 2185 (novembre 2020) . - pp 46 - 49[article]Réservation
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