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Generating 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)
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[article]
Titre : Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes Type de document : Article/Communication Auteurs : Christian Kruse, Auteur ; Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du recuit simulé
[Termes IGN] chevauchement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] échantillonnage de données
[Termes IGN] Europe centrale
[Termes IGN] guerre
[Termes IGN] image aérienne
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] processus ponctuel marqué
[Termes IGN] processus stochastiqueRésumé : (auteur) Even more than 75 years after the Second World War, numerous unexploded bombs (duds) linger in the ground and pose a considerable hazard to society. The areas containing these duds are documented in so-called impact maps, which are based on locations of exploded bombs; these locations can be found in aerial images taken shortly after bombing. To generate impact maps, in this paper we present a novel approach based on marked point processes (MPPs) for the automatic detection of bomb craters in such images, some of which are overlapping. The object model for the craters is represented by circles and is embedded in the MPP-framework. By means of stochastic sampling, the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function that describes the consistency with a predefined object model. High gradient magnitudes along the object borders and homogeneous grey values inside the objects are favoured, while overlaps between objects are penalized. Reversible Jump Markov Chain Monte Carlo sampling, in combination with simulated annealing, provides the global optimum of the energy function. Our procedure allows the combination of individual detection results covering the same location. Afterwards, a probability map for duds is generated from the detections via kernel density estimation and areas around the detections are classified as contaminated, resulting in an impact map. Our results, based on 74 aerial wartime images taken over different areas in Central Europe, show the potential of the method; among other findings, a clear improvement is achieved by using redundant image information. We also compared the MPP method for bomb crater detection with a state-of-of-the-art convolutional neural network (CNN) for generating region proposals; it turned out that the CNN outperforms the MPPs if a sufficient amount of representative training data is available and a threshold for a region to be considered as crater is properly tuned prior to running the experiments. If this is not the case, the MPP approach achieves better results. Numéro de notice : A2022-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100017 Date de publication en ligne : 02/06/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101057
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022)[article]Location-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)
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Titre : Location-aware neural graph collaborative filtering Type de document : Article/Communication Auteurs : Shengwen Li, Auteur ; Chenpeng Sun, Auteur ; Renyao Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1550 - 1574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] comportement
[Termes IGN] données localisées des bénévoles
[Termes IGN] filtrage d'information
[Termes IGN] jeu de données
[Termes IGN] noeud
[Termes IGN] point d'intérêt
[Termes IGN] réseau neuronal de graphesRésumé : (auteur) Collaborative filtering (CF) is initiated by representing users and items as vectors and seeks to describe the relationship between users and items at a profound level, thus predicting users’ preferred behavior. To address the issue that previous research ignored higher-order geographical interactions hidden in users’ historical behaviors, this paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF), which incorporates location information of items for improving prediction performance. The model characterizes the interactions between items based on spatial decay law from a graph perspective and designs two strategies to capture the interaction effects of users and items considering node heterogeneity. An optimized loss function with spatial distances of items is also developed in the model. Extensive experiments are conducted on three publicly available real-world datasets to examine the effectiveness of our model. Results show that LA-NGCF achieves competitive performances compared with several state-of-the-art models, which suggests that location information of items is beneficial for improving the performance of personalized recommendations. This paper offers an approach to incorporate weighted interactions between items into CF algorithms and enriches the methods of utilizing geographical information for artificial intelligence applications. Numéro de notice : A2022-592 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2073594 Date de publication en ligne : 11/05/2022 En ligne : https://doi.org/10.1080/13658816.2022.2073594 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101292
in International journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1550 - 1574[article]Mapping 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)
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Titre : Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series Type de document : Article/Communication Auteurs : Maximilian Lange, Auteur ; Hannes Feilhauer, Auteur ; Ingolf Kühn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échantillonnage de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] prairie
[Termes IGN] série temporelleRésumé : (auteur) Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands. Numéro de notice : A2022-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112888 Date de publication en ligne : 13/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100805
in Remote sensing of environment > vol 277 (August 2022) . - n° 112888[article]A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery / Sajid Ghuffar in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
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Titre : A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery Type de document : Article/Communication Auteurs : Sajid Ghuffar, Auteur ; Tobias Bolch, Auteur ; Ewelina Rupnik , Auteur ; Atanu Bhattacharya, Auteur
Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie
voir aussi https://research-repository.st-andrews.ac.uk/bitstream/10023/26124/1/Ghuffar_2022_IEEE_TGRS_Pipeline_automated_processing_AAM.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compensation par faisceaux
[Termes IGN] géométrie de l'image
[Termes IGN] géométrie épipolaire
[Termes IGN] glacier
[Termes IGN] Himalaya
[Termes IGN] image Corona
[Termes IGN] image panoramique
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'appuiRésumé : (auteur) The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time dependent exterior orientation parameters is employed. The results show that using the entire frame of the Corona image, bundle adjustment using well-distributed GCPs results in an average standard deviation (SD) of less than 2 pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the accuracy. We further assess a polynomial epipolar resampling method for rectification of Corona stereo images. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long term storage as likely cause of systematic deviations. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation (NMAD) of elevation differences of ? 4m over an area of approx. 4000km2. We show that the proposed pipeline can be applied to sequence of complex scenes involving high relief and glacierized terrain and that the resulting DEMs can be used to compute long term glacier elevation changes over large areas. Numéro de notice : A2022-952 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3200151 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3200151 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103286
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - pp[article]Predicting vegetation stratum occupancy from airborne LiDAR data with deep learning / Ekaterina Kalinicheva in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)
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Titre : Predicting vegetation stratum occupancy from airborne LiDAR data with deep learning Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva , Auteur ; Loïc Landrieu
, Auteur ; Clément Mallet
, Auteur ; Nesrine Chehata
, Auteur
Année de publication : 2022 Projets : TOSCA-FRISBEE / Article en page(s) : n° 102863 Note générale : bibliographie
This study has been co-funded by CNES (TOSCA FRISBEE Project, convention no200769/00) and CONFETTI Project (Nouvelle Aquitaine Region project, France).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] parcelle agricole
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] strate végétaleRésumé : (auteur) We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms. Numéro de notice : A2022-578 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102863 Date de publication en ligne : 19/07/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99425
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102863[article]Documents numériques
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