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Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7211 - 7223 Titre : Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network Type de document : Article/Communication Auteurs : Jussi Leinonen, Auteur ; Daniele Nerini, Auteur ; Alexis Berne, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7211 - 7223 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données météorologiques [Termes IGN] épaisseur de nuage [Termes IGN] image à basse résolution [Termes IGN] image GOES [Termes IGN] modèle atmosphérique [Termes IGN] précipitation [Termes IGN] processus stochastique [Termes IGN] réduction d'échelle [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal convolutif [Termes IGN] Suisse Résumé : (auteur) Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as “downscaling” in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two data sets: one consisting of radar-measured precipitation from Switzerland; the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both data sets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month data set of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan. Numéro de notice : A2021-645 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032790 date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98349 [article]

A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery / Seth Goodman in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 674 - 691 Titre : A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery Type de document : Article/Communication Auteurs : Seth Goodman, Auteur ; Ariel BenYishay, Auteur ; Daniel Runfola, Auteur Année de publication : 2021 Article en page(s) : pp 674 - 691 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] conflit [Termes IGN] image Landsat-8 [Termes IGN] implémentation (informatique) [Termes IGN] Nigéria [Termes IGN] prédiction [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN‐based approach leveraging Landsat 8 imagery to predict locations of conflict‐related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non‐fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low‐cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate‐resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN‐based methods of estimating development‐related indicators may be effective in applications beyond those explored in the literature. Numéro de notice : A2021-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12661 date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1111/tgis.12661 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97625 [article]

Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 147 - 163 Titre : Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Di Liu, Auteur ; Jonathan Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 147 - 163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] balayage laser [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] graphe [Termes IGN] reconnaissance de formes [Termes IGN] réseau neuronal convolutif [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile Résumé : (Auteur) The representation of 3D data is the key issue for shape analysis. However, most of the existing representations suffer from high computational cost and structure information loss. This paper presents a novel sequential slice representation with an attention-embedding network, named RSSNet, for 3D point cloud recognition and retrieval in road environments. RSSNet has two main branches. Firstly, a sequential slice module is designed to map disordered 3D point clouds to ordered sequence of shallow feature vectors. A gated recurrent unit (GRU) module is applied to encode the spatial and content information of these sequential vectors. The second branch consists of a key-point based graph convolution network (GCN) with an embedding attention strategy to fuse the sequential and global features to refine the structure discriminability. Three datasets were used to evaluate the proposed method, one acquired by our mobile laser scanning (MLS) system and two public datasets (KITTI and Sydney Urban Objects). Experimental results indicated that the proposed method achieved better performance than recognition and retrieval state-of-the-art methods. RSSNet provided recognition rates of 98.08%, 95.77% and 70.83% for the above three datasets, respectively. For the retrieval task, RSSNet obtained excellent mAP values of 95.56%, 87.16% and 69.99% on three datasets, respectively. Numéro de notice : A2020-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.003 date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94582 [article]

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A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106 Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Brésil [Termes IGN] carte de confiance [Termes IGN] Citrus (genre) [Termes IGN] détection d'arbres [Termes IGN] géolocalisation [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] inventaire de la végétation [Termes IGN] réseau neuronal convolutif [Termes IGN] verger Résumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525 [article]

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Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 1 - 17 Titre : Transferring deep learning models for cloud detection between Landsat-8 and Proba-V Type de document : Article/Communication Auteurs : Gonzalo Mateo-García, Auteur ; Valero Laparra, Auteur ; Dan López-Puigdollers, Auteur ; Luis Gómez-Chova, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage par transformation [Termes IGN] apprentissage profond [Termes IGN] conversion de données [Termes IGN] détection des nuages [Termes IGN] échantillonnage de données [Termes IGN] image Landsat-8 [Termes IGN] image multibande [Termes IGN] image PROBA [Termes IGN] jeu de données [Termes IGN] masque [Termes IGN] réseau neuronal convolutif [Termes IGN] seuillage de points Résumé : (Auteur) Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79–89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1–10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images. Numéro de notice : A2020-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.024 date de publication en ligne : 10/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94522 [article]

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Remote sensing based building extraction / Mohammad Awrangjeb (2020)  

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Representation learning for natural language processing / Zhiyuan Liu (2020)  

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SpiNNaker: A spiking neural network architecture / Steve Furber (2020)  

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Système de traitement d’images temps réel dédié à la mesure de champs denses de déplacements et de déformations / Seyfeddine Boukhtache (2020)  

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Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Learning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

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Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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