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Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)
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Titre : Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan Type de document : Article/Communication Auteurs : Dirk Tiede, Auteur ; Gina Schwendemann, Auteur ; Ahmad Alobaidi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1213-1227 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 par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] échantillonnage
[Termes IGN] épidémie
[Termes IGN] gestion de crise
[Termes IGN] HRV (capteur)
[Termes IGN] image à très haute résolution
[Termes IGN] image Pléiades-HR
[Termes IGN] itération
[Termes IGN] SoudanRésumé : Auteur) Within the constraints of operational work supporting humanitarian organizations in their response to the Covid-19 pandemic, we conducted building extraction for Khartoum, Sudan. We extracted approximately 1.2 million dwellings and buildings, using a Mask R-CNN deep learning approach from a Pléiades very high-resolution satellite image with 0.5 m pixel resolution. Starting from an untrained network, we digitized a few hundred samples and iteratively increased the number of samples by validating initial classification results and adding them to the sample collection. We were able to strike a balance between the need for timely information and the accuracy of the result by combining the output from three different models, each aiming at distinctive types of buildings, in a post-processing workflow. We obtained a recall of 0.78, precision of 0.77 and F1 score of 0.78, and were able to deliver first results in only 10 days after the initial request. The procedure shows the great potential of convolutional neural network frameworks in combination with GIS routines for dwelling extraction even in an operational setting. Numéro de notice : A2021-464 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12766 Date de publication en ligne : 06/05/2021 En ligne : https://doi.org/10.1111/tgis.12766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98060
in Transactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1213-1227[article]Multiscale cloud detection in remote sensing images using a dual convolutional neural network / Markku Luotamo in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
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Titre : Multiscale cloud detection in remote sensing images using a dual convolutional neural network Type de document : Article/Communication Auteurs : Markku Luotamo, Auteur ; Sari Metsämäki, Auteur ; Arto Klami, Auteur Année de publication : 2021 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection des nuages
[Termes IGN] granularité d'image
[Termes IGN] image Sentinel-MSI
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence, features that have a large spatial extent still cause challenges in tasks, such as cloud masking. To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full-resolution images. The first component distinguishes between patches in the inner cloud area from patches at the cloud’s boundary region. For the cloud-ambiguous edge patches requiring further segmentation, the framework then delegates computation to a fine-grained model component. We apply the architecture to a cloud detection data set of complete Sentinel-2 multispectral images, approximately annotated for minimal false negatives in a land-use application. On this specific task and data, we achieve a 16% relative improvement in pixel accuracy over a CNN baseline based on patching. Numéro de notice : A2021-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3015272 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3015272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97781
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp[article]Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)
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Titre : Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park Type de document : Article/Communication Auteurs : Emma L. Davis, Auteur ; Andrew Trant, Auteur ; Robert G. Way, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbuste
[Termes IGN] Arctique
[Termes IGN] Canada
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] écosystème
[Termes IGN] écotone
[Termes IGN] géostatistique
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] parc naturel national
[Termes IGN] régression logistique
[Termes IGN] surveillance de la végétation
[Termes IGN] toundraRésumé : (auteur) Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and regional declines in caribou. Little is known, however, about how biophysical controls mediate plant responses to climate warming, and available observational data are limited in temporal and spatial scope. In this study, we investigated the drivers of land cover change for the 9700 km2 extent of the park using satellite remote sensing and geostatistical modelling. Random forest classification was used to hindcast and simulate land cover change for four different land cover types from 1985 to 2019 with topographic and surface reflectance imagery (Landsat archive). The resulting land cover maps, in addition to topographic and biotic variables, were then used to predict where future shrub expansion is likely to occur using a binomial regression framework. Land cover hindcasts showed a 235% increase in shrub and a 105% increase in wet vegetation cover from 1985/89 to 2015/19. Shrub cover was highly persistent and displaced wet vegetation in southern, low-elevation areas, whereas wet vegetation expanded to formerly dry, mid-elevations. The predictive model identified both biotic (initial cover class, number of surrounding shrub neighbors), and topographic variables (elevation, latitude, and distance to the coast) as strong predictors of future shrub expansion. A further 51% increase in shrub cover is expected by 2039/43 relative to 2014 reference data. Establishing long-term monitoring plots within TMNP in areas where rapid vegetation change is predicted to occur will help to validate remote sensing observations and will improve our understanding of the consequences of change for biotic and abiotic components of the tundra ecosystem, including important cultural keystone species. Numéro de notice : A2021-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112085 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.3390/rs13112085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97832
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2085[article]Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles in XYZ, n° 167 (juin 2021)
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Titre : Reconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne Type de document : Article/Communication Auteurs : Valentin Desbiolles, Auteur Année de publication : 2021 Article en page(s) : pp 33 - 38 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] Autocad Map
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dessin assisté par ordinateur
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] jumeau numérique
[Termes IGN] orthoimage
[Termes IGN] reconnaissance d'objets
[Termes IGN] transformation de Hough
[Termes IGN] voie ferréeRésumé : (Auteur) Ce projet propose une étude sur l’insertion automatique d’objets utiles au fonctionnement d’une voie ferrée dans un plan DAO. Ces objets sont visibles sur des orthophotos acquises par moyens aéroportés (drone ou hélicoptère). La solution se scinde en deux grands axes : 1- la détection et la localisation des objets d’intérêt sur une orthophoto ; 2- leurs insertions dans un plan DAO. Ce PFE parcourt ainsi les différentes techniques pour automatiser une phase de reconnaissance de certains éléments cibles sur une image pour finir sur le développement d’une méthode permettant de les reporter dans un plan DAO automatiquement. Numéro de notice : A2021-462 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Date de publication en ligne : 01/06/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97928
in XYZ > n° 167 (juin 2021) . - pp 33 - 38[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2021021 RAB Revue Centre de documentation En réserve L003 Disponible Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
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Titre : Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning Type de document : Article/Communication Auteurs : Qiutong Yu, Auteur ; Wei Liu, Auteur ; Wesley Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 405 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] carte d'occupation du sol
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] série temporelleRésumé : (Auteur) Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS. Numéro de notice : A2021-373 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.6.405 Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.6.405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97825
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 405 - 412[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021061 SL Revue Centre de documentation Revues en salle Disponible Uncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)
PermalinkA deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)
PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)
PermalinkAutomatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression / Ignacio Hernández-Bautista in The Visual Computer, vol 37 n° 5 (May 2021)
PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])
PermalinkIntegrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)
PermalinkLearning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
PermalinkLearning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
PermalinkLifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May-1 2021)
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