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Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (October-1 2021)
[article]
Titre : Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery Type de document : Article/Communication Auteurs : Run Yu, Auteur ; Youqing Luo, Auteur ; Quan Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119493 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dépérissement
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] maladie phytosanitaire
[Termes IGN] milieu tropical
[Termes IGN] peuplement mélangé
[Termes IGN] Pinus (genre)
[Termes IGN] Pinus massoniana
[Termes IGN] réflectance spectrale
[Termes IGN] Ulmus (genre)Résumé : (auteur) Pine wilt disease (PWD) is a global devastating threat to forest ecosystems. Therefore, a feasible and effective approach to precisely monitor PWD infection is indispensable, especially at the early stages. However, a precise definition of “early stage” and a rapid and high-efficiency method to detect PWD infection have not been well established. In this study, we systematically divided the PWD infection into green, early, middle, and late stages based on the needle color, the resin secretion, and whether the pine wood nematode (PWN) was carried. Simultaneously, an unmanned aerial vehicle (UAV) equipped with multispectral cameras was used to obtain images. Two target detection algorithms (Faster R-CNN and YOLOv4) and two traditional machine learning algorithms based on feature extraction (random forest and support vector machine) were employed to realize the recognition of infected pine trees. Moreover, we took into consideration of the influence of green broad-leaved trees on the identification of pine trees at the early stage of PWD infection. We obtained the following results: (1) the accuracy of Faster R-CNN (60.98–66.7%) was higher than that of YOLOv4 (57.07–63.55%), but YOLOv4 outperformed in terms of model size, processing speed, training time, and testing time; (2) although the traditional machine learning models had higher accuracy (73.28–79.64%), they were not able to directly identify the object from the images; (3) the accuracy of early detection of PWD infection showed an increase of 3.72–4.29%, from 42.36–44.59% to 46.08–48.88%, when broad-leaved trees were considered. In this study, the combination of UAV-based multispectral images and target detection algorithms allowed us to monitor the occurrence of PWD and obtain the distribution of infected trees at an early stage, which can provide technical support for the prevention and control of PWD. Numéro de notice : A2021-658 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119493 En ligne : https://doi.org/10.1016/j.foreco.2021.119493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98395
in Forest ecology and management > vol 497 (October-1 2021) . - n° 119493[article]Extracting 3D indoor maps with any shape accurately using building information modeling data / Qi Qiu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)
[article]
Titre : Extracting 3D indoor maps with any shape accurately using building information modeling data Type de document : Article/Communication Auteurs : Qi Qiu, Auteur ; Mingjun Wang, Auteur ; Qingsheng Xie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carroyage
[Termes IGN] carte d'intérieur
[Termes IGN] carte en 3D
[Termes IGN] conception assistée par ordinateur
[Termes IGN] détection de contours
[Termes IGN] grille
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] service fondé sur la positionRésumé : (auteur) Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems. Numéro de notice : A2021-778 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100700 Date de publication en ligne : 14/10/2021 En ligne : https://doi.org/10.3390/ijgi10100700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98842
in ISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 700[article]Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])
[article]
Titre : Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques Type de document : Article/Communication Auteurs : Rajkumar Dhakar, Auteur ; Vinay Kumar Sehgal, Auteur ; Debasish Chakraborty, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2044 - 2064 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] correction atmosphérique
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] Inde
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réseau neuronal artificielRésumé : (auteur) This study assessed the effect of atmospheric correction algorithms, inversion techniques and image spatial and spectral resolution on wheat crop LAI retrieval using Sentinel-2 MSI and Landsat-8 OLI imagery. The LAI retrievals were validated with in-situ measurements collected in farmers’ fields. The MSI-based LAI retrievals improved significantly when images were atmospherically corrected using MODTRAN than using the libRadtran code. Among the two PROSAIL inversion approaches, look-up table outperforms artificial neural network for LAI retrievals. Using the best strategy of atmospheric correction and inversion, the effect of spatial resolution from 20 m (MSI) to 30 m (OLI) while using common six bands, showed non-significant improvement in LAI retrievals. The inclusion of additional two red-edge bands as available in MSI significantly reduced the uncertainly in LAI retrievals over that obtained by using six bands, while inclusion of only additional VNIR band did not show any significant effect on LAI retrievals. Numéro de notice : A2021-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1687591 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98666
in Geocarto international > vol 36 n° 18 [01/10/2021] . - pp 2044 - 2064[article]GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
[article]
Titre : GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings Type de document : Article/Communication Auteurs : Bin Liu, Auteur ; Yuxiao Ren, Auteur ; Hanchi Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8305 - 8325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] cible cachée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] géolocalisation par radar pénétrant GPR
[Termes IGN] reconstruction d'image
[Termes IGN] revêtement
[Termes IGN] tunnelRésumé : (auteur) A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Numéro de notice : A2021-710 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3046454 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3046454 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98610
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8305 - 8325[article]GROOPS: A software toolkit for gravity field recovery and GNSS processing / Torsten Mayer-Gürr in Computers & geosciences, vol 155 (October 2021)
[article]
Titre : GROOPS: A software toolkit for gravity field recovery and GNSS processing Type de document : Article/Communication Auteurs : Torsten Mayer-Gürr, Auteur ; Saniya Behzadpour, Auteur ; Annette Eicker, Auteur Année de publication : 2021 Article en page(s) : n° 104864 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] C++
[Termes IGN] champ de pesanteur terrestre
[Termes IGN] logiciel libre
[Termes IGN] orbitographie
[Termes IGN] série temporelle
[Termes IGN] traitement de données GNSSRésumé : (auteur) The Gravity Recovery Object Oriented Programming System (GROOPS) is a software toolkit written in C++ that enables the user to perform core geodetic tasks. Key features of the software include gravity field recovery from satellite and terrestrial data, the determination of satellite orbits from global navigation satellite system (GNSS) measurements, and the computation of GNSS constellations and ground station networks. Next to raw data processing, GROOPS is capable to operate on time series and spatial data to directly analyze and visualize the computed data sets. Most tasks and algorithms are (optionally) parallelized through the Message Passing Interface, thus the software enables a smooth transition from single-CPU desktop computers to large distributed computing environments for resource intensive tasks. For an easy and intuitive setup of complex workflows, GROOPS contains a graphical user interface to create and edit configuration files. The source code of the software is freely available on GitHub (https://github.com/groops-devs/groops) together with documentation, a cookbook with guided examples, and step-by-step installation instructions. Numéro de notice : A2021-948 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.cageo.2021.104864 Date de publication en ligne : 23/06/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99765
in Computers & geosciences > vol 155 (October 2021) . - n° 104864[article]Impact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkLandslide susceptibility prediction based on image semantic segmentation / Bowen Du in Computers & geosciences, vol 155 (October 2021)PermalinkLeast squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkA novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkOn the suitability of a unified GIS-BIM-HBIM framework for cataloguing and assessing vulnerability in Historic Urban Landscapes: a critical review / Rafael Ramirez Eudave in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkSpatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkSpatial thinking in cartography teaching for schoolchildren / Sonia Maria Vanzella Castellar in International journal of cartography, vol 7 n° 3 (October 2021)PermalinkThe integration of GPS/BDS real-time kinematic positioning and visual–inertial odometry based on smartphones / Zun Niu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkUnsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkA web GIS-based integration of 3D digital models with linked open data for cultural heritage exploration / Ikrom Nishanbaev in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkAssessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])PermalinkMapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkThe impact of landscape characteristics on the performance of upscaled maps / Peijun Sun in Geocarto international, vol 36 n° 17 ([15/09/2021])Permalink3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkAutomatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkBenford’s law and geographical information – the example of OpenStreetMap / Franz-Benjamin Mocnik in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)PermalinkCombining photogrammetric and bathymetric data to build a 3D model of a canal tunnel / Emmanuel Moisan in Photogrammetric record, Vol 36 n° 175 (September 2021)PermalinkA deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkA framework for ecosystem service assessment using GIS interoperability standards / Martin Lacayo in Computers & geosciences, vol 154 (September 2021)PermalinkGIScience integrated with computer vision for the examination of old engravings and drawings / Motti Zohar in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)PermalinkIndoor space as the basis for modelling of buildings in a 3D Cadastre / Jernej Tekavec in Survey review, Vol 53 n° 380 (September 2021)PermalinkLes journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)PermalinkA learning-based approach to automatically evaluate the quality of sequential color schemes for maps / Taisheng Chen in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)PermalinkMetaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs / Arnaud Flori in Machine Vision and Applications, vol 32 n° 5 (September 2021)PermalinkMise en place d'un dispositif expérimental numérique pour l'enseignement des risques naturels avec le jeu vidéo Minetest / Jérôme Staub in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)PermalinkLa modélisation automatique d'un bâtiment à partir d'un nuage de points / Thibault Mauger in XYZ, n° 168 (septembre 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkA multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)PermalinkRelevés de la grotte Cosquer : partie 1, la grotte, les premiers relevés, un monument et une histoire chaotiques / Bertrand Chazaly in XYZ, n° 168 (septembre 2021)PermalinkRelevés de la grotte Cosquer : partie 2, vers une dématérialisation de la grotte par digitalisation des processus et numérisations 3D, comment offrir à la communauté scientifique et au grand public l’accès au plus inaccessible des grands sanctuaires ornés d’Europe / Bertrand Chazaly in XYZ, n° 168 (septembre 2021)PermalinkStochastic 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)PermalinkThree-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkDeep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkA hybrid data model for dynamic GIS: application to marine geomorphological dynamics / Younes Hamdani in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkIntegrating GIS and location modeling: A relational approach / Ting L. Lei in Transactions in GIS, Vol 25 n° 4 (August 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkPredicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)Permalink