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Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis / Daniele Cirillo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis Type de document : Article/Communication Auteurs : Daniele Cirillo, Auteur ; Francesca Cerritelli, Auteur ; Silvano Agostini, Auteur Année de publication : 2022 Article en page(s) : n° 437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Apennins
[Termes IGN] carte géologique
[Termes IGN] déformation de la croute terrestre
[Termes IGN] géologie
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] post-traitement
[Termes IGN] structure-from-motionRésumé : (auteur) We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area’s tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application. Numéro de notice : A2022-648 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080437 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.3390/ijgi11080437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101464
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 437[article]Transfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Transfer learning from citizen science photographs enables plant species identification in UAV imagery Type de document : Article/Communication Auteurs : Salim Soltani, Auteur ; Hannes Feilhauer, Auteur ; Robbert Duker, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données naturalistes
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] espèce végétale
[Termes IGN] filtrage de la végétation
[Termes IGN] identification de plantes
[Termes IGN] image captée par drone
[Termes IGN] orthoimage couleur
[Termes IGN] science citoyenne
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications. Numéro de notice : A2022-488 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100016 Date de publication en ligne : 23/05/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100956
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100016[article]Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])
[article]
Titre : Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks Type de document : Article/Communication Auteurs : Gensheng Hu, Auteur ; Yanqiu Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3520 - 3539 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] image captée par drone
[Termes IGN] Pinus (genre)
[Termes IGN] santé des forêtsRésumé : (auteur) This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology. Numéro de notice : A2022-588 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1864025 Date de publication en ligne : 06/01/2021 En ligne : https://doi.org/10.1080/10106049.2020.1864025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101362
in Geocarto international > vol 37 n° 12 [01/07/2022] . - pp 3520 - 3539[article]Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)
[article]
Titre : Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 Type de document : Article/Communication Auteurs : Fahime Arabi Aliabad, Auteur ; Hamid Reza Ghafarian Malamiri, Auteur ; Saeed Shojaei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3227 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] croissance urbaine
[Termes IGN] détection de changement
[Termes IGN] Google Earth
[Termes IGN] image captée par drone
[Termes IGN] image Sentinel-MSI
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method. Numéro de notice : A2022-572 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.3390/rs14133227 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.3390/rs14133227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101288
in Remote sensing > vol 14 n° 13 (July-1 2022) . - n° 3227[article]Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)
[article]
Titre : Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review Type de document : Article/Communication Auteurs : André Duarte, Auteur ; Nuno Borralho, Auteur ; Pedro Cabral, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 911 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] insecte nuisible
[Termes IGN] maladie parasitaire
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] santé des forêts
[Termes IGN] structure-from-motion
[Termes IGN] surveillance forestièreRésumé : (auteur) Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented. Numéro de notice : A2022-483 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13060911 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.3390/f13060911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100897
in Forests > vol 13 n° 6 (June 2022) . - n° 911[article]True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)PermalinkAlternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)PermalinkUnveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations / Francesco Solano in Ecological indicators, vol 138 (May 2022)PermalinkAutomated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkAssessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity / Stéphane Bertin in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkDirect photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkHybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy / Norbert Haala in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)PermalinkPolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkAutomatic extraction of building geometries based on centroid clustering and contour analysis on oblique images taken by unmanned aerial vehicles / Leilei Zhang in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkComparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment / Longfei Zhou in Urban Forestry & Urban Greening, vol 69 (March 2022)PermalinkEstimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds / Jiayuan Lin in Urban Forestry & Urban Greening, vol 69 (March 2022)PermalinkMonitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkMulti-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkIntegrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan / Katsuto Shimizu in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkAutomatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkClassification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)Permalink