Descripteur
Documents disponibles dans cette catégorie (1270)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Automatic 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)
[article]
Titre : Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi Type de document : Article/Communication Auteurs : Yafei Jing, Auteur ; Yuhuan Ren, Auteur ; Yalan Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] extraction automatique
[Termes IGN] image captée par drone
[Termes IGN] orthoimage
[Termes IGN] séisme
[Termes IGN] Yunnan (Chine)Résumé : (auteur) Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable. Numéro de notice : A2022-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14020382 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.3390/rs14020382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99577
in Remote sensing > vol 14 n° 2 (January-2 2022) . - n° 382[article]Co-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)
[article]
Titre : Co-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements Type de document : Article/Communication Auteurs : Mokhamad Nur Cahyadi, Auteur ; Buldan Muslim, Auteur ; Danar Guruh Pratomo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 401 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] diffusion de Rayleigh
[Termes IGN] données GLONASS
[Termes IGN] données GNSS
[Termes IGN] Indonésie
[Termes IGN] onde acoustique
[Termes IGN] perturbation ionosphérique
[Termes IGN] propagation ionosphérique
[Termes IGN] séisme
[Termes IGN] Sumatra
[Termes IGN] teneur totale en électrons
[Termes IGN] tsunamiRésumé : (auteur) The study of ionospheric disturbances associated with the two large strike-slip earthquakes in Indonesia was investigated, which are West Sumatra on 2 March 2016 (Mw = 7.8), and Palu on 28 September 2018 (Mw = 7.5). The anomalies were observed by measuring co-seismic ionospheric disturbances (CIDs) using the Global Navigation Satellite System (GNSS). The results show positive and negative CIDs polarization changes for the 2016 West Sumatra earthquake, depending on the position of the satellite line-of-sight, while the 2018 Palu earthquake shows negative changes only due to differences in co-seismic vertical crustal displacement. The 2016 West Sumatra earthquake caused uplift and subsidence, while the 2018 Palu earthquake was dominated by subsidence. TEC anomalies occurred about 10 to 15 min after the two earthquakes with amplitude of 2.9 TECU and 0.4 TECU, respectively. The TEC anomaly amplitude was also affected by the magnitude of the earthquake moment. The disturbance signal propagated with a velocity of ~1–1.72 km s−1 for the 2016 West Sumatra earthquake and ~0.97–1.08 km s−1 for the 2018 Palu mainshock earthquake, which are consistent with acoustic waves. The wave also caused an oscillation signal of ∼4 mHz, and their azimuthal asymmetry of propagation confirmed the phenomena in the Southern Hemisphere. The CID signal could be identified at a distance of around 400–1500 km from the epicenter in the southwestern direction. Numéro de notice : A2022-103 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14020401 Date de publication en ligne : 16/01/2022 En ligne : https://doi.org/10.3390/rs14020401 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99571
in Remote sensing > vol 14 n° 2 (January-2 2022) . - n° 401[article]Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])
[article]
Titre : Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation Type de document : Article/Communication Auteurs : Narissara Nuthammachot, Auteur ; Askar Askar, Auteur ; Dimitris Stratoulias, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 366 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] biomasse aérienne
[Termes IGN] corrélation
[Termes IGN] échantillonnage de données
[Termes IGN] forêt privée
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] Indonésie
[Termes IGN] précision de l'estimationRésumé : (auteur) Above-ground Biomass (AGB) represents the largest amount of biomass found on earth. Passive and active remote sensors have been a useful tool in estimating AGB for this purpose; nevertheless, both data sources suffer from saturation problems in dense vegetation. A combination of optical and radar data could potentially increase the accuracy of AGB estimation. In this study we evaluate the synergistic use of Sentinel-1 and Sentinel-2 for assessing AGB in a private forest in Yogyakarta, Indonesia. Forty five sample plots of 20 m x 20 m were used as ground truth data. AGB correlated with Sentinel-1 backscatter and Sentinel-2 derived variables with R2 = 0.34 and R2 = 0.82, respectively; nevertheless, the synergistic use of Sentinel-1 and Sentinel-2 yielded the highest accuracy (i.e., R2 = 0.84). The results indicate that AGB in Yogyakarta is most accurately estimated based on the synergy of optical and radar satellite images. Numéro de notice : A2022-049 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1726507 Date de publication en ligne : 13/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1726507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99440
in Geocarto international > vol 37 n° 2 [15/01/2022] . - pp 366 - 376[article]Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])
[article]
Titre : Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 657 - 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image Pléiades-HR
[Termes IGN] image Worldview
[Termes IGN] occupation du sol
[Termes IGN] segmentation sémantique
[Termes IGN] TurquieRésumé : (auteur) Research to improve the accuracy of very high-resolution satellite image classification algorithms is still one of the hot topics in the field of remote sensing. Successful results of deep learning methods in areas such as image classification and object detection have led to the application of these methods to remote sensing problems. Recently, Convolutional Neural Networks (CNNs) are among the most common deep learning methods used in image classification, however, the use of CNN’s in satellite image classification is relatively new. Due to the high computational complexity of 3D CNNs, which aim to extract both spatial and spectral information, 2D CNNs focussing on the extraction of spatial information are often preferred. High-resolution satellite images, however, contain crucial spectral information as well as spatial information. In this study, a 3D-2D CNN model using both spectral and spatial information was applied to extract more accurate land cover information from very high-resolution satellite images. The model was applied on a Worldview-2 satellite image including agricultural product areas such as tea, hazelnut groves and land use classes such as buildings and roads. The results of the CNN based model were also compared against those of the Support Vector Machine (SVM) and Random Forest (RF) algorithms. The post-classification accuracies were obtained using 800 control points generated by a web interface created for crowdsourcing purposes. The classification accuracy was 95.6% for the 3D-2D CNN model, 89.2% for the RF and 86.4% for the SVM. Numéro de notice : A2022-305 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1734871 Date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1734871 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100379
in Geocarto international > vol 37 n° 2 [15/01/2022] . - pp 657 - 677[article]Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)
[article]
Titre : Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS Type de document : Article/Communication Auteurs : Sumedh R. Kashiwar, Auteur ; Manik Chandra Kundu, Auteur ; Usha R. Dongarwar, Auteur Année de publication : 2022 Article en page(s) : pp 937 - 959 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte thématique
[Termes IGN] dégradation des sols
[Termes IGN] érosion
[Termes IGN] érosion hydrique
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] Inde
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle RUSLE
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] précipitation
[Termes IGN] rive
[Termes IGN] système d'information géographiqueRésumé : (auteur) The agricultural land of the whole world is deteriorating due to the loss of top fertile soil reducing agricultural productivity and groundwater availability. Mainly, natural conditions and human manipulations have made soils extremely prone to soil erosion. Therefore, information on soil erosion status is of paramount importance to the policymakers for land conservation planning in a limited time. Spatial information systems like GIS and RS are known for their efficiencies. With that prospect, the GIS-based RUSLE model is used in this study to assess the soil erosion losses from Bhandara regions of Maharashtra, India. The study area comes under Wainganga sub-river basin, a portion of the Godavari River basin. We have prepared the required five potential parameters (R*K*LS*C*P) of RUSLE model on pixel-to-pixel basis. We have prepared the R factor map from monthly rainfall data of Indian Meteorological Department (IMD) and K factor map by digital the soil series map of NBSS & LUP, Govt. of India. We have used the digital elevation model data (DEM) of Cartosat-1 for LS-factor map, Landsat 8 and Sentinel-2A satellite dataset to generate LULC and NDVI map to obtain C and P factors. The results and satellite data were validated using Google Earth Pro and field observations. The results showed significant soil erosion from the river banks and wastelands near water bodies, with the soil loss values ranging between 20 and 40 t ha−1 yr−1. The land under reserved forest was very slight erosion-prone soil with soil loss of Numéro de notice : A2022-180 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-021-04974-5 Date de publication en ligne : 16/08/2021 En ligne : https://doi.org/10.1007/s11069-021-04974-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99856
in Natural Hazards > vol 110 n° 2 (January 2022) . - pp 937 - 959[article]Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)PermalinkAn approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkAn extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkAssessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India / Rajib Mitra in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkAttributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)PermalinkA constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data / Jing Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkCultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 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)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)Permalink