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Visualising post-disaster damage on maps: a user study / Thomas Candela in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1364 - 1393 Titre : Visualising post-disaster damage on maps: a user study Type de document : Article/Communication Auteurs : Thomas Candela, Auteur ; Matthieu Péroche, Auteur ; Arnaud Sallaberry, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1364 - 1393 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de répartition par points [Termes IGN] catastrophe naturelle [Termes IGN] comportement [Termes IGN] dommage matériel [Termes IGN] enquête [Termes IGN] lecture de carte [Termes IGN] oculométrie [Termes IGN] psychologie cognitive [Termes IGN] représentation cartographique [Termes IGN] sémiologie graphique [Termes IGN] tessellation [Vedettes matières IGN] Géovisualisation Résumé : (auteur) The mapping of the damage caused by natural disasters is a crucial step in deciding on the actions to take at the international, national, and local levels. The large variety of representations that we have observed leads to problems of transfer and variations in analysis. In this article, we propose a representation, Regular Dot map (RD), and we compare it to 4 others routinely used to visualise post-disaster damage. Our comparison is based on a user study in which a set of participants carried out various tasks on multiple datasets using the various visualisations. We then analysed the behaviour during the experiment using three approaches: (1) quantitative analysis of user answers according to the reality on the ground, (2) quantitative analysis of user preferences in terms of perceived effectiveness and appearance, and (3) qualitative analysis of the data collected using an eye tracker. The results of this study lead us to believe that RD is the best compromise in terms of effectiveness among the various representations studied. Numéro de notice : A2022-492 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2063872 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1080/13658816.2022.2063872 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100971 [article]

Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 2 (February 2022) . - pp 685 - 694 Titre : Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation Type de document : Article/Communication Auteurs : Ramazan Unlu, Auteur ; Recep Kiriş, Auteur Année de publication : 2022 Article en page(s) : pp 685 - 694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] bâtiment [Termes IGN] classification par nuées dynamiques [Termes IGN] détection de changement [Termes IGN] dommage matériel [Termes IGN] données étiquetées d'entrainement [Termes IGN] réseau neuronal convolutif [Termes IGN] segmentation d'image [Termes IGN] séisme Résumé : (auteur) Detecting damaged buildings after an earthquake as quickly as possible is important for emergency teams to reach these buildings and save the lives of many people. Today, damaged buildings after the earthquake are carried out by the survivors contacting the authorities or using some air vehicles such as helicopters. In this study, AI-based systems were tested to detect damaged or destroyed buildings by integrating into street camera systems after unexpected disasters. For this purpose, we have used VGG-16, VGG-19, and NASNet convolutional neural network models which are often used for image recognition problems in the literature to detect damaged buildings. In order to effectively implement these models, we have first segmented all the images with the K-means clustering algorithm. Thereafter, for the first phase of this study, segmented images labeled “damaged buildings” and “normal” were classified and the VGG-19 model was the most successful model with a 90% accuracy in the test set. Besides, as the second phase of the study, we have created a multiclass classification problem by labeling segmented images as “damaged buildings,” “less damaged buildings,” and “normal.” The same three architectures are used to achieve the most accurate classification results on the test set. VGG-19 and VGG-16, and NASNet have achieved considerable success in the test set with about 70%, 67%, and 62% accuracy, respectively. Numéro de notice : A2022-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02043-9 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02043-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100039 [article]

Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111 Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] densité des points [Termes IGN] détection du bâti [Termes IGN] dommage matériel [Termes IGN] données localisées 3D [Termes IGN] extraction de données [Termes IGN] filtrage de points [Termes IGN] mur [Termes IGN] séisme [Termes IGN] semis de points Résumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727 [article]

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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)  

Public [article]  inRemote sensing > vol 14 n° 2 (January-2 2022) . - n° 382 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 [article]

OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 805 Titre : OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry Type de document : Article/Communication Auteurs : Xuan Fang, Auteur ; Jincheng Li, Auteur ; Ying Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 805 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image orientée objet [Termes IGN] Chine [Termes IGN] classification barycentrique [Termes IGN] dommage matériel [Termes IGN] données de terrain [Termes IGN] érosion [Termes IGN] extraction de traits caractéristiques [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] pente [Termes IGN] photogrammétrie aérienne [Termes IGN] segmentation d'image [Termes IGN] surface cultivée [Termes IGN] terrasse Résumé : (auteur) Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China. Numéro de notice : A2021-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120805 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.3390/ijgi10120805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99178 [article]

A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)  

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Disaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

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Learning 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)  

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Potentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])  

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Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon / Tamer ElGharbawi in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Optimisations cartographiques pour la gestion des crises et des risques majeurs : le cas de la cartographie des dommages post-catastrophes / Thomas Candela (2021)  

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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)  

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Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)  

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Optimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Atlas of remote sensing of the Wenchuan earthquake / Huadong Guo (2019)  

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