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Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)
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Titre : Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry Type de document : Article/Communication Auteurs : Wei Wang ; Wenbo Zhao, Auteur ; Bo Chai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105191 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] Chine
[Termes IGN] discontinuité
[Termes IGN] éboulement
[Termes IGN] extraction de données
[Termes IGN] front rocheux
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] matrice
[Termes IGN] pente
[Termes IGN] photogrammétrie aérienne
[Termes IGN] profondeur
[Termes IGN] risque naturel
[Termes IGN] semis de points
[Termes IGN] texture d'imageRésumé : (auteur) Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in challenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos acquired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information-dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes. Numéro de notice : A2022-655 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105191 Date de publication en ligne : 08/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105191 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101504
in Computers & geosciences > vol 166 (September 2022) . - n° 105191[article]Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition / Tiantian Yan in Pattern recognition, vol 127 (July 2022)
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Titre : Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition Type de document : Article/Communication Auteurs : Tiantian Yan, Auteur ; Jian Shi, Auteur ; Haojie Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108629 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] arbre aléatoire minimum
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de données
[Termes IGN] granularité d'image
[Termes IGN] image à basse résolution
[Termes IGN] image à haute résolution
[Termes IGN] relation sémantique
[Termes IGN] texture d'imageRésumé : (auteur) The existing methods of fine-grained image recognition mainly devote to learning subtle yet discriminative features from the high-resolution input. However, their performance deteriorates significantly when they are used for low quality images because a lot of discriminative details of images are missing. We propose a discriminative information restoration and extraction network, termed as DRE-Net, to address the problem of low-resolution fine-grained image recognition, which has widespread application potential, such as shelf auditing and surveillance scenarios. DRE-Net is the first framework for weakly supervised low-resolution fine-grained image recognition and consists of two sub-networks: (1) fine-grained discriminative information restoration sub-network (FDR) and (2) recognition sub-network with the semantic relation distillation loss (SRD-loss). The first module utilizes the structural characteristic of minimum spanning tree (MST) to establish context information for each pixel by employing the spatial structures between each pixel and other pixels, which can help FDR focus on and restore the critical texture details. The second module employs the SRD-loss to calibrate recognition sub-network by transferring the correct relationships between every two pixels on the feature map. Meanwhile the SRD-loss can further prompt the FDR to recover reliable and accurate fine-grained details and guide the recognition sub-network to perceive the discriminative features from the correct relationships. Extensive experiments on three benchmark datasets and one retail product dataset demonstrate the effectiveness of our proposed framework. Numéro de notice : A2022-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2022.108629 Date de publication en ligne : 06/03/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101168
in Pattern recognition > vol 127 (July 2022) . - n° 108629[article]Characteristics of disease maps of zoonoses: A scoping review and a recommendation for a reporting guideline for disease maps / Inthuja Selvaratnam in Cartographica, vol 57 n° 2 (Summer 2022)
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Titre : Characteristics of disease maps of zoonoses: A scoping review and a recommendation for a reporting guideline for disease maps Type de document : Article/Communication Auteurs : Inthuja Selvaratnam, Auteur ; Olaf Berke, Auteur ; Abhinand Thaivalappil, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 113 - 126 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse de données
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte choroplèthe
[Termes IGN] données localisées
[Termes IGN] échelle cartographique
[Termes IGN] extraction de données
[Termes IGN] lecture de carte
[Termes IGN] maladie infectieuse
[Termes IGN] projectionRésumé : (auteur) This scoping review investigates the characteristics and reporting of disease maps of zoonoses published in the scientific literature from 2017 to 2018. Two reviewers conducted duplicate screening of titles and abstracts identified from a search in Medline and additional databases. Studies were included if they had a disease map figure describing a zoonotic disease. Map characteristics were extracted and summarized from full-text articles meeting inclusion criteria. The search identified 1666 records. A total of 302 articles meeting eligibility criteria were included, comprising 505 disease maps. While most studies (66%) used maps for descriptive exploratory purposes of identifying and representing spatial patterns visually, disease maps were also used analytically to display the results of geospatial and spatial statistical analyses in studies (34%). Most published disease maps identified in this review were reported without information that could be important for geospatial interpretations and their reproducibility. Specifically, 92% of maps in this review did not report the map projection. The findings from this scoping review support the development of a reporting guideline for thematic disease maps. Numéro de notice : A2022-635 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2021-0019 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3138/cart-2021-0019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101422
in Cartographica > vol 57 n° 2 (Summer 2022) . - pp 113 - 126[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 031-2022021 RAB Revue Centre de documentation En réserve L003 Disponible GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules Type de document : Article/Communication Auteurs : Xuke Hu, Auteur ; Hussein S. Al-Olimat, Auteur ; Jens Kersten, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 310 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] classification hybride
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données topographiques
[Termes IGN] extraction de données
[Termes IGN] géobalise
[Termes IGN] microblogue
[Termes IGN] OpenStreetMap
[Termes IGN] répertoire toponymique
[Termes IGN] toponyme
[Termes IGN] TwitterRésumé : (auteur) Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. Numéro de notice : A2022-164 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1947507 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1947507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99787
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 310 - 337[article]Novel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)
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Titre : Novel model for predicting individuals’ movements in dynamic regions of interest Type de document : Article/Communication Auteurs : Xiaoqi Shen, Auteur ; Wenzhong Shi, Auteur ; Pengfei Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 250 - 271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] chaîne de Markov
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données spatiotemporelles
[Termes IGN] épidémie
[Termes IGN] extraction de données
[Termes IGN] migration humaine
[Termes IGN] mobilité territoriale
[Termes IGN] modèle de simulation
[Termes IGN] réseau social
[Termes IGN] zone d'activité économique
[Termes IGN] zone d'intérêtRésumé : (auteur) The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a novel model to predict individuals’ movements in dynamic regions of interest (ROIs), taking into account changes in activity areas and movement regularity. To address changes in the activity areas, we design a new updating strategy that can ensure the realistic extraction of an individual’s ROIs. Then, we develop an integration model for changes in the movement regularity based on two newly proposed prediction methods that consider both rapid and slow changes. The proposed integration model is evaluated based on five real-world social media datasets; three Weibo datasets related to COVID-19 collected in three Chinese cities, one Twitter dataset collected in New York and one dense GPS dataset. The results demonstrate that the proposed model can achieve better performances than state-of-the-art models, especially when mobility patterns change greatly. Combined with related pandemic data, this study will benefit pandemic prevention and control. Numéro de notice : A2022-131 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15481603.2022.2026637 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1080/15481603.2022.2026637 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99719
in GIScience and remote sensing > vol 59 n° 1 (2022) . - pp 250 - 271[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)
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)
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)
PermalinkPermalinkRemote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)
PermalinkAn analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap / A. Yair Grinberger in Transactions in GIS, Vol 25 n° 2 (April 2021)
PermalinkExtracting knowledge from legacy maps to delineate eco-geographical regions / Lin Yang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkConsolidation of crowd-sourced geo-ragged data for parameterized travel recommendations / Ago Luberg (2021)
PermalinkParticiper à la construction de la base de données des toponymes maritimes du SHOM / Solenn Tual (2021)
PermalinkRemotely-sensed rip current dynamics and morphological control in high-energy beach environments / Isaac Rodriguez Padilla (2021)
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