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Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning 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 1317 - 1342 Titre : Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1317 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] distorsion d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] hauteur du bâti [Termes IGN] image Streetview [Termes IGN] lever tachéométrique [Termes IGN] modèle numérique de surface [Termes IGN] porte Résumé : (auteur) Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas. Numéro de notice : A2022-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1981334 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/13658816.2021.1981334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100970 [article]

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Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 4 (October 2020) . - pp 329 - 342 Titre : Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Cuizhen Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 329 - 342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données [Termes IGN] Kiangsi (Chine) [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] taille du jeu de données Résumé : (auteur) Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. While existing land cover data can be converted to large training sets, the size of training data set needs to be carefully considered. In this paper, we used different portions of a high-resolution land cover map to produce different sizes of training sets to train DCNNs (SegNet and U-Net) and then quantitatively evaluated the impact of training set size on the performance of the trained DCNN. We also introduced a new metric, Edge-ratio, to assess the performance of DCNN in maintaining the boundary of land cover objects. Based on the experiments, we document the relationship between the segmentation accuracy and the size of the training set, as well as the nonstationary accuracies among different land cover types. The findings of this paper can be used to effectively tailor the existing land cover data to training sets, and thus accelerate the assessment and employment of deep learning techniques for high-resolution land cover map extraction. Numéro de notice : A2020-800 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1803402 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1803402 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96723 [article]

Big data computing for geospatial applications / Zhenlong Li (2020)  

Public Titre : Big data computing for geospatial applications Type de document : Monographie Auteurs : Zhenlong Li, Éditeur scientifique ; Wenwu Tang, Éditeur scientifique ; Qunying Huang, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 222 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-245-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatio-temporelle [Termes IGN] cyberinfrastructure [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] informatique en nuage [Termes IGN] métadonnées [Termes IGN] représentation géographique [Termes IGN] réseau sémantique Résumé : (éditeur) The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. Note de contenu : 1- Introduction to Big Data computing for geospatial applications 2- MapReduce-based D-ELT framework to address the challenges of geospatial Big Data 3- High-performance overlay analysis of massive geographic polygons that considers shape complexity in a cloud environment 4- Parallel cellular automata Markov model for land use change prediction over MapReduce framework 5- Terrain analysis in Google Earth Engine: A method adapted for high-gerformance global-scale analysis 6- Integrating geovisual analytics with machine learning for human mobility pattern discovery 7- Social media Big Data mining and spatio-temporal analysis on public emotions for disaster mitigation 8- A novel method of missing road generation in city blocks based on big mobile navigation trajectory data 9- A task-oriented knowledge base for geospatial problem-solving 10- Geographic knowledge graph (GeoKG): A formalized geographic knowledge representation 11- Advanced cyberinfrastructure to enable search of big climate datasets in THREDDS Numéro de notice : 28389 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-245-5 En ligne : https://doi.org/10.3390/books978-3-03943-245-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98688

Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)  

Public [article]  inCartography and Geographic Information Science > vol 46 n° 3 (May 2019) . - pp 228 - 242 Titre : Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level Type de document : Article/Communication Auteurs : Jiang Juqin, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2019 Article en page(s) : pp 228 - 242 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agrégation spatiale [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données démographiques [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] erreur systématique [Termes IGN] Etats-Unis [Termes IGN] géobalise [Termes IGN] régression géographiquement pondérée [Termes IGN] Twitter Résumé : (Auteur) Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties. Numéro de notice : A2019-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1434834 Date de publication en ligne : 09/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1434834 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92338 [article]

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