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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 23 p. Titre : Los Angeles as a digital place: The geographies of user‐generated content Type de document : Article/Communication Auteurs : Andrea Ballatore, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] centre urbain [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] Foursquare [Termes descripteurs IGN] Los Angeles [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] participation du public [Termes descripteurs IGN] représentation géographique [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] Twitter Résumé : (auteur) Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London. Numéro de notice : A2020-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12600 date de publication en ligne : 02/01/2020 En ligne : https://doi.org/10.1111/tgis.12600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96156 [article]

Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)  

Public [article]  inEarth and space science > vol 7 n° 7 (July 2020) . - 10 p. Titre : Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation Type de document : Article/Communication Auteurs : Michael B. Heflin, Auteur ; Andrea Donnellan, Auteur ; Jay Parker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 10 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] automatisation des processus [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] champ de vitesse [Termes descripteurs IGN] déformation horizontale de la croute terrestre [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] dorsale [Termes descripteurs IGN] faille géologique [Termes descripteurs IGN] modèle géologique [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] sismologie [Termes descripteurs IGN] station GPS [Termes descripteurs IGN] valeur aberrante [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) This paper describes the methods used to estimate positions, velocities, breaks, and seasonalterms from daily Global Navigation Satellite System (GNSS) measurements. Break detection and outlierremoval have been automated so that decades of daily measurements from thousands of stations can beprocessed in a few hours. New measurements are added, and parameters are updated every week. Modelparameters allow separation of interseismic, annual, coseismic, and postseismic signals. Tools availablethrough GeoGateway (http://geo-gateway.org) allow rapid visualization and analysis of these terms forresults that can be subsetted in time or space. Results show highly variable and nonlinear motion for GPSstations in southern California. The variable motion is related to seasonal motions, distributed tectonicmotion, earthquakes, and postseismic motions that can continue for years. In some areas results suggest thatadditional processes are responsible for the observed motions. In general, following earthquakes, stationsreturn to their longterm motions after 2–3 years, though some exceptions occur. The use of the tools showsnonlinear motion in the Salton Trough of southern California related to the 2010 M7.2 El MayorCucapahearthquake, 2012 Brawley earthquake swarm, and a creep event on the Superstition Hills fault in 2017. Numéro de notice : A2020-446 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2019EA000644 date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1029/2019EA000644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95516 [article]

Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 277 - 288 Titre : Delineating and modeling activity space using geotagged social media data Type de document : Article/Communication Auteurs : Lingqian Hu, Auteur ; Zhenhong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2020 Article en page(s) : pp 277 - 288 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] distance [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] logement [Termes descripteurs IGN] loisir [Termes descripteurs IGN] Los Angeles [Termes descripteurs IGN] quartier [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] sport [Termes descripteurs IGN] Twitter [Termes descripteurs IGN] voisinage (topologie) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) It has become increasingly important in spatial equity studies to understand activity spaces – where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users’ home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research. Numéro de notice : A2020-135 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1705187 date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/15230406.2019.1705187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94843 [article]

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Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136 Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse des correspondances [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749 [article]

Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680 Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Oakland (Californie) [Termes descripteurs IGN] régression [Termes descripteurs IGN] semis de points Résumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711 [article]

Improving operational radar rainfall estimates using profiler observations over complex terrain in Northern California / Haonan Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) / Chanwoo Jin in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Subsidence is determined in the heart of the Central Valley using Post Processed Static and Precise Point Positioning techniques / Y. Facio in Journal of applied geodesy, vol 14 n° 1 (January 2020)  

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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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Landsats 1–5 multispectral scanner system sensors radiometric calibration update / Cibele Teixeira-Pinto in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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An exploratory analysis of usability of Flickr tags for land use/land cover attribution / Yingwei Yan in Geo-spatial Information Science, vol 22 n° 1 (March 2019)  

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Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)  

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Exploring uncertainties in terrain feature extraction across multi-scale, multi-feature, and multi-method approaches for variable terrain / Boleslo E. Romero in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)  

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Exploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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