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Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)
[article]
Titre : Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis Type de document : Article/Communication Auteurs : Kai Zhang, Auteur ; Zhen Qian, Auteur ; Yue Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] cartographie du bruit
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] image Streetview
[Termes IGN] lutte contre le bruit
[Termes IGN] milieu urbain
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] pollution acoustique
[Termes IGN] trafic routier
[Termes IGN] ville durableRésumé : (auteur) Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs). Numéro de notice : A2022-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103598 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100167
in Sustainable Cities and Society > vol 78 (March 2022) . - n° 103598[article]Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)
[article]
Titre : Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method Type de document : Article/Communication Auteurs : Yijiang Zhao, Auteur ; Wentao Yang, Auteur ; Yizhi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 236 - 258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Açores, archipel des
[Termes IGN] algorithme glouton
[Termes IGN] données localisées des bénévoles
[Termes IGN] étiquette
[Termes IGN] géobalise
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routierRésumé : (auteur) Numerous studies have shown that OpenStreetMap (OSM) data can achieve high positional quality. However, the thematic attributes of OSM objects can be modified several times, which has a large impact on semantic heterogeneity. Identifying transition patterns within OSM feature classes is an important preliminary step for the tag recommendation algorithm, which can reduce the number of modifications and enhance the efficiency of OSM data updates. In this article, we propose an approach for discovering transition patterns among OSM feature classes. We first produced the transition matrix of feature classes and then developed a graph. Next, the Louvain method for community detection was utilized to cluster the feature classes. OSM data from Indiana, USA, and the Azores, Portugal, were used for our experiments. Some transition patterns were discovered: (1) many feature classes with the most transitions are the same in both datasets and most transitions occur in road-related feature classes; (2) people tend to tag general classes if they are unsure of the specific classes of tagged objects; and (3) most class transitions occurred as a result of volunteers improving the specificity and precision of feature classes. Moreover, consistently confusing concept pairs were identified. Numéro de notice : A2022-178 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12843 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1111/tgis.12843 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99835
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 236 - 258[article]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)
[article]
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]An 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)
[article]
Titre : An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web Type de document : Article/Communication Auteurs : Abdulkadir Memduhoglu, Auteur ; Melih Basaraner, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement géométrique
[Termes IGN] approche participative
[Termes IGN] bâtiment
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multiéchelles
[Termes IGN] Istanbul (Turquie)
[Termes IGN] ontologie
[Termes IGN] OpenStreetMap
[Termes IGN] SWRL
[Termes IGN] web des données
[Termes IGN] web sémantique
[Termes IGN] zone urbaineRésumé : (auteur) The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users. Numéro de notice : A2022-016 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1952108 Date de publication en ligne : 24/08/2021 En ligne : https://doi.org/10.1080/15230406.2021.1952108 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99147
in Cartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 1 - 17[article]Réservation
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Titre : Attention-based vandalism detection in OpenStreetMap Type de document : Article/Communication Auteurs : Nicolas Tempelmeier, Auteur ; Elena Demidova, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2022 Conférence : WWW 2022, ACM Web Conference 2022 25/04/2022 29/04/2022 Lyon online France Proceedings ACM Importance : pp 643 - 651 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection d'anomalie
[Termes IGN] fiabilité des données
[Termes IGN] historique des données
[Termes IGN] OpenStreetMapMots-clés libres : vandalisme Résumé : (auteur) OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid. Numéro de notice : C2022-008 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.1145/3485447.3512224 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1145/3485447.3512224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100493 CrossroadsDescriber, automatic textual description of OpenStreetMap intersections / Jérémy Kalsron (2022)PermalinkDevelopment of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)PermalinkÉvaluation de la qualité des données géographiques d'OpenStreetMap à l'aide des méthodes d'apprentissage automatique : cas de la République de Djibouti / Ibrahim Maidaneh Abdi (2022)PermalinkGenerating geographical location descriptions with spatial templates: a salient toponym driven approach / Mark M. Hall in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkPhotogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkThe use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)PermalinkPoint-of-interest (POI) data validation methods: An urban case study / Lih Wei Yeow in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkImpact of travel time uncertainties on modeling of spatial accessibility: a comparison of street data sources / Yan Lin in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)PermalinkAssessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])PermalinkBenford’s law and geographical information – the example of OpenStreetMap / Franz-Benjamin Mocnik in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkTowards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkA framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])PermalinkTowards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases / Johanna Guth in Journal of Spatial Information Science (JoSIS), n° 22 (2021)PermalinkDecision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 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)PermalinkPermalinkImports massifs de données dans OpenStreetMap : un phénomène en plein essor / Mamadou Bailo Balde (2021)PermalinkMachine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (2021)PermalinkPermalink