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OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier / Quy Thy Truong in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)
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Titre : OSMWatchman: Learning how to detect vandalized contributions in OSM using a Random Forest classifier Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Guillaume Touya
, Auteur ; Cyril de Runz, Auteur
Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] cartographie collaborative
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données localisées des bénévoles
[Termes IGN] OpenStreetMap
[Termes IGN] qualité des donnéesRésumé : (auteur) Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Numéro de notice : A2020-507 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090504 Date de publication en ligne : 22/08/2020 En ligne : https://doi.org/10.3390/ijgi9090504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95682
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - n° 504[article]Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm / Jingwei Hou in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)
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Titre : Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm Type de document : Article/Communication Auteurs : Jingwei Hou, Auteur ; Yixian Du, Auteur Année de publication : 2020 Article en page(s) : pp 71 - 87 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] base de données localisées
[Termes IGN] canalisation
[Termes IGN] Chine
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] occupation du sol
[Termes IGN] réseau de drainage
[Termes IGN] simulation spatiale
[Termes IGN] utilisation du solRésumé : (auteur) This study presents a water accumulation diffusion algorithm to spatially simulate rainstorm-induced waterlogging for people’s lives and property safety. Taking part of Jinfeng District in Yinchuan City, China, as a study area, a storm water management model (SWMM) model is constructed with the aid of geographic information system (GIS) and remote sensing (RS) technologies. GIS is used to divide sub-catchments, generalize drainage system, set parameters, construct spatial geodatabase, and identify flood extents and depths. RS is used to obtain land-use/land-cover information. The water accumulation diffusion algorithm is then designed using the strategies of the dynamic interactions between pipes and surface and between central pixel and its neighbourhood pixels to transform water accumulation volume of sub-catchment into the submerged range and water accumulation depth. Positions, extents, depths, and volumes of water accumulation from pipe network and surface are simulated, respectively. The spatial simulation precisions of rainstorm waterlogging from the pipe network and surface are verified according to the measured and cyber rainstorm data, respectively. The results show that (1) the number of water accumulation nodes increases with the increase of rainfall intensity; (2) urban waterlogging is mainly distributed in the intersects of roads, low depressions and the aged drainage networks; and (3) spatial simulation of urban rainstorm waterlogging based on the GIS, RS, and SWMM techniques and the water accumulation diffusion algorithm is reliable. The results can provide decision-makings to predict rainstorm waterlogging, design drainage network, and construct a sponge city. Numéro de notice : A2020-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2019.1707719 Date de publication en ligne : 06/01/2020 En ligne : https://doi.org/10.1080/19475705.2019.1707719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95891
in Geomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 71 - 87[article]Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)
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Titre : Using OpenStreetMap data and machine learning to generate socio-economic indicators Type de document : Article/Communication Auteurs : Daniel Feldmeyer, Auteur ; Claude Meisch, Auteur ; Holger Sauter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] changement climatique
[Termes IGN] chômage
[Termes IGN] classification par réseau neuronal
[Termes IGN] collectivité territoriale
[Termes IGN] données localisées des bénévoles
[Termes IGN] données socio-économiques
[Termes IGN] inégalité
[Termes IGN] limite administrative
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMapRésumé : (auteur) Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Numéro de notice : A2020-663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090498 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.3390/ijgi9090498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96139
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 16 p.[article]Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
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Titre : Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience Type de document : Article/Communication Auteurs : Yingwei Yan, Auteur ; Chen-Chieh Feng, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1765 - 1791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées des bénévoles
[Termes IGN] GeoWeb
[Termes IGN] littérature
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] presse (media)
[Termes IGN] problème de Dirichlet
[Termes IGN] qualité des données
[Termes IGN] recherche
[Termes IGN] réseau social
[Termes IGN] SIG participatif
[Termes IGN] source de données
[Termes IGN] système d'information géographique
[Termes IGN] Twitter
[Termes IGN] utilisation du sol
[Termes IGN] WikimapiaRésumé : (auteur) More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors. Numéro de notice : A2020-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730848 Date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95623
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1765 - 1791[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2020 (August 2020)
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Titre : A regression model of spatial accuracy prediction for Openstreetmap buildings Type de document : Article/Communication Auteurs : Ibrahim Maidaneh Abdi , Auteur ; Arnaud Le Guilcher
, Auteur ; Ana-Maria Olteanu-Raimond
, Auteur
Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 4 Article en page(s) : pp 39 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] modèle de régression
[Termes IGN] OpenStreetMap
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité des donnéesRésumé : (auteur) Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor. Numéro de notice : A2020-506 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2020-39-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95647
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2020 (August 2020) . - pp 39 - 47[article]SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)
PermalinkCan SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkCNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkComparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkExploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkExtraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkGeometric distortion of historical images for 3D visualization / Evelyn Paiz-Reyes in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkGuided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkLanduse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])
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