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A framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1276 - 1291 Titre : A framework for classification of volunteered geographic data based on user’s need Type de document : Article/Communication Auteurs : Nazila Mohammadi, Auteur ; Amin Sedaghat, Auteur Année de publication : 2021 Article en page(s) : pp 1276 - 1291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] indicateur de qualité [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] zone urbaine Résumé : (auteur) VGI is an attractive source of data, but the quality assurance limits its usages. This study proposes a framework to estimate the quality of the VGI and to classify them based on the user’s need. For this purpose, a set of properties is defined to describe the data in various aspects. The principal component analysis (PCA) method is applied to reach a new set of uncorrelated indicators (UI). Volunteered data is classified based on the user’s need and takes a quality index (QI). UI and QI values are used to train the ANN. Finally, the trained ANN determines the output of the network in a way that returns QI using the UI as inputs. The proposed method was applied to estimate the quality classes of VGI in a part of an urban area. According to the results of the confusion matrix, the total accuracy of the proposed framework was 81.6%. Numéro de notice : A2021-436 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1641562 date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1641562 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97806 [article]

Towards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101632 Titre : Towards generating network of bikeways from Mapillary data Type de document : Article/Communication Auteurs : Xuan Ding, Auteur ; Hongchao Fan, Auteur ; Jianya Gong, Auteur Année de publication : 2021 Article en page(s) : n° 101632 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] cycliste [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] Mapillary [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] Suède [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data. Numéro de notice : A2021-432 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101632 date de publication en ligne : 17/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97798 [article]

Crowdsourcing of popular toponyms: How to collect and preserve toponyms in spoken use / Daniel Vrbik in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 303 Titre : Crowdsourcing of popular toponyms: How to collect and preserve toponyms in spoken use Type de document : Article/Communication Auteurs : Daniel Vrbik, Auteur ; Václav Lábus, Auteur Année de publication : 2021 Article en page(s) : n° 303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] normalisation [Termes descripteurs IGN] République Tchèque [Termes descripteurs IGN] web mapping [Termes descripteurs IGN] zone urbaine Résumé : (auteur) The article presents a process of collecting unstandardized toponyms, in particular urbanonyms (place names denoting objects located in the cadastre of the city), within the territory of two municipalities in the Czech Republic. The collecting process was performed in two phases by crowdsourcing, using a web map application created especially for this purpose. In the first phase (October 2019–September 2020) it was collecting as many unstandardized toponyms as possible. In the second phase (October 2020–January 2021) we focused on the degree of the knowledge of these toponyms among the population living within the studied territory. The interest on the side of the general public was surprising in both phases. In the first phase, over five hundred respondents submitted more than two and a half thousand place names, most of them during the first two weeks. More than nine hundred respondents actively participated in the second phase, thanks to which we received an average of 200 responses for each place name. As regards the motivation of the public, it was most often altruism, patriotism, and curiosity that stimulated them; in the second phase, the element of gamification, embedded into the map application, also had a positive effect. The collected data can be used, for instance, in the activities of local authorities in the process of standardization of place names or as reference data for maps used within the integrated rescue system. Numéro de notice : A2021-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10050303 date de publication en ligne : 05/05/2021 En ligne : https://doi.org/10.3390/ijgi10050303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97676 [article]

Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 339-348 Titre : Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery Type de document : Article/Communication Auteurs : Neema Nicodemus Lyimo, Auteur ; Fang Luo, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur Année de publication : 2021 Article en page(s) : pp 339-348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] corrélation d'images [Termes descripteurs IGN] distance euclidienne [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données ouvertes [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] système à base de connaissances [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification. Numéro de notice : A2021-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.339 date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.339 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97695 [article]

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Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101605 Titre : Understanding collective human movement dynamics during large-scale events using big geosocial data analytics Type de document : Article/Communication Auteurs : Junchuan Fan, Auteur ; Kathleen Stewart, Auteur Année de publication : 2021 Article en page(s) : n° 101605 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] dynamique spatiale [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] éclipse solaire [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] événement [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] migration humaine [Termes descripteurs IGN] mobilité territoriale [Termes descripteurs IGN] téléphonie mobile Résumé : (auteur) Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes. Numéro de notice : A2021-275 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101605 date de publication en ligne : 05/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101605 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97358 [article]

1996–2017 GPS position time series, velocities and quality measures for the CORS Network / Jarir Saleh in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

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An 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)  

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Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

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Horizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)  

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Les bornes sortent de l'oubli / Anonyme in Géomètre, n° 2189 (mars 2021)

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Crowdsourcing without data bias: Building a quality assurance system for air pollution symptom mapping / Marta Samulowska in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

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A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol inconnu ([01/02/2021])  

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A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

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Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data / Yi Qiang in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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