Centre dedocumentation
scientifique

From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping / Tian Lan in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 22 Titre : From data to narratives: Scrutinising the spatial dimensions of social and cultural phenomena through lenses of interactive web mapping Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Oliver O'Brien, Auteur ; James Cheshire, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] carte interactive [Termes IGN] culture [Termes IGN] données démographiques [Termes IGN] données socio-économiques [Termes IGN] impact social [Termes IGN] récit [Termes IGN] Royaume-Uni [Termes IGN] sciences sociales [Termes IGN] web mapping Résumé : (auteur) Modern web mapping techniques have enhanced the storytelling capability of cartography. In this paper, we present our recent development of a web mapping facility that can be used to extract interesting stories and unique insights from a diverse range of socio-economic and demographic variables and indicators, derived from a variety of datasets. We then use three curated narratives to show that online maps are effective ways of interactive storytelling and visualisation, which allow users to tailor their own story maps. We discuss the reasons for the revival of the recent attention to narrative mapping and conclude that our interactive web mapping facility powered by data assets can be employed as an accessible and powerful toolkit, to identify geographic patterns of various social and economic phenomena by social scientists, journalists, policymakers, and the public. Numéro de notice : A2022-541 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-022-00117-x Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1007/s41651-022-00117-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101105 [article]

Integration of GNSS observations with volunteered geographic information for improved navigation performance / Tarek Hassan in Journal of applied geodesy, vol 16 n° 3 (July 2022)  

Public [article]  inJournal of applied geodesy > vol 16 n° 3 (July 2022) . - pp 265 - 277 Titre : Integration of GNSS observations with volunteered geographic information for improved navigation performance Type de document : Article/Communication Auteurs : Tarek Hassan, Auteur ; Tamer Fath-Allah, Auteur ; Mohamed Elhabiby, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 265 - 277 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données GNSS [Termes IGN] données localisées des bénévoles [Termes IGN] Google Earth [Termes IGN] hauteur du bâti [Termes IGN] modélisation 3D [Termes IGN] OpenStreetMap [Termes IGN] positionnement par GNSS [Termes IGN] signal GNSS [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Pedestrian and vehicular navigation relies mainly on Global Navigation Satellite System (GNSS). Even if different navigation systems are integrated, GNSS positioning remains the core of any navigation process as it is the only system capable of providing independent solutions. However, in harsh environments, especially urban ones, GNSS signals are confronted by many obstructions causing the satellite signals to reach the receivers through reflected paths. These No-Line of Sight (NLOS) signals can affect the positioning accuracy significantly. This contribution proposes a new algorithm to detect and exclude these NLOS signals using 3D building models constructed from Volunteered Geographic Information (VGI). OpenStreetMap (OSM) and Google Earth (GE) data are combined to build the 3D models incorporated with GNSS signals in the algorithm. Real field data are used for testing and validation of the presented algorithm and strategy. The accuracy improvement, after exclusion of the NLOS signals, is evaluated employing phase-smoothed code observations. The results show that applying the proposed algorithm can improve the horizontal positioning accuracy remarkably. This improvement reaches 10.72 m, and the Root Mean Square Error (RMSE) drops by 1.64 m (46 % improvement) throughout the epochs with detected NLOS satellites. In addition, the improvement is analyzed in the Along-Track (AT) and Cross-Track (CT) directions. It reaches 6.89 m in the AT direction with a drop of 1.076 m in the RMSE value, while it reaches 8.64 m with a drop of 1.239 m in the RMSE value in the CT direction. Numéro de notice : A2022-496 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2021-0063 Date de publication en ligne : 23/03/2022 En ligne : https://doi.org/10.1515/jag-2021-0063 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100986 [article]

Swipe versus multiple view: a comprehensive analysis using eye-tracking to evaluate user interaction with web maps / Stanislav Popelka in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° 3 (May 2022) . - pp 252 - 270 Titre : Swipe versus multiple view: a comprehensive analysis using eye-tracking to evaluate user interaction with web maps Type de document : Article/Communication Auteurs : Stanislav Popelka, Auteur ; Jaroslav Burian, Auteur ; Marketa Beitlova, Auteur Année de publication : 2022 Article en page(s) : pp 252 - 270 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] ArcGIS online [Termes IGN] carte interactive [Termes IGN] cartographie par internet [Termes IGN] interactivité [Termes IGN] interface web [Termes IGN] oculométrie [Termes IGN] représentation cognitive [Termes IGN] utilisateur civil [Termes IGN] vision [Termes IGN] web mapping [Vedettes matières IGN] Cartologie Résumé : (auteur) The comparison of multiple maps is a common fundamental process used by geographers to explore the world. The most frequently applied interactive methods for the comparison of maps are multiple view and swipe. Swipe allows the user to interactively drag and overlap two different maps. Multiple view is based on the simultaneous side-by-side display of several maps. The current paper presents an analysis of the use of these two map comparison techniques in an Esri environment using an eye-tracking study which involved 25 participants. The participants completed two different tasks which compared land suitability using two or four maps. Based on an analysis of the recorded data, we compared the effectiveness of these methods through the accuracy of answers, the trial duration, and eye-tracking metrics of the individual compositional elements of the interactive maps. Cognitive processing was investigated through the analysis of dynamic areas of interest. This labor-intensive analysis yielded results which could be visualized using sequence charts. Based on these analyses, we concluded that the participants worked more effectively with multiple views, especially in comparing four maps. Working with swipe in the Esri environment is non-intuitive in comparisons of more than two maps. Many participants instead preferred simple toggling between layers instead of interactive swipe comparisons. However, when swipe was used to compare two maps, the method was more efficient, especially during cognitively demanding tasks. Numéro de notice : A2022-293 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.2015721 Date de publication en ligne : 25/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2015721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100343 [article]

Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821 Titre : Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Zhipeng Gui, Auteur ; Jimin Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 799 - 821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] descripteur [Termes IGN] données d'entrainement sans étiquette [Termes IGN] image cartographique [Termes IGN] métadonnées [Termes IGN] projection [Termes IGN] système d'information géographique [Termes IGN] Web Map Service [Termes IGN] web mapping Résumé : (auteur) Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach. Numéro de notice : A2022-258 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1968407 En ligne : https://doi.org/10.1080/13658816.2021.1968407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100231 [article]

Flood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 n° 1 (2022) . - pp 949 - 974 Titre : Flood susceptibility mapping using meta-heuristic algorithms Type de document : Article/Communication Auteurs : Alireza Arabameri, Auteur ; Amir Seyed Danesh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 949 - 974 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] base de données localisées [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] Google Earth [Termes IGN] inondation [Termes IGN] Iran [Termes IGN] optimisation par essaim de particules [Termes IGN] SAGA GIS [Termes IGN] séparateur à vaste marge [Termes IGN] traitement de données localisées [Termes IGN] vulnérabilité [Termes IGN] zone à risque Résumé : (auteur) Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds. Numéro de notice : A2022-300 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2060138 Date de publication en ligne : 11/04/2022 En ligne : https://doi.org/10.1080/19475705.2022.2060138 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100383 [article]

Historical Vltava River valley–various historical sources within web mapping environment / Jiří Krejčí in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Permalink

Building a collaborative online catalogue of geoportals in Brazil / Eduardo Silverio da Silva in Boletim de Ciências Geodésicas, vol 27 n° 4 ([01/12/2021])  

Permalink

Evaluating narrative in geoportals for territorial public policies / Luis Manuel Batista in Cartographica, vol 56 n° 4 (winter 2021)  

Permalink

Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])  

Permalink

Real-time web map construction based on multiple cameras and GIS / Xingguo Zhang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

Permalink

A framework for ecosystem service assessment using GIS interoperability standards / Martin Lacayo in Computers & geosciences, vol 154 (September 2021)  

Permalink

Shore zone classification from ICESat-2 data over Saint Lawrence Island / Huan Xie in Marine geodesy, vol 44 n° 5 (September 2021)  

Permalink

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])  

Permalink

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)  

Permalink

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

Permalink