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scientifique

Quality assessment of volunteered geographic information for outdoor activities: an analysis of OpenStreetMap data for names of peaks in Japan / Jun Yamashita in Geo-spatial Information Science, vol 26 n° inconnu ([01/08/2023])  

Public [article]  inGeo-spatial Information Science > vol 26 n° inconnu [01/08/2023] Titre : Quality assessment of volunteered geographic information for outdoor activities: an analysis of OpenStreetMap data for names of peaks in Japan Type de document : Article/Communication Auteurs : Jun Yamashita, Auteur ; Toshikazu Seto, Auteur ; Nobusuke Iwasaki, Auteur ; Yuichiro Nishimura, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] données localisées des bénévoles [Termes IGN] Japon [Termes IGN] montagne [Termes IGN] OpenStreetMap [Termes IGN] oronymie [Termes IGN] qualité des données Résumé : (auteur) Geographical studies of outdoor activities have increased in recent years with the rise in popularity of these activities worldwide, including in Japan. Volunteered geographic information (VGI) is a key tool for organizing outdoor activities as it offers a means to determine the locational information and names of places. To evaluate the quality of VGI, geospatial data generated by land survey agencies and other VGI are often utilized as reference data. However, since these reference data may not be available, other methods are necessary to assure the quality of VGI. In this study, we examined five trust indicators based on the inherent characteristics of VGI through an empirical case study. We used mountain names extracted from OpenStreetMap in Japan as data because there were almost no other VGI in the vicinity. As a result, we isolated three trust indicators, namely versions, users, and tag corrections, to examine the thematic accuracy of VGI because these were the only statistically significant indicators. However, we found that the prediction rate of thematic accuracy was very low. To improve thematic accuracy, this study recommends using the most accurate versions, applying correctly given tags, and considering the motivations and characteristics of the VGI contributors. Numéro de notice : A2022-611 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2022.2085188 Date de publication en ligne : 01/07/2022 En ligne : https://doi.org/10.1080/10095020.2022.2085188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101365 [article]

Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031 Titre : Towards global scale segmentation with OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Munazza Usmani, Auteur ; Maurizio Napolitano, Auteur ; Francesca Bovolo, Auteur Année de publication : 2023 Article en page(s) : n° 100031 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bâtiment [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données localisées des bénévoles [Termes IGN] image à haute résolution [Termes IGN] information sémantique [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol Résumé : (auteur) Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications. Numéro de notice : A2023-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100031 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102807 [article]

SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921 Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018]. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] apprentissage automatique [Termes IGN] boosting adapté [Termes IGN] cartographie urbaine [Termes IGN] Chine [Termes IGN] détection du bâti [Termes IGN] données localisées des bénévoles [Termes IGN] image à très haute résolution [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] représentation spatiale [Termes IGN] zone urbaine Résumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504 [article]

Who owns the map? Data sovereignty and government spatial data collection, use, and dissemination / Peter A. Johnson in Transactions in GIS, vol 27 n° 1 (February 2023)  

Public [article]  inTransactions in GIS > vol 27 n° 1 (February 2023) . - pp 275 - 289 Titre : Who owns the map? Data sovereignty and government spatial data collection, use, and dissemination Type de document : Article/Communication Auteurs : Peter A. Johnson, Auteur ; Teresa Scassa, Auteur Année de publication : 2023 Article en page(s) : pp 275 - 289 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] carte [Termes IGN] collecte de données [Termes IGN] diffusion de données [Termes IGN] domaine public [Termes IGN] données localisées des bénévoles [Termes IGN] données spatiotemporelles [Termes IGN] droit d'auteur [Termes IGN] OpenStreetMap [Termes IGN] planification [Termes IGN] pouvoirs publics [Termes IGN] source de données [Termes IGN] statut juridique Résumé : (auteur) Maps, created through the collection, assembly, and analysis of spatial data are used to support government planning and decision-making. Traditionally, spatial data used to create maps are collected, controlled, and disseminated by government, although over time, this role has shifted. This shift has been driven by the availability of alternate sources of data collected by private sector companies, and data contributed by volunteers to open mapping platforms, such as OpenStreetMap. In theorizing this shift, we provide examples of how governments use data sovereignty as a tool to shape spatial data collection, use, and sharing. We frame four models of how governments may navigate shifting spatial data sovereignty regimes; first, with government retaining complete control over data collection; second, with government contracting a third party to provide specific data collection services, but with data ownership and dissemination responsibilities resting with government; third, with government purchasing data under terms of access set by third party data collectors, who disseminate data to several parties, and finally, with government retreating from or relinquishing data sovereignty altogether. Within this rapidly changing landscape of data providers, we propose that governments must consider how to address data sovereignty concerns to retain their ability to control data use in the public interest. Numéro de notice : A2023-134 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.13024 Date de publication en ligne : 22/01/2023 En ligne : https://doi.org/10.1111/tgis.13024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102680 [article]

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

Public [article]  inSustainable Cities and Society > vol 89 (February 2023) . - n° 104337 Titre : Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities Type de document : Article/Communication Auteurs : Pavlos Tsagkis, Auteur ; Efthimios Bakogiannis, Auteur ; Alexandros Nikitas, Auteur Année de publication : 2023 Article en page(s) : n° 104337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] Corine (base de données) [Termes IGN] croissance urbaine [Termes IGN] données localisées libres [Termes IGN] étalement urbain [Termes IGN] Grèce [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle orienté agent [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. Numéro de notice : A2023-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104337 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102486 [article]

Analysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)  

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Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

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Measuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data / Tao Wu in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

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Open mapping towards sustainable development goals / Patricia Solís (2023)  

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Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)  

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LinkClimate: An interoperable knowledge graph platform for climate data / Jiantao Wu in Computers & geosciences, vol 169 (December 2022)  

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Semantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)  

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An unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

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A new spatial database framework for pedestrian indoor navigation based on the OpenStreetMap tag information / Gift Dumedah in Transactions in GIS, vol 26 n° 7 (November 2022)  

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Application of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

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