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The use of Volunteer Geographic Information for producing and maintaining authoritative Land Use and Land Cover data, [Report from] EuroSDR and LandSense Workshop, November 24th - 25th 2020 - Online Conference / Ana-Maria Raimond (2022)  

Public Titre : The use of Volunteer Geographic Information for producing and maintaining authoritative Land Use and Land Cover data, [Report from] EuroSDR and LandSense Workshop, November 24th - 25th 2020 - Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Raimond , Auteur ; Joep Crompvoets, Auteur ; Inian Moorthy, Auteur ; Clément Mallet , Auteur ; Bénédicte Bucher , Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR Workshop report Projets : Landsense / Raimond, Ana-Maria Importance : 39 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] cartographie collaborative [Termes IGN] changement d'occupation du sol [Termes IGN] données localisées des bénévoles [Termes IGN] fusion de données [Termes IGN] intégration de données [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] prototype [Termes IGN] utilisation du sol [Termes IGN] validation des données Résumé : (Auteur) [Introduction] The report refers to the workshop that was organized on behalf of EuroSDR and the LandSense project (24-25 November 2020). LandSense aims to build a citizen observatory for Land Use and Land Cover (LULC) monitoring by proposing innovate technologies for data collection, change detection, data quality assessment and offering tools and systems to empower different communities (e.g., private companies, Non Governmental Organisation, National Mapping Agencies, research, public authorities) to monitor and report on LULC. The workshop was co-organized by the LASTIG laboratory of the University Gustave Eiffel and IGN-ENSG, the French National Mapping agency (Ana-Maria Olteanu-Raimond, Clément Mallet, Bénédicte Bucher), the Katholieke Universiteit Leuven (Joep Crompvoets), the International Institute for Applied Systems Analysis (Inian Moorthy) and EuroSDR. LULC data are necessary for different applications (e.g., urbanization growth, biodiversity conservation, climate change) in monitoring our environment at national, regional and local scales. Different European initiatives such as CORINE Land Cover, Copernicus, Urban Atlas allow the production of LULC data in vector format (i.e. feature-based LULC). The National Mapping Agencies (NMAs) also produce feature based LULC data at regional or national scales based on demand and available resources. The feature-based LULC data are generally cyclically produced every 3 to 6 years, which is not always adequate. Moreover, producing LULC data is costly and a lack of in-situ information can generate incompleteness or inaccuracies. Recent research shows that LULC databases may take advantage of the use of Volunteer Geographic Information (VGI) to produce or improve update LULC data. For example, different approaches allowing to derive LULC data from OpenStreetMap are proposed. In this context the objective of the workshop was to bring together different actors (e.g., National mapping agencies, academic communities, private companies) having experiences in feature?based LULC data production or change detection in order to 1) dress an exhaustive list of the current practices and issues in mapping feature-based LULC data and 2) share innovative approaches allowing to produce, monitor and update LULC data. [...] Note de contenu : INTRODUCTION GENERALE 1. Introduction 1.1 Land Use and Land Cover data: specificities and challenges 1.2 VGI and citizen science for LULC monitoring 2. Session 1: Use of VGI for LULC data production 2.1 National Land Cover and Land Use Information System of Spain (SIOSE)- Coordination, production, maintenance and VGI 2.2 A fusion data approach for integrating VGI to update and enrich authoritative LULC data 2.3 OpenStreetMap for Earth Observation (OSM4EO) land use application and benchmark 2.4 Using OpenStreetMap as a data source for training classifiers to generate LULC maps 3. Session 2: Data collection and validation 3.1 A mapping prototype for land use mapping by land users 3.2 A mobile application for collecting snow data in support to satellite remote sensing 3.3 Global land cover monitoring, validation and participation: experiences from several case studies 4. Session 3: Sustainability 4.1 Crowdsourcing reference data collection for land cover and land use mapping: Findings from Picture Pile and FotoquestGo 4.2 Land Cover Monitoring System with Sentinel-Hub and Python Machine Learning Library eo-learn. Is it possible to build a fast and cost-effective LCMS? 4.3 Regular monitoring of landscape changes with Copernicus data- The German land cover change detection service 4.4 Authentication as a Service - A LandSense contribution to improve the FAIR principle in Citizen Science 5. Conclusion Numéro de notice : 26826 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Rapport sur congrès nature-HAL : DirectOuvrColl/Actes DOI : sans Date de publication en ligne : 09/05/2022 En ligne : https://hal.archives-ouvertes.fr/hal-03662950/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100695

Point-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)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 11 (November 2021) . - n° 735 Titre : Point-of-interest (POI) data validation methods: An urban case study Type de document : Article/Communication Auteurs : Lih Wei Yeow, Auteur ; Raymond Low, Auteur ; Yu Xiang Tan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse spatiale [Termes IGN] données cartographiques [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] erreur de positionnement [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] qualité des données [Termes IGN] Singapour [Termes IGN] validation des données Résumé : (auteur) Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low ( Numéro de notice : A2021-830 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10110735 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.3390/ijgi10110735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98968 [article]

GNSS metadata and data validation in the EUREF Permanent Network / Carine Bruyninx in GPS solutions, vol 23 n° 4 (October 2019)  

Public [article]  inGPS solutions > vol 23 n° 4 (October 2019) Titre : GNSS metadata and data validation in the EUREF Permanent Network Type de document : Article/Communication Auteurs : Carine Bruyninx, Auteur ; Juliette Legrand, Auteur ; András Fabian, Auteur ; Eric Pottiaux, Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] métadonnées géographiques [Termes IGN] qualité des metadonnées [Termes IGN] récepteur GNSS [Termes IGN] réseau permanent EUREF [Termes IGN] station GNSS [Termes IGN] validation des données Résumé : (Auteur) The EUREF Permanent Network (EPN) is a network of continuously operating GNSS stations installed throughout the European continent. The EPN Central Bureau (CB) performs the day-to-day EPN coordination, acts as liaison between station operators, data centers, and analysis centers, and maintains the EPN Information System. Over the last years, the EPN CB has accommodated the enhancements required by the new EU General Data Protection Regulation, new multi-GNSS signals, new RINEX formats, increased usage of real-time GNSS data, and the new GeodesyML metadata exchange format. We will discuss how the EPN CB validates and provides access to EPN station metadata and monitors EPN data sets in terms of availability, latency, and quality to ensure they meet the user requirements. The analysis of 23 years of EPN GNSS data quality checks demonstrates some of the most frequently encountered tracking problems affecting EPN stations, and specific GNSS receiver types, throughout the years. Numéro de notice : A2019-332 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0880-9 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1007/s10291-019-0880-9 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93420 [article]

A crowdsourcing-based game for land cover validation / Maria Antonia Brovelli in Applied geomatics, vol 10 n° 1 (March 2018)  

Public [article]  inApplied geomatics > vol 10 n° 1 (March 2018) . - pp 1 - 11 Titre : A crowdsourcing-based game for land cover validation Type de document : Article/Communication Auteurs : Maria Antonia Brovelli, Auteur ; Irene Celino, Auteur ; Andrea Fiano, Auteur ; Monia Elisa Molinari, Auteur ; Vijaycharan Venkatachalam, Auteur Année de publication : 2018 Article en page(s) : pp 1 - 11 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Côme [Termes IGN] jeu [Termes IGN] occupation du sol [Termes IGN] participation du public [Termes IGN] production participative [Termes IGN] QGIS [Termes IGN] science citoyenne [Termes IGN] validation des données Mots-clés libres : Game With A Purpose Résumé : (Auteur) Land cover datasets are critical environmental information which are becoming increasingly available nowadays as open data. Accuracy of these datasets is key for their use in manifold applications and can be obtained through validation processes, e.g., the intercomparison with other existing land cover data. The results of this procedure usually highlight disagreements between the compared products which should be further analyzed. The presented work has the aim to address this need by proposing an innovative crowdsourcing-based game that engages citizens in validating disagreements between land cover datasets. The game was played during the Free and Open Source Software for Geospatial (FOSS4G) Europe Conference 2015 by the conference participants and allowed to evaluate the disagreements between the GlobeLand30 and the DUSAF land cover datasets on the Como city area (Italy). The results show the feasibility of the proposed approach and the potentiality of gaming in user engagement for land cover validation campaigns. Numéro de notice : A2018-157 Affiliation des auteurs : non IGN Thématique : IMAGERIE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-017-0201-3 Date de publication en ligne : 29/11/2017 En ligne : https://doi.org/10.1007/s12518-017-0201-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89776 [article]

The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 61 - 74 Titre : The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data Type de document : Article/Communication Auteurs : Alby D. Rocha, Auteur ; Thomas A. Groen, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 61 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] complexité [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] modèle de simulation [Termes IGN] optimisation (mathématiques) [Termes IGN] précision [Termes IGN] régression [Termes IGN] validation des données Résumé : (Auteur) The growing number of narrow spectral bands in hyperspectral remote sensing improves the capacity to describe and predict biological processes in ecosystems. But it also poses a challenge to fit empirical models based on such high dimensional data, which often contain correlated and noisy predictors. As sample sizes, to train and validate empirical models, seem not to be increasing at the same rate, overfitting has become a serious concern. Overly complex models lead to overfitting by capturing more than the underlying relationship, and also through fitting random noise in the data. Many regression techniques claim to overcome these problems by using different strategies to constrain complexity, such as limiting the number of terms in the model, by creating latent variables or by shrinking parameter coefficients. This paper is proposing a new method, named Naïve Overfitting Index Selection (NOIS), which makes use of artificially generated spectra, to quantify the relative model overfitting and to select an optimal model complexity supported by the data. The robustness of this new method is assessed by comparing it to a traditional model selection based on cross-validation. The optimal model complexity is determined for seven different regression techniques, such as partial least squares regression, support vector machine, artificial neural network and tree-based regressions using five hyperspectral datasets. The NOIS method selects less complex models, which present accuracies similar to the cross-validation method. The NOIS method reduces the chance of overfitting, thereby avoiding models that present accurate predictions that are only valid for the data used, and too complex to make inferences about the underlying process. Numéro de notice : A2017-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88407 [article]

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