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The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)  

Public Titre : The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data : EuroSDR and LandSense Workshop, November 24th - 25th 2020, Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Olteanu-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 Conférence : VGI4LULC 2020, The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data 24/11/2020 25/11/2020 online OA Proceedings Importance : 40 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] approche participative [Termes IGN] cartographie collaborative [Termes IGN] collecte de données [Termes IGN] Corine Land Cover [Termes IGN] détection de changement [Termes IGN] données localisées des bénévoles [Termes IGN] intégration de données [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] science citoyenne [Termes IGN] utilisation du sol Résumé : (éditeur) 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. 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 : 28680 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_vgi4lulc.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99973

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation / Jingyan Yu in Computers, Environment and Urban Systems, vol 90 (November 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 90 (November 2021) . - n° 101689 Titre : Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation Type de document : Article/Communication Auteurs : Jingyan Yu, Auteur ; Alex Hagen-Zanker, Auteur ; Naratip Santitissadeekorn, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101689 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] Corine Land Cover [Termes IGN] croissance urbaine [Termes IGN] estimation bayesienne [Termes IGN] Grande-Bretagne [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle dynamique Résumé : (auteur) Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration. This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures. The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions. Numéro de notice : A2021-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101689 Date de publication en ligne : 12/08/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98367 [article]

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 14 n° 4 (April 2021)  

Public [article]  inInternational Journal of Digital Earth > vol 14 n° 4 (April 2021) . - pp 480 - 509 Titre : A data fusion-based framework to integrate multi-source VGI in an authoritative land use database Type de document : Article/Communication Auteurs : Lanfa Liu, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Laurence Jolivet , Auteur ; Arnaud Le Bris , Auteur ; Linda M. See, Auteur Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Raimond, Ana-Maria Article en page(s) : pp 480 - 509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'occupation du sol [Termes IGN] base de données localisées de référence [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] fusion de données [Termes IGN] intégration de données [Termes IGN] mise à jour de base de données [Termes IGN] OCS GE [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally. Numéro de notice : A2021-069 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17538947.2020.1842524 Date de publication en ligne : 05/11/2020 En ligne : https://doi.org/10.1080/17538947.2020.1842524 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96522 [article]

Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)  

Public [article]  inRemote sensing > Vol 13 n° 6 (March 2021) . - n° 1060 - 32 p. Titre : Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : AI4GEO / Raimond, Ana-Maria, MAESTRIA / Mallet, Clément Article en page(s) : n° 1060 - 32 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement sémantique [Termes IGN] apprentissage dirigé [Termes IGN] carte d'occupation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Corine Land Cover [Termes IGN] détection de changement [Termes IGN] image à haute résolution [Termes IGN] inférence [Termes IGN] mise à jour automatique [Termes IGN] mise à jour de base de données Résumé : (Auteur) CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC. Numéro de notice : A2021-244 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13061060 Date de publication en ligne : 11/03/2021 En ligne : https://dx.doi.org/10.3390/rs13061060 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97311 [article]

Contextual land-cover map translation with semantic segmentation / Luc Baudoux (2021)  

Public Titre : Contextual land-cover map translation with semantic segmentation Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Mallet, Clément Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 2488 - 2491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de données d'occupation du sol [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Corine Land Cover [Termes IGN] France (administrative) [Termes IGN] segmentation sémantique [Termes IGN] transformation géométrique [Termes IGN] translation Résumé : (auteur) This paper presents a framework for translating a land-cover map into another one in a supervised way. This links to numerous applications (updating, completion, etc.). Conversely to existing approaches, we jointly perform spatial and semantic transformation without any prior knowledge. The proposed method assumes that: i) examples of the source and target maps already exist, ii) the spatial resolution of the source map is equal or higher than the target one. The translation is performed using an asymmetric Convolutional Neural Network with positional encoding. Experimental results show the effectiveness of the method in retrieving a yearly version of Corine Land Cover (CLC) at country-scale (France) using an existing high-resolution map and with similar accuracy than existing CLC maps (~80%). Numéro de notice : C2021-049 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9553693 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9553693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99414

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Current challenges in operational very high resolution land-cover mapping / Clément Mallet (2020)  

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Information Géographique Volontaire, vers un usage conjoint avec l’information géographique institutionnelle / Ana-Maria Olteanu-Raimond (2020)  

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New method for environmental monitoring in armed conflict zones: a case study of Syria / Samira Mobaied in Environmental Monitoring and Assessment, vol 191 n° 11 (November 2019)  

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Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

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International workshop on large scale land cover mapping from remote sensing, 3 décembre 2019 / Mathieu Fauvel (2019)

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Rapport d'activité 2018 de l'Institut National de l'Information Géographique et Forestière IGN, 1. Les missions et activités de l'IGN / Institut national de l'information géographique et forestière (2012 -) (2019)

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A new generation of the United States National Land Cover Database : Requirements, research priorities, design, and implementation strategies / Limin Yang in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Exploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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