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occupation du sol
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Espace, organisation de l' Utilisation du sol Politique foncière Sol, Occupation du Sols -- Utilisation Sols -- Utilisation Terrains -- Utilisation Terrains, Utilisation des Utilisation du sol Espace (économie politique) >> Aménagement du territoire Paysage -- Évaluation Syndrome NIMBY >>Terme(s) spécifique(s) : Améliorations foncières Cadastres Décharges contrôlées Immobilier Photographie aérienne en utilisation du sol Politique forestière Promotion immobilière Propriété foncière Propriété immobilière -- Acquisition par l'Administration Terres publiques Zones d'aménagement différé Equiv. LCSH : Land use Domaine(s) : 330 |
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Machine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)
Titre : Machine learning and geographic information systems for large-scale mapping of renewable energy potential Type de document : Thèse/HDR Auteurs : Dan Assouline, Auteur ; Jean-Louis Scartezzini, Directeur de thèse ; Nahid Mohajeri Pour Rayeni, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2019 Importance : 294 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour l'obtention du grade de Docteur ès Sciences à l'Ecole Polytechnique Fédérale de LausanneLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données météorologiques
[Termes IGN] données topographiques
[Termes IGN] énergie éolienne
[Termes IGN] énergie géothermique
[Termes IGN] énergie renouvelable
[Termes IGN] énergie solaire
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] occupation du sol
[Termes IGN] prédiction
[Termes IGN] SuisseIndex. décimale : THESE Thèses et HDR Résumé : (auteur) A promising pathway to follow in order to reach sustainable development goals is an increased
reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the demand loads in locations of interest. In particular, large-scale supply estimation studies are needed in order to evaluate areas of high potential for each type of energy source for a particular region, and allow for the elaboration of efficient global energy strategies. In Switzerland, the “Energy Strategy 2050”, initiated in 2011 by the Swiss Federal Council, sets an example with the ambitious goal of reaching a 50-80% reduction of CO2 emissions by the year 2050, with a clear course of action: phasing-out nuclear power, improving energy efficiency, and greatly increasing the use of renewables. This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). For each of the three considered energy sources, an adapted methodology is suggested to assess its large-scale potential, by estimating multiple variables of interest (with a suitable time resolution, e.g. monthly or yearly), using widely available data, and combining these variables into potential values. These latter estimated variables, dictating the potential, include: (i) the monthly wind speed, and rural and urban topographic/obstacle configuration for wind energy, (ii) the ground thermal conductivity, volumetric heat capacity and monthly temperature gradient for geothermal energy, (iii) the monthly solar radiation, available area for PV panels over rooftops, geometrical characteristics of rooftops and monthly shading factors over rooftops for solar energy. The use of Machine Learning algorithms (notably Support Vector Machines and Random Forests) allows, given adequate features and training data (examples for some locations), for the prediction of the latter variables at unknown locations, along with the uncertainty attached to the predictions. In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies.
Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies. Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.Note de contenu : 1- Introduction
2- Machine Learning
3- Theory and modeling of renewable energy systems
4- Wind energy: a theoretical potential estimation
5- Very shallow geothermal energy: a theoretical potential estimation
6- Solar energy: a technical potential estimation at commune scale
7- Solar energy: an improved potential estimation at pixel scale
8- ConclusionNuméro de notice : 25797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences : EPFLausanne : 2019 nature-HAL : Thèse DOI : sans En ligne : https://infoscience.epfl.ch/record/264971?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95038 Multimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)
Titre de série : Multimodal scene understanding: algorithms, applications and deep learning, ch. 11 Titre : Decision fusion of remote-sensing data for land cover classification Type de document : Chapitre/Contribution Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Walid Ouerghemmi , Auteur ; Cyril Wendl, Auteur ; Tristan Postadjian , Auteur ; Anne Puissant, Auteur ; Clément Mallet , Auteur Editeur : Londres, New York : Academic Press Année de publication : 2019 Importance : pp 341 - 382 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] fusion de données multisource
[Termes IGN] image à très haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] occupation du sol
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (Auteur) Very high spatial resolution (VHR) multispectral imagery enables a fine delineation of objects and a possible use of texture information. Other sensors provide a lower spatial resolution but an enhanced spectral or temporal information, permitting one to consider richer land cover semantics. So as to benefit from the complementary characteristics of these multimodal sources, a decision late fusion scheme is proposed. This makes it possible to benefit from the full capacities of each sensor, while dealing with both semantic and spatial uncertainties. The different remote-sensing modalities are first classified independently. Separate class membership maps are calculated and then merged at the pixel level, using decision fusion rules. A final label map is obtained from a global regularization scheme in order to deal with spatial uncertainties while conserving the contrasts from the initial images. It relies on a probabilistic graphical model involving a fit-to-data term related to merged class membership measures and an image-based contrast-sensitive regularization term. Conflict between sources can also be integrated into this scheme. Two experimental cases are presented. In the first case one considers the fusion of VHR multispectral imagery with lower spatial resolution hyperspectral imagery for fine-grained land cover classification problem in dense urban areas. In the second case one uses SPOT 6/7 satellite imagery and Sentinel-2 time series to extract urban area footprints through a two-step process: classifications are first merged in order to detect building objects, from which a urban area prior probability is derived and eventually merged to Sentinel-2 classification output for urban footprint detection. Numéro de notice : H2019-002 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-0-12-817358-9.00017-2 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/B978-0-12-817358-9.00017-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93303 Retrieving relevant land cover and land use data to study urban climate change / Bénédicte Bucher (2019)
Titre : Retrieving relevant land cover and land use data to study urban climate change Type de document : Article/Communication Auteurs : Bénédicte Bucher , Auteur ; Marie-Dominique Van Damme , Auteur ; Stephane Garcia , Auteur Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Projets : URCLIM / Masson, Valéry Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] changement climatique
[Termes IGN] climat urbain
[Termes IGN] image Landsat
[Termes IGN] métadonnées géographiques
[Termes IGN] occupation du sol
[Termes IGN] pertinence
[Termes IGN] plateforme collaborative
[Termes IGN] recherche d'information géographique
[Termes IGN] spécification
[Termes IGN] spécification de processus
[Termes IGN] utilisation du solRésumé : (auteur) The study of urban local phenomena related to climate, like heat islands, road icing, streets overflow during high precipitation events or air pollution, is necessary to develop efficient adaptation strategies to climate change. The URCLIM project studies more specifically urban climate knowledge production and services design. It is funded by the "European Research Area for Climate Services" that targets "the user-driven development, translation and transfer of climate knowledge to researchers and decision—makers in policy and business [..] as well as guidance in the use of climate knowledge." Climate scientists model interactions between meteorological phenomena (wind, moisture, temperature) described at a given scale and the surface of earth described at a finer scale in order to calculate finer meteorological phenomena, e.g. temperature variations depending on trees in cities. The climate community designs such generic canopy models adapted for a set of similar places. To obtain land data required to feed these canopy models, instead of each team producing ad hoc land data on his experimental site, this community has developed a joint approach: 1) agree on common formal specifications of such land models, also known as Local climate Zones, 2) design a production procedure of such Local climate zones data affordable by the community itself. The World Urban Database and Access Portal Tools, WUDAPT support collaborative production of Local climate Zones level 0 (resolution from 500m to 1km) based on Landsat satellite imagery. Producing Local climate Zones level 1 (50 to 100 meters), requires other sources related to buildings and vegetation (Masson et al. 2019). This requires discovering and reusing heterogeneous spatial data whereas there is neither one search engine nor a set of well identified catalogues that can be searched with user-oriented query words. This presentation will concentrate firstly on analyzing what are the relevance criteria from the urban climate scientist perspective to retrieve an existing urban land model or to produce it. We consider for example an accessibility criterion as well as an extrapolation criterion. Second we review the contribution of available metadata and ontologies to make proper recommendations to this scientist who wishes to design an urban land model for his specific study. Important metadata are: features catalogues, spatial and temporal coverage, temporal, geometric and semantic resolutions and accuracies. Last we demonstrate a metadata curation process based on the URCLIM infolab, a collaborative metadata platform (Bucher and Van Damme 2018). Numéro de notice : C2019-066 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97021
Titre : Soil moisture assessment in grasslands using optical remote sensing data Type de document : Mémoire Auteurs : Luc Beraud, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2019 Importance : 50 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spectrale
[Termes IGN] corrélation
[Termes IGN] couvert végétal
[Termes IGN] données de terrain
[Termes IGN] humidité du sol
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice d'humidité
[Termes IGN] indice de végétation
[Termes IGN] prairie
[Termes IGN] radiométrieIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Cette étude évalue la possibilité d’estimer l’humidité du sol des prairies via l’utilisation de données optiques de télédétection satellitaire. Les images satellites utilisées sont issues de la mission Sentinel-2 et permettent une évaluation de l’humidité du sol à une résolution d’environ vingt mètres. Des échantillons de sols ont été collectés dans différentes prairies pour établir des données de référence d’humidité du sol. Puis, des liens entre l’humidité des sols mesurée et la radiométrie des prairies ont été recherchés via l’emploi d’indices optiques et de méthodes statistiques de corrélation avec les observations et les mesures in situ réalisées. Cependant, la densité du couvert végétal des prairies ajoute une couche d’incertitudes du fait de l’influence de nombreux paramètres de végétation. Vingt indices optiques ont été utilisés afin de définir expérimentalement les plus appropriés. A l’issue du projet, la meilleure corrélation obtenue a un score R2 de 0.9 avec 11 point de référence. Les résultats ont permis de réaliser une classification de l’estimation de l’humidité des sols. Ainsi, les résultats sont prometteurs et donnent une bonne corrélation entre l’humidité des sols pour le jeu de données d’une acquisition terrain et la radiométrie des images satellites. Cependant, les autres acquisitions terrain ne permettent pas d’obtenir une telle corrélation et soulignent la nécessité de développer une nouvelle méthode réduisant l’impact une nouvelle méthode des autres facteurs qui changent la radiométrie optique de la végétation. Note de contenu : INTRODUCTION
1. Subject and context presentation
1.1 Setting and objectives
1.2 State of the research
1.3 Data and methods
2. Data collection and processing
2.1 Processing overview
2.2 In situ data
2.3 Image processing
2.4 Data processing for the statistical analysis
3. Statistical analysis
3.1 Raw band assessment
3.2 Indices assessment
3.3 Conclusion
4. Retrieval of soil moisture
4.1 Data preprocessing
4.2 Machine learning
CONCLUSION
ANNEXES :
A. Indices
B. Fieldworks
C. Soil moisture regressions
D. Processing steps: from raw data to classificationNuméro de notice : 26103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Institute for Environmental Solutions Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93847 Documents numériques
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Soil moisture assessment in grasslands... - pdf auteurAdobe Acrobat PDF The difficult way towards Land cover and land use data harmonization across scales, space and time in Europe / Dominique Laurent (2019)
Titre : The difficult way towards Land cover and land use data harmonization across scales, space and time in Europe Type de document : Article/Communication Auteurs : Dominique Laurent , Auteur Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Projets : TimeMachine / Gouet-Brunet, Valérie Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] harmonisation des données
[Termes IGN] infrastructure européenne de données localisées
[Termes IGN] INSPIRE
[Termes IGN] métamodèle
[Termes IGN] occupation du sol
[Termes IGN] utilisation du solMots-clés libres : Knowledge Exchange Network Résumé : (auteur) Describing land, through its physical properties and functional characteristics, to support decision from local to global level, in particular to observe land evolution through time, at a sustainable cost for society is a domain with many challenges and opportunities. This is the domain of land cover and land use data design which receives attention from a vast community, from raw data providers (satellite imagery, in situ data) to data curators and integrators as well as users. One of the first attempts to harmonize Land Cover and Land Use data has been the INSPIRE Directive (voted in 2007) that aims to provide the legal framework for achieving a European Spatial Data Infrastructure where the Commission could reuse national data used by members for their national policies. Among its Implementing Rules, interoperability is addressed through the definition of common data models for land cover and for land use information. INSPIRE being based on existing data, these models have been defined to be quite flexible. On the one hand, this European legal context and the new technical opportunities may push data producers to design new land cover and land use products, with for example more concern for European reusability of national products. On the other hand, land cover and land use data are often used to compute evolution indicators, which requires stable enough or at least comparable specifications; which rather push data providers to stick to former data specification. More recently, UN-GGIM: Europe (United Nations initiative on Global Geographic Information Management) has set up a Working Group on spatial data the most useful to analyze, achieve or monitor the Sustainable Development Goals, called core data. This group defines priorities for the production of new data or the enhancement of existing one. Land Cover and Land Use are identified as core data themes. The EuroGeographics INSPIRE KEN (Knowledge Exchange Network) and EuroSDR organised a workshop in November 2017 on this topic: how to make the most of available technologies (in terms of precision, accuracy and cost) as well as how to achieve products comparability and reusability across scales, space and time. Main conclusion was that quite diverse national practices must be accounted, though the concept of separating land cover and land use was widely adopted. Besides, attendees express the need to connect to new communities: deep learning to cope with big data, and communities studying the surveyed phenomena to integrate more domain knowledge in land cover and land use surveying process. Last, meta-models like EAGLE supporting the comparison of classifications were recognized as a key SDI component.
The presentation will remind why data harmonization is useful, it will provide an overview of what has been achieved and explain the remaining difficulties.Numéro de notice : C2019-068 Affiliation des auteurs : IGN (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97023 Urban growth simulations in order to represent the impacts of constructions and environmental constraints on urban sprawl / Mojtaba Eslahi (2019)PermalinkDesigning an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)PermalinkA 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)PermalinkUrban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)PermalinkComparing historical and contemporary maps : a methodological framework for a cartographic map comparison applied to Swiss maps / Christin Loran in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)PermalinkAn experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency / Ana-Maria Olteanu-Raimond in Land, vol 7 n° 3 (September 2018)PermalinkAssessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)PermalinkImprovement of countrywide vegetation mapping over Japan and comparison to existing maps / Ram C. Sharma in Advances in Remote Sensing, vol 7 n° 3 (September 2018)PermalinkAlgorithm of land cover spatial data processing for the local flood risk mapping / Monika Siejka in Survey review, vol 50 n° 362 (August 2018)Permalink