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Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)  

Public [article]  inRemote sensing > vol 11 n° 18 (September 2019) . - n° 2164 Titre : Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Arnaud Le Bris , Auteur ; Christiane Weber, Auteur Année de publication : 2019 Projets : HYEP / Weber, Christiane Article en page(s) : n° 2164 Note générale : bibliographie This paper constitutes a substantial extension of: https://doi.org/10.1109/IGARSS.2018.8518204 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] détection d'objet [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] panneau photovoltaïque [Termes IGN] zone urbaine Résumé : (auteur) High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Numéro de notice : A2019-430 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11182164 Date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.3390/rs11182164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93739 [article]

Machine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)  

Public 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 Lausanne Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] classification dirigée [Termes IGN] classification par forêts aléatoires [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] Suisse 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- Conclusion Numéro 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

LiDAR, a technology to assist with smart cities and climate change resilience: a case study in an urban metropolis / Ryan Garnett in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 5 (May 2018) Titre : LiDAR, a technology to assist with smart cities and climate change resilience: a case study in an urban metropolis Type de document : Article/Communication Auteurs : Ryan Garnett, Auteur ; Matthew D. Adams, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bassin hydrographique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] eau pluviale [Termes IGN] énergie solaire [Termes IGN] métropole [Termes IGN] modèle numérique de terrain [Termes IGN] secours d'urgence [Termes IGN] Toronto Résumé : (Auteur) In this paper, we demonstrate three unique use cases of LiDAR data and processing, which can be implemented in an urban metropolis to determine the challenges that are associated with climate change. LiDAR data for the City of Toronto were collected in April 2015 with a density of 10 points/m2. We utilized both a digital terrain model and a bare earth digital elevation model in this work. The first case study estimated storm water, in which we compared flow accumulation values and catchment areas generated with a 20-m DEM and a 1-m LiDAR DEM. The finer resolution DEM demonstrated that the urban street features play a significant role in flow accumulation by directing flows. Urban catchment areas were found to occur on spatial scales that were smaller than the 20-m DEM cell size. For the second case study, the solar potential in the City of Toronto was calculated based on the slope and aspect of each land parcel. According to area, 56% of the city was found to have high solar potential, with 33% and 11% having medium and low solar potential. For the third case study, we calculated the building heights for 16,715 high-rise buildings in Toronto, which were combined with ambulance and fire emergency response times required to reach the base of the building. All buildings that had more than 17 stories were within a 5-min response time for both fire and ambulance services. Only 79% and 88% of these buildings were within a 3-min response time for ambulance and fire emergencies, respectively. LiDAR data provides a highly detailed record of the built urban environment and can provide support in the planning and assessment of climate change resilience activities. Numéro de notice : A2018-343 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7050161 Date de publication en ligne : 24/04/2018 En ligne : https://doi.org/10.10.3390/ijgi7050161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90565 [article]

n° 35 - mai 2018 - Chiffres clés des énergies renouvelables, édition 2018 (Bulletin de Datalab) / CGDD Commissariat Général au Développement Durable  

Public [n° ou bulletin]  est un bulletin de Datalab / CGDD Commissariat Général au Développement Durable (2016 - )   Titre : n° 35 - mai 2018 - Chiffres clés des énergies renouvelables, édition 2018 Type de document : Périodique Auteurs : CGDD Commissariat Général au Développement Durable, Auteur Année de publication : 2018 Importance : 82 p. Format : 10 x 15 cm Langues : Français (fre) Descripteur : [Vedettes matières IGN] Environnement [Termes IGN] analyse comparative [Termes IGN] biocarburant [Termes IGN] biomasse (combustible) [Termes IGN] énergie éolienne [Termes IGN] énergie géothermique [Termes IGN] énergie renouvelable [Termes IGN] énergie solaire [Termes IGN] Europe (géographie politique) [Termes IGN] France (administrative) Index. décimale : 50.00 Environnement Note de contenu : 1 - Quel est le poids des énergies renouvelables en France ? 2 - Quelles sont les différentes filières d'énergies renouvelables présentes en France 3 - Quelle est la place de la France en matière d'énergies renouvelables, en Europe et dans le monde ? Annexes Numéro de notice : 163-201808 Nature : Numéro de périodique En ligne : http://www.statistiques.developpement-durable.gouv.fr/fileadmin/documents/Produi [...] Format de la ressource électronique : URL Bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=31302 [n° ou bulletin]

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Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method / Moussa Sofiane Karoui (2018)  

Public Titre : Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Arnaud Le Bris , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : HYEP / Weber, Christiane Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1640 - 1643 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse des mélanges spectraux [Termes IGN] détection de contours [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] panneau photovoltaïque [Termes IGN] zone urbaine Résumé : (auteur) Hyperspectral remote sensing data offer unique opportunities for the characterization of land surface in urban areas. However, no hyperspectral-unmixing based studies have been conducted to automatically detect photovoltaic panels, which represent one of the important components of energy systems in such areas. In this paper, a hyperspectral-unmixing based method is proposed to detect photovoltaic panels and to estimate their areas. This approach is based on an original multiplicative nonnegative matrix factorization (NMF) algorithm with some known photovoltaic panel spectra. The proposed method can be considered as a partial/informed NMF approach. Experiments are conducted on realistic synthetic and real data to evaluate the performance of the proposed approach. In both cases, obtained results show that the proposed method yields much better overall performance than a method from the literature. Numéro de notice : C2018-047 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8518204 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8518204 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91270

n° 8 - février 2017 - Chiffres clés des énergies renouvelables, édition 2016 (Bulletin de Datalab) / CGDD Commissariat Général au Développement Durable    

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Amélioration des procédés de traitement des missions d’inspection sur centrale photovoltaïque / Karim Mazary (2017)

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Optimal site selection for sitting a solar park using multi-criteria decision analysis and geographical information systems / Andreas Georgiou in Geoscientific instrumentation methods and data systems, vol 5 n° 2 (July - December 2016)  

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A place in the sun / Krista Montgomery in GEO: Geoconnexion international, vol 14 n° 8 (September 2015)

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Ableitung von 3D-Gebäudeobjekten aus 3D-Flächen und Katastergrundissen mit Hilfe von SketchUp© / Tobias Jung in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, vol 138 n° 5 (01/09/2013)

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Cadastre solaire : recherche systématique des toits les plus appropriés pour les installations photovoltaïques / D. Klauser in Géomatique suisse, vol 110 n° 5 (01/05/2012)

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POTSOL : analyse du potentiel photovoltaïque des toits de bâtiments / F. Grin in Géomatique suisse, vol 109 n° 11 (01/11/2011)

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Web-based solar roof cadastre goes international / S. Lanig in GEO: Geoconnexion international, vol 10 n° 7 (July – august 2011)

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Solar atlas of Berlin / D. Ludwig in GIM international, vol 24 n° 3 (March 2010)

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n°2 Hors série - octobre 2009 - Le journal du photovoltaïque (Bulletin de Systèmes solaires, le journal des énergies renouvelables)

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