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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)  

Public [article]  inEnvironmental Monitoring and Assessment > vol 191 n° 11 (November 2019) Titre : New method for environmental monitoring in armed conflict zones: a case study of Syria Type de document : Article/Communication Auteurs : Samira Mobaied, Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] dégradation de l'environnement [Termes descripteurs IGN] guerre [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] protection de la biodiversité [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] Syrie [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] zone à risque Résumé : (auteur) Today, armed conflict affects some twenty countries, covering an area making up 11% of the surface area of the Earth. Any degradation of nature in these areas represents a harmful depletion of the world’s natural heritage. Despite this, environmental issues are neglected during these periods of conflict, considered secondary to the urgency of restoring peace and safeguarding human life. Yet their consequences are potentially severe. In these areas, it is future generations who will suffer the effects of the current devastation for a very long time. In this context, the method developed in this study, named (Geographic Information System) for Environmental Monitoring in Wartime, can be used to calculate a risk indicator for environmental degradation, spatial monitoring and risk management. This will make it possible to identify the main threats to protected areas, catalogue the damage caused to the environment by armed conflicts and create a dynamic risk map. In this paper, GIS-EMW has been applied to calculate a risk indicator for environmental degradation in Syria. Numéro de notice : A2019-509 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10661-019-7805-5 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1007/s10661-019-7805-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93794 [article]

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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1960 - 1983 Titre : Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur Année de publication : 2019 Article en page(s) : pp 1960 - 1983 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] Auckland [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalement urbain [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] optimisation (mathématiques) Résumé : (auteur) Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. Numéro de notice : A2019-428 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1600701 date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1600701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93561 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 108 - 123 Titre : A new generation of the United States National Land Cover Database : Requirements, research priorities, design, and implementation strategies Type de document : Article/Communication Auteurs : Limin Yang, Auteur ; Suming Jin, Auteur ; Patrick Danielson, Auteur ; Collin Homer, Auteur ; Leila Gass, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] données multitemporelles [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] historique des données [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] implémentation (informatique) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] zone humide Résumé : (Auteur) The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Numéro de notice : A2018-537 Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.006 date de publication en ligne : 13/09/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91550 [article]

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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 237 - 251 Titre : Exploring geo-tagged photos for land cover validation with deep learning Type de document : Article/Communication Auteurs : Hanfa Xing, Auteur ; Yuan Meng, Auteur ; Zixuan Wang, Auteur ; Kaixuan Fan, Auteur ; Dongyang Hou, Auteur Année de publication : 2018 Article en page(s) : pp 237 - 251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] image numérique [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] production participative [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Land cover validation plays an important role in the process of generating and distributing land cover thematic maps, which is usually implemented by high cost of sample interpretation with remotely sensed images or field survey. With an increasing availability of geo-tagged landscape photos, the automatic photo recognition methodologies, e.g., deep learning, can be effectively utilised for land cover applications. However, they have hardly been utilised in validation processes, as challenges remain in sample selection and classification for highly heterogeneous photos. This study proposed an approach to employ geo-tagged photos for land cover validation by using the deep learning technology. The approach first identified photos automatically based on the VGG-16 network. Then, samples for validation were selected and further classified by considering photos distribution and classification probabilities. The implementations were conducted for the validation of the GlobeLand30 land cover product in a heterogeneous area, western California. Experimental results represented promises in land cover validation, given that GlobeLand30 showed an overall accuracy of 83.80% with classified samples, which was close to the validation result of 80.45% based on visual interpretation. Additionally, the performances of deep learning based on ResNet-50 and AlexNet were also quantified, revealing no substantial differences in final validation results. The proposed approach ensures geo-tagged photo quality, and supports the sample classification strategy by considering photo distribution, with accuracy improvement from 72.07% to 79.33% compared with solely considering the single nearest photo. Consequently, the presented approach proves the feasibility of deep learning technology on land cover information identification of geo-tagged photos, and has a great potential to support and improve the efficiency of land cover validation. Numéro de notice : A2018-289 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.025 date de publication en ligne : 16/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90404 [article]

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Domain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks / Tristan Postadjian (2018)  

Public Titre : Domain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks Type de document : Article/Communication Auteurs : Tristan Postadjian , Auteur ; Arnaud Le Bris , Auteur ; Hichem Sahbi, Auteur ; Clément Mallet , Auteur Congrès : Congrès: IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium observing, understanding and forecasting the dynamics of our planet (22 - 27 juillet 2018; Valencia, Espagne) , Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Importance : pp 3631 - 3634 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] classification [Termes descripteurs IGN] grande échelle [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (auteur) Semantic segmentation of remote sensing images enables in particular land-cover map generation for a given set of classes. Very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many tasks, from object recognition to semantic labelling, including the classification of Very High Resolution (VHR) satellite images. However, while plethora of works aim at improving object delineation on geographically restricted areas, few tend to solve this classification task at very large scales. New issues occur such as intra-class class variability, diachrony between surveys, and the appearance of new classes in a specific area, that do not exist in the predefined set of labels. Therefore, this work intends to (i) perform large scale classification and to (ii) expand a set of land-cover classes, using the off-the-shelf model learnt in a specific area of interest and adapting it to unseen areas. Numéro de notice : C2018-048 Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8518799 date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8518799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91271

L'occupation et l'usage des sols par télédétection, où en sommes-nous aujourd'hui ? [Introduction] / Clément Mallet (2018) 

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Journée Copernicus / Anonyme in Géomatique expert, n° 116 (mai - juin 2017)

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Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)  

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Effect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)  

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Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping / Mirco Sturari in European journal of remote sensing, vol 50 n° 1 (2017)  

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Optimal resolution for linking remotely sensed and forest inventory data in Europe / Adam Moreno in Remote sensing of environment, vol 183 (15 September 2016)  

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Mapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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La ville à l’échelle de l’Europe : apports du couplage et de l’expertise de bases de données issues de l’imagerie satellitale / Anne Bretagnolle in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)

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Towards operational very high resolution land-cover mapping / Clément Mallet (2016) 

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Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI / Yuanwei Qin in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

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