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scientifique

Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

Public [article]  inSustainable Cities and Society > vol 85 (October 2022) . - n° 104026 Titre : Machine learning and natural language processing of social media data for event detection in smart cities Type de document : Article/Communication Auteurs : Andrei Hodorog, Auteur ; Ioan Petri, Auteur ; yacine Rezgui, Auteur Année de publication : 2022 Article en page(s) : n° 104026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] classification bayesienne [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] outil d'aide à la décision [Termes IGN] régression multiple [Termes IGN] taxinomie [Termes IGN] traitement du langage naturel [Termes IGN] ville intelligente Résumé : (auteur) Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. Numéro de notice : A2022-764 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104026 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101785 [article]

Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2103 - 2130 Titre : Deep image deblurring: A survey Type de document : Article/Communication Auteurs : Kaihao Zhang, Auteur ; Wenqi Ren, Auteur ; Wenhan Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2103 - 2130 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déconvolution [Termes IGN] estimation par noyau [Termes IGN] filtrage du bruit [Termes IGN] image floue [Termes IGN] qualité d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] taxinomie [Termes IGN] vision par ordinateur Résumé : (auteur) Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions. Numéro de notice : A2022-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01633-5 Date de publication en ligne : 25/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01633-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101444 [article]

Effect of riparian soil moisture on bacterial, fungal and plant communities and microbial decomposition rates in boreal stream-side forests / M.J. Annala in Forest ecology and management, vol 519 (September-1 2022)  

Public [article]  inForest ecology and management > vol 519 (September-1 2022) . - n° 120344 Titre : Effect of riparian soil moisture on bacterial, fungal and plant communities and microbial decomposition rates in boreal stream-side forests Type de document : Article/Communication Auteurs : M.J. Annala, Auteur ; K. Lehosmaa, Auteur ; S.H.K. Ahonen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120344 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cours d'eau [Termes IGN] écosystème forestier [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] forêt ripicole [Termes IGN] Fungi [Termes IGN] humidité du sol [Termes IGN] micro-organisme [Termes IGN] plante ripicole [Termes IGN] taxinomie [Termes IGN] zone tampon [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Riparian habitats of boreal forests are considered as hotspots for biochemical processes and biodiversity, and varying width riparian buffers have been proposed to protect species diversity of the riparian forests. However, evidence of the role of soil moisture variation in shaping riparian biodiversity and ecosystem functioning remain scarce particularly regarding belowground diversity. We studied how distance from the stream and soil moisture of the riparian zone affected species richness and community composition of plants, bacteria, and fungi as well as microbial decomposition rates. Using a split-plot design with a plant survey and amplicon sequencing for microorganisms we identified taxa associated with different categories of moisture and distance from the stream along six headwater stream-sides in middle boreal forests in Northern Finland. Tea-bag Index was used to assess the decomposition rates. PERMANOVA and linear mixed-effect models were used to analyze the data. Variation in riparian soil moisture influenced species composition and richness of plants and bacteria. Plant communities also changed from herbaceous dominated to shrub dominated with increasing distance from the stream. Fungal communities, however, did not respond to soil moisture or distance from the stream, and there were only slight differences in fungal trophic guilds among moisture and distance categories. Decomposition of organic material by microorganisms was faster adjacent to the stream than further away, and moist riparian areas had higher decomposition rates than drier ones. Decomposition rates were positively related to pH, Ca, Mg and NH4 and soil temperature. Synthesis and applications We show that above- and belowground diversity and microbial decomposition are associated to soil moisture at riparian sites supporting the idea of leaving wider unmanaged buffers in moist habitats to safeguard the overall forest diversity. Our findings further emphasize the need to consider soil moisture when planning the measures for riparian protection as changes in riparian soil moisture could lead to deterioration of organic matter decomposition. Different responses of the examined plant and microbial communities to riparian soil conditions clearly imply that overall riparian diversity cannot be explained based on a single community type, and that different organisms may respond differently to human-induced changes in stream riparian zone. Numéro de notice : A2022-485 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.foreco.2022.120344 Date de publication en ligne : 04/06/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120344 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100923 [article]

A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807 Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] arbre aléatoire minimum [Termes IGN] distribution spatiale [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] réseau neuronal de graphes [Termes IGN] taxinomie [Termes IGN] trafic routier [Termes IGN] triangulation de Delaunay [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-460 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622 [article]

Generic programming in modern C++ for image processing / Michaël Roynard (2022)  

Public Titre : Generic programming in modern C++ for image processing Type de document : Thèse/HDR Auteurs : Michaël Roynard, Auteur ; Thierry Géraud, Directeur de thèse ; Edwin Carlinet, Directeur de thèse Editeur : Paris : Sorbonne Université Année de publication : 2022 Importance : 237 p. Format : 21 x 30 cm Note générale : bibliographie Doctoral thesis submitted to fufill the requirements for the degree of Doctor of Sorbonne Université with the doctoral speciality of "Software Engineering and Image Processing" Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques [Termes IGN] C++ [Termes IGN] langage de programmation [Termes IGN] morphologie mathématique [Termes IGN] programmation informatique [Termes IGN] taxinomie [Termes IGN] traitement d'image Index. décimale : THESE Thèses et HDR Résumé : (auteur) C++ is a multi-paradigm language that enables the initiated programmer to set up efficient image processing algorithms. This language strength comes from several aspects. C++ is high-level, which enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One of the most fundamental aspects where C++ really shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless,conciliating the aspects of genericity, efficiency, and simplicity is not trivial. Modern C++ (post-2011) has brought new features that made the language simpler and more powerful. In this thesis, we first explore one particular C++20aspect: the concepts, in order to build a concrete taxonomy of image related types and algorithms. Second, we explore another addition to C++20, ranges (and views), and we apply this design to image processing algorithms and image types in order to solve issues such as how hard it is to customize/tweak image processing algorithms. Finally, we explore possibilities regarding how we can offer a bridge between static (compile-time) generic C++ code and dynamic (runtime) Python code. We offer our own hybrid solution and benchmark its performance as well as discuss what can be done in the future with JIT technologies. Considering those three axes, we will address the issue regarding the way to conciliate generic programming, efficiency and ease of use. Note de contenu : I Context and History of Generic programming 1- Introduction 2- Generic programming (genericity) II Applying Generic programming for Image processing in the static world 3- Taxonomy for Image Processing: Image types and algorithms 4- Image views III Bringing Generic programming to the dynamic world 5- A bridge between the static world and the dynamic world 6- Conclusion and continuation Numéro de notice : 24083 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : PhD thesis : Software Engineering and Image Processing : Sorbonne Université : 2022 Organisme de stage : EPITA DOI : sans En ligne : https://theses.hal.science/tel-03922670 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102391

L'îlot Kergaher, un lieu nature pour toutes et tous / Raphaël Mattioni (2022) 

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Developing reliably distinguishable color schemes for legends of natural resource taxonomy-based maps / Virgil Vlad in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)  

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Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)  

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Apport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (2021)  

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Consolidation of crowd-sourced geo-ragged data for parameterized travel recommendations / Ago Luberg (2021)  

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An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

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Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)  

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Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)  

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A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)    

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“Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap / Clancy Wilmott in Cartographic perspectives, n° 92 (2019)  

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