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urbanisme
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Aménagement urbain, Développement urbain, Habitat (urbanisme), Planification urbaine, Ville modèle. Synonyme(s)aménagement urbainVoir aussi |
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A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]Remote sensing in urban planning: Contributions towards ecologically sound policies? / Thilo Wellmann in Landscape and Urban Planning, vol 204 (December 2020)
[article]
Titre : Remote sensing in urban planning: Contributions towards ecologically sound policies? Type de document : Article/Communication Auteurs : Thilo Wellmann, Auteur ; Angela Lausch, Auteur ; Erik Andersson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103921 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écologie
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] littérature
[Termes IGN] orthoimage
[Termes IGN] planification urbaine
[Termes IGN] risque environnemental
[Termes IGN] semis de points
[Termes IGN] service écosystémique
[Termes IGN] télédétection
[Termes IGN] urbanismeRésumé : (auteur) Remote sensing has evolved to become a key tool for various fields of environmental analysis, thus actively informing policy across areas and domains. To evaluate the degree to which remote sensing is contributing to the science of ecologically-oriented urban planning, we carried out a systematic literature review using the SCOPUS database, searching for articles integrating knowledge in urban planning, remote sensing and ecology. We reviewed 186 articles, analysing various issues in urban environments worldwide. Key findings include that the level of integration between the three disciplines is limited, with only 12% of the papers fully integrating ecology, remote sensing and planning while 24% of the studies use specific methods from one domain only. The vast majority of studies is oriented towards contributing to the knowledge base or monitoring the impacts of existing policies. Few studies are directly policy relevant by either contributing to direct issues in planning and making specific design suggestions or evaluations. The accessibility of the scientific findings remains limited, as the majority of journal articles are not open access and proprietary software and data are frequently used. To overcome these issues, we suggest three future avenues for science as well as three potential entry points for remote sensing into applied urban planning. By doing so, remote sensing data could become a vital tool actively contributing to policies, civil engagement and concrete planning measures by providing independent and cost effective environmental analyses. Numéro de notice : A2020-734 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.landurbplan.2020.103921 Date de publication en ligne : 27/08/2020 En ligne : https://doi.org/10.1016/j.landurbplan.2020.103921 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96348
in Landscape and Urban Planning > vol 204 (December 2020) . - n° 103921[article]Semantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : Semantic‐based urban growth prediction Type de document : Article/Communication Auteurs : Marvin Mc Cutchan, Auteur ; Simge Özdal‐Oktay, Auteur ; Ioannis Giannopoulos, Auteur Année de publication : 2020 Article en page(s) : 1482 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] croissance urbaine
[Termes IGN] dynamique spatiale
[Termes IGN] Europe (géographie politique)
[Termes IGN] information sémantique
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] organisation spatiale
[Termes IGN] OWL
[Termes IGN] prévision
[Termes IGN] réseau neuronal artificiel
[Termes IGN] urbanisation
[Termes IGN] ville durableRésumé : (Auteur) Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks. Numéro de notice : A2020-766 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12655 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1111/tgis.12655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96657
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - 1482 - 1503[article]Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)
[article]
Titre : Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Type de document : Article/Communication Auteurs : Oliver Lock, Auteur ; Christopher Pettit, Auteur Année de publication : 2020 Article en page(s) : pp 275 - 292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] artefact
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] planification urbaine
[Termes IGN] réseau social
[Termes IGN] sentiment
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] traitement du langage naturel
[Termes IGN] transport public
[Termes IGN] ville intelligenteRésumé : (auteur) We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques. Numéro de notice : A2020-787 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1815596 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1080/10095020.2020.1815596 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96545
in Geo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 275 - 292[article]The Urban Climate Services URCLIM project / Valéry Masson in Climate Services, vol 20 (December 2020)
[article]
Titre : The Urban Climate Services URCLIM project Type de document : Article/Communication Auteurs : Valéry Masson, Auteur ; Erwan Bocher, Auteur ; Bénédicte Bucher , Auteur ; Zenaida Chitu, Auteur ; Sidonie Christophe , Auteur ; et al., Auteur Année de publication : 2020 Projets : URCLIM / Masson, Valéry Article en page(s) : n° 100194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse multicritère
[Termes IGN] changement climatique
[Termes IGN] gestion urbaine
[Termes IGN] propagation d'incertitude
[Termes IGN] urbanisme
[Termes IGN] ville
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (auteur) [introduction] While already just over 50% of the world population lives in cities, it is expected that on balance practically all population growth up to 2050 is in cities, amounting to 3 billion extra urban inhabitants amounting to a total urban population of 6.5 billion people (Revi et al., 2014). Cities are particularly vulnerable to climate change because of their high concentration of population, goods, capital stock, and infrastructures. Heat waves in particular, enhanced locally by the so-called Urban Heat Island (UHI) lead to an above-normal mortality rate in cities (Wong et al., 2013; Shaposhnikov et al., 2014). Intense precipitation in urban areas, on the other hand, cause more easily floods with dire consequences because of the impermeability of the urban surfaces (Muis et al., 2015; Yin et al., 2016). Air quality conditions in cities are recurrently and often even continuously exceeding health limits. Furthermore, cities are strong emitters of greenhouse-gases, as the high concentration of human activities, like transport and industry, entails high levels of energy consumption. Numéro de notice : A2020-819 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cliser.2020.100194 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1016/j.cliser.2020.100194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97237
in Climate Services > vol 20 (December 2020) . - n° 100194[article]Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkDétection du changement de l'étalement urbain au bas-Sahara algérien : apport de la télédétection spatiale et des SIG, cas de la ville de Biskra (Algérie) / Assoule Dechaicha in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)PermalinkLandslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkUrban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model / Tingting Xu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)PermalinkUrban tree species identification and carbon stock mapping for urban green planning and management / MD Abdul Choudhury in Forests, vol 11 n°11 (November 2020)PermalinkUsing climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas / Rabeeh Hosseinihaghighi in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkWorldwide detection of informal settlements via topological analysis of crowdsourced digital maps / Satej Soman in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkMonitoring population dynamics in the Pearl River Delta from 2000 to 2010 / Sisi Yu in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkCoupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkHierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkLa méthode de la photo-interview à partir de la photographie aérienne : Le cas d’un bidonville à Nanterre dans les années 1960 / Laetitia Delavoipiere in EchoGeo, n° 54 (octobre - décembre 2020)PermalinkNetwork-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)PermalinkA preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkA spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications / T. Chakraborty in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkSpatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkUrban flooding in Britain: an approach to comparing ancient and contemporary flood exposure / T.E. O'Shea in Natural Hazards, Vol 104 n° 1 (October 2020)PermalinkUrban Wi-Fi fingerprinting along a public transport route / Guenther Retscher in Journal of applied geodesy, vol 14 n° 4 (October 2020)PermalinkA spatio-temporal web-application for the understanding of the formation of the Parisian metropolis / Emile Blettery in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol VI-4/W1 ([03/09/2020])PermalinkComprehensive decision-strategy space exploration for efficient territorial planning strategies / Olivier Billaud in Computers, Environment and Urban Systems, vol 83 (September 2020)Permalink