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Titre : Innovative geo-information tools for governance Type de document : Monographie Auteurs : Yola Georgiadou, Éditeur scientifique ; Diana Reckien, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 186 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03921-338-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] chaleur
[Termes IGN] changement climatique
[Termes IGN] énergie renouvelable
[Termes IGN] gestion de l'eau
[Termes IGN] information géographique
[Termes IGN] outil d'aide à la décision
[Termes IGN] politique publique
[Termes IGN] recherche interdisciplinaire
[Termes IGN] surveillance hydrologique
[Termes IGN] téléphone intelligent
[Termes IGN] urbanisationRésumé : (éditeur) In current times, highly complex and urgent policy problems--e.g., climate change, rapid urbanization, equitable access to key services, land rights, and massive human resettlement--challenge citizens, NGOs, private corporations, and governments at all levels. These policy problems, often called 'wicked', involve multiple causal factors, anticipated and unanticipated effects, as well as high levels of disagreement among stakeholders about the nature of the problem and the appropriateness of solutions. Given the wickedness of such policy problems, interdisciplinary and longitudinal research is required, integrating and harnessing the diverse skills and knowledge of urban planners, anthropologists, geographers, geo-information scientists, economists, and others. This Special Issue promotes innovative concepts, methods, and tools, as well as the role of geo-information, to help (1) analyze alternative policy solutions, (2) facilitate stakeholder dialogue, and (3) explore possibilities for tackling wicked problems related to climate change, rapid urbanization, equitable access to key services (such as water and health), land rights, and human resettlements in high-, middle-, and low-income countries in the North and South. Such integrative approaches can deepen our understanding of how different levels of government and governance reach consensus, despite diverging beliefs and preferences. Due to the particularly complex spatiotemporal characteristics of wicked policy problems, innovative concepts, alternative methods, and new geo-information tools play a significant role. Note de contenu : 1- Monitoring Rural Water Points in Tanzania with Mobile Phones: The Evolution of the
SEMA App
2- An Interactive Planning Support Tool for Addressing Social Acceptance of Renewable Energy Projects in The Netherlands
3- The Governance Landscape of Geospatial E-Services—The Belgian Case
4- Closing Data Gaps with Citizen Science?Findings from the Danube Region
5- Tensions in Rural Water Governance: The Elusive Functioning of Rural Water Points in Tanzania
6- Evolving Spatial Data Infrastructures and the Role of Adaptive Governance
7- Wicked Water Points: The Quest for an Error Free National Water Point Database
8- What do New Yorkers Think about Impacts and Adaptation to Heat Waves? An Evaluation
Tool to Incorporate Perception of Low-Income Groups into Heat Wave Adaptation Scenarios in New York City
9-Numéro de notice : 25988 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Monographie DOI : 10.3390/books978-3-03921-338-2 En ligne : https://doi.org/10.3390/books978-3-03921-338-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96754
Titre : Landscape urbanism and green infrastructure Type de document : Monographie Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 ISBN/ISSN/EAN : 78-3-03921-370-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme
[Termes IGN] bien-être collectif
[Termes IGN] développement durable
[Termes IGN] écosystème urbain
[Termes IGN] espace vert
[Termes IGN] paysage urbain
[Termes IGN] planification urbaine
[Termes IGN] ville durableRésumé : (éditeur) This volume examines the applicability of landscape urbanism theory in contemporary landscape architecture practice by bringing together ecology and architecture in the built environment. Using participatory planning of green infrastructure and application of nature-based solutions to address urban challenges, landscape urbanism seeks to reintroduce critical connections between natural and urban systems. In light of ongoing developments in landscape architecture, the goal is a paradigm shift towards a landscape that restores and rehabilitates urban ecosystems. Nine contributions examine a wide range of successful cases of designing livable and resilient cities in different geographical contexts, from the United States of America to Australia and Japan, and through several European cities in Italy, Portugal, Estonia, and Greece. While some chapters attempt to conceptualize the interconnections between cities and nature, others clearly have an empirical focus. Efforts such as the use of ornamental helophyte plants in bioretention ponds to reduce and treat stormwater runoff, the recovery of a poorly constructed urban waterway or participatory approaches for optimizing the location of green stormwater infrastructure and examining the environmental justice issue of equative availability and accessibility to public open spaces make these innovations explicit. Thus, this volume contributes to the sustainable cities goal of the United Nations. Note de contenu : 1- The emergence of landscape urbanism: A chronological criticism essay
2- Public green infrastructure contributes to city livability: A systematic quantitative review
3- Environmental justice in accessibility to green infrastructure in two European cities
4- Residents’ perception of informal green space - a case study of Ichikawa city, Japan
5- Prioritizing suitable locations for green stormwater infrastructure based on social factors in Philadelphia
6- Visitor satisfaction with a public green infrastructure and Urban Nature Space in Perth,Western Australia
6- Assessing stormwater nutrient and heavy metal plant uptake in an experimental bioretention pond
7- Urban river recovery inspired by nature-based solutions and biophilic design in Albufeira, Portugal
8- The usage and perception of pedestrian and cycling streets on residents’ well-being in Kalamaria, GreeceNuméro de notice : 28519 Affiliation des auteurs : non IGN Thématique : URBANISME Nature : Monographie DOI : sans En ligne : https://doi.org/10.3390/books978-3-03921-370-2 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97306
Titre de série : Learning to understand remote sensing images, 1 Titre : Volume 1 Type de document : Monographie Auteurs : Qi Wang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 426 p. ISBN/ISSN/EAN : 978-3-03897-685-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse texturale
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] réseau neuronal convolutifRésumé : (Editeur) With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field. Numéro de notice : 26301A Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-685-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03897-685-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95033
Titre de série : Learning to understand remote sensing images, 2 Titre : Volume 2 Type de document : Monographie Auteurs : Qi Wang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 376 p. ISBN/ISSN/EAN : 978-3-03897-699-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse texturale
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] réseau neuronal convolutifRésumé : (Editeur) With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field. Numéro de notice : 26301B Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-699-8 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03897-699-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95034 Machine learning techniques applied to geoscience information system and remote sensing / Saro Lee (2019)
Titre : Machine learning techniques applied to geoscience information system and remote sensing Type de document : Monographie Auteurs : Saro Lee, Éditeur scientifique ; Hyung-Sup Jung, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 438 p. ISBN/ISSN/EAN : ISBN 978-3-03921-215-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] géosciences
[Termes IGN] réseau neuronal convolutif
[Termes IGN] système d'information géographique
[Termes IGN] télédétection
[Termes IGN] traitement de données localiséesRésumé : (éditeur) As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing. Numéro de notice : 25831 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif En ligne : https://www.mdpi.com/books/pdfview/book/1533 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95158 Microwave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink