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1. |
Record Nr. |
UNINA9910456472103321 |
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Autore |
Simms Andrew |
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Titolo |
Ecological debt [[electronic resource] ] : global warming and the wealth of nations / / Andrew Simms |
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Pubbl/distr/stampa |
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London, : Pluto Press, 2009 |
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ISBN |
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1-78371-059-4 |
1-84964-377-6 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (336 p.) |
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Disciplina |
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Soggetti |
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Environmental economics |
Environmental protection - Citizen participation |
Consumption (Economics) - Environmental aspects |
Global warming |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references (p. 190-204) and index. |
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Sommario/riassunto |
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--New edition of this highly acclaimed guide-- 'Creative and compelling.' Guardian'Essential reading.' Head of the IPCC 'A new phrase has entered the language.' Anita RoddickThis is the second edition of Andrew Simms's highly regarded guide to ecological |
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2. |
Record Nr. |
UNINA9910789490203321 |
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Titolo |
Remote sensing of protected lands [[electronic resource] /] / edited by Yeqiao Wang |
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Pubbl/distr/stampa |
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Boca Raton, : CRC Press, c2012 |
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ISBN |
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0-429-10567-3 |
1-4398-4188-8 |
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Descrizione fisica |
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1 online resource (613 p.) |
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Collana |
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Taylor & Francis series in remote sensing applications |
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Altri autori (Persone) |
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Disciplina |
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333.73/160285 |
333.73160285 |
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Soggetti |
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Protected areas - Remote sensing |
Remote sensing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front Cover; Contents; Series Foreword; Preface; Acknowledgments; Editor; Contributors; Chapter 2 - Remote Sensing for Inventory and Monitoring of U.S. National Parks; Chapter 3 - Monitoring Landscape Dynamics of National Parks in the Western United States; Chapter 4 - Forest Dynamics within and around Olympic National Park Assessed Using Time-Series Landsat Observations; Chapter 13 - Integration of Remote Sensing and In Situ Ecology for the Design and Evaluation of Marine-Protected Areas: Examples from Tropical and Temperate Ecosystems |
Chapter 14 - Remote Sensing Assessment of Wildfire Impact and Simulation Modeling of Short-Term Post-Fire Vegetation Recovery within the Dixie National ForestChapter 15 - Satellite-Observed Endorheic Lake Dynamics across the Tibetan Plateau between Circa 1976 and 2000; Chapter 16 - Multisensor Remote Sensing of Forest Dynamics in Central Siberia; Chapter 17 - Remote Sensing and Modeling for Assessment of Complex Amur (Siberian) Tiger and Amur (Far Eastern) Leopard Habitats in the Russian Far East; Chapter 18 - The Influence of Realistic Vegetation Phenology on Regional Climate Modeling |
Chapter 19 - Monitoring Natural Hazards in Protected Lands Using Interferometric Synthetic Aperture RadarChapter 20 - Characterizing |
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Biophysical Properties in Protected Tropical Forests with Synergistic Use of Optical and SAR Imagery; Chapter 22 - Monitoring and Forecasting Climate Impacts on Ecosystem Dynamics in Protected Areas Using the Terrestrial Observation and Prediction System; Chapter 23 - Geospatial Decision Models for Management of Protected Wetlands; Back Cover |
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Sommario/riassunto |
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National parks, wildlife refuges and sanctuaries, natural reserves, conservation areas, frontier lands, and marine-protected areas are increasingly recognized as essential providers of ecosystem services and biological resources. As debates about climate change and sustainability intensify, protected areas become more important as indicators of ecosystem conditions in particular environments or in comparison with adjacent environments. The first book of its kind, Remote Sensing of Protected Lands showcases state-of-the-art remote sensing applications for the i |
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3. |
Record Nr. |
UNINA9910299284403321 |
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Titolo |
Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent / / edited by Jianlong Zhou, Fang Chen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XXIII, 482 p. 140 illus., 114 illus. in color.) |
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Collana |
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Human–Computer Interaction Series, , 2524-4477 |
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Disciplina |
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Soggetti |
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User interfaces (Computer systems) |
Human-computer interaction |
Artificial intelligence |
Pattern recognition systems |
User Interfaces and Human Computer Interaction |
Artificial Intelligence |
Automated Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I Transparency in Machine Learning -- Part II Visual Explanation of Machine Learning Process -- Part III Algorithmic Explanation of Machine Learning Models -- Part IV User Cognitive Responses in ML-Based Decision Making -- Part V Human and Evaluation of Machine Learning -- Part VI Domain Knowledge in Transparent Machine Learning Applications. |
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Sommario/riassunto |
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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction. |
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