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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    gbv_177848817X
    Format: 1 Online-Ressource (438 p.)
    ISBN: 9783039212156 , 9783039212163
    Content: 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
    Note: English
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almahu_BV045389467
    Format: 1 Online-Ressource (xxix, 361 Seiten) : , Illustrationen, Diagramme, Karten (überwiegend farbig).
    ISBN: 978-3-030-01440-7
    Series Statement: Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-01439-1
    Language: English
    Keywords: Konferenzschrift ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    almahu_9949711514702882
    Format: 1 electronic resource (438 p.)
    ISBN: 3-03921-216-8
    Content: 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.
    Note: English
    Additional Edition: ISBN 3-03921-215-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    edocfu_9959213097502883
    Format: 1 electronic resource (438 p.)
    ISBN: 3-03921-216-8
    Content: 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.
    Note: English
    Additional Edition: ISBN 3-03921-215-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    Basel :MDPI AG - Multidisciplinary Digital Publishing Institute,
    UID:
    almahu_9949711506402882
    Format: 1 online resource (228 pages) : , illustrations
    Content: Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.
    Note: About the Special Issue Editor v -- Saro Lee -- Editorial for Special Issue: Application of Artificial Neural Networks in Geoinformatics doi: 10.3390/app8010055 1 -- Sunmin Lee, Moung-Jin Lee and Hyung-Sup Jung Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea doi: 10.3390/app7070683 4 Hyun-Joo Oh and Saro Lee -- Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree doi: 10.3390/app7101000 25 -- Saro Lee, Sunmin Lee, Wonkyong Song and Moung-Jin Lee -- Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning doi: 10.3390/app7090912 39 -- Syyed Adnan Raheel Shah, Tom Brijs, Naveed Ahmad, Ali Pirdavani, Yongjun Shen and Muhammad Aamir Basheer -- Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis-Artificial Neural Networks Approach doi: 10.3390/app7090886 54 -- Mustafa Ridha Mezaal, Biswajeet Pradhan, Maher Ibrahim Sameen, Helmi Zulhaidi Mohd Shafri and Zainuddin Md Yusoff -- Optimized Neural Architecture for Automatic Landslide Detection from High-Resolution Airborne Laser Scanning Data doi: 10.3390/app7070730 73 -- Guandong Chen, Yu Li, Guangmin Sun and Yuanzhi Zhang -- Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images doi: 10.3390/app7100968 93 -- Jeong-In Hwang, Sung-Ho Chae, Daeseong Kim and Hyung-Sup Jung -- Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat5 Imagery doi: 10.3390/app7090961 108 -- Alessandro Piscini, Vito Romaniello, Christian Bignami and Salvatore Stramondo A New Damage Assessment Method by Means of Neural Network and Multi-Sensor -- Satellite Data doi: 10.3390/app7080781 122 Books MDPI -- Prima Riza Kadavi, Won-Jin Lee and Chang-Wook Lee Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat -- Imagery and the Artificial Neural Networks Approach doi: 10.3390/app7090935 132 -- Soo-Kyung Kwon, Hyung-Sup Jung, Won-Kyung Baek and Daeseong Kim -- Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network doi: 10.3390/app7101046 146 -- Giles M. Foody Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural -- Network Classification doi: 10.3390/app7090888-- Young-Ji Byon, Jun Su Ha, Chung-Suk Cho, Tae-Yeon Kim and Chan Yeob Yeun -- Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai doi: 10.3390/app7090923 174 -- Maher Ibrahim Sameen and Biswajeet Pradhan -- Severity Prediction of Traffic Accidents with Recurrent Neural Networks doi: 10.3390/app7060476 191 -- N ´adia F. Afonso and Jos´e C. M. Pires -- Characterization of Surface Ozone Behavior at Different Regimes doi: 10.3390/app7090944 208.
    Additional Edition: ISBN 3-03842-742-X
    Language: English
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  • 6
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    edoccha_9959213097502883
    Format: 1 electronic resource (438 p.)
    ISBN: 3-03921-216-8
    Content: 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.
    Note: English
    Additional Edition: ISBN 3-03921-215-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    Basel, Switzerland :MDPI,
    UID:
    edoccha_9959704373202883
    Format: 1 online resource : , illustrations
    ISBN: 3-03842-741-1
    Content: Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    Online Resource
    Online Resource
    Basel, Switzerland :MDPI,
    UID:
    almahu_9949507792802882
    Format: 1 online resource : , illustrations
    ISBN: 3-03842-741-1
    Content: Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Online Resource
    Online Resource
    Basel, Switzerland :MDPI,
    UID:
    edocfu_9959704373202883
    Format: 1 online resource : , illustrations
    ISBN: 3-03842-741-1
    Content: Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    UID:
    almafu_9959013733402883
    Format: 1 online resource (XXIX, 361 p. 196 illus., 172 illus. in color.)
    Edition: 1st ed. 2019.
    ISBN: 3-030-01440-1
    Series Statement: Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development,
    Content: This edited volume is based on the best papers accepted for presentation during the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018. The book compiles a wide range of topics addressing various issues by experienced researchers mainly from research institutes in the Mediterranean, MENA region, North America and Asia. Remote sensing observations can close gaps in information scarcity by complementing ground-based sparse data. Spatial, spectral, temporal and radiometric characteristics of satellites sensors are most suitable for features identification. The local to global nature and broad spatial scale of remote sensing with the wide range of spectral coverage are essential characteristics, which make satellites an ideal platform for mapping, observation, monitoring, assessing and providing necessary mitigation measures and control for different related Earth's systems processes. Main topics in this book include: Geo-informatics Applications, Land Use / Land Cover Mapping and Change Detection, Emerging Remote Sensing Applications, Rock Formations / Soil Lithology Mapping, Vegetation Mapping Impact and Assessment, Natural Hazards Mapping and Assessment, Ground Water Mapping and Assessment, Coastal Management of Marine Environment and Atmospheric Sensing.
    Note: Geoinformatics & Applications -- Land Use Land Cover Mapping & Urban Form Assessment -- Lidar Drone and Emerging Technologies Applications -- Rock Formations & Soil Lithology Mapping -- Vegetation Mapping impact Assessment -- Natural Hazards Monitoring & Mapping -- Ground Water Mapping & Assessment -- Coastal Management & Marine Environement -- Atmospheric Sensing.
    Additional Edition: ISBN 3-030-01439-8
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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