Feature Papers for Land Innovations – Data and Machine Learning

The Special Issue “Feature Papers for Land Innovations—Data and Machine Learning” makes valuable contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. In particular, this Speci...

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Izdano: MDPI - Multidisciplinary Digital Publishing Institute 2024
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collection Directory of Open Access Books
description The Special Issue “Feature Papers for Land Innovations—Data and Machine Learning” makes valuable contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. In particular, this Special Issue welcame geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts were particularly welcome as well.
format Online
id doab-20.500.12854ir-137505
institution Directory of Open Access Books
language eng
publishDate 2024
publishDateRange 2024
publishDateSort 2024
publisher MDPI - Multidisciplinary Digital Publishing Institute
publisherStr MDPI - Multidisciplinary Digital Publishing Institute
record_format ojs
spelling doab-20.500.12854ir-1375052024-05-14T13:20:02Z Feature Papers for Land Innovations – Data and Machine Learning Zhang, Chuanrong spatial big data land use and land cover geospatial artificial intelligence machine learning deep learning big data processing big data analysis big data visualization thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics The Special Issue “Feature Papers for Land Innovations—Data and Machine Learning” makes valuable contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. In particular, this Special Issue welcame geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts were particularly welcome as well. 2024-05-14T13:19:58Z 2024-05-14T13:19:58Z 2024 book ONIX_20240514_9783725800834_107 9783725800834 9783725800841 https://directory.doabooks.org/handle/20.500.12854/137505 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8664 https://mdpi.com/books/pdfview/book/8664 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0084-1 10.3390/books978-3-7258-0084-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725800834 9783725800841 242 open access
spellingShingle spatial big data
land use and land cover
geospatial artificial intelligence
machine learning
deep learning
big data processing
big data analysis
big data visualization
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
Feature Papers for Land Innovations – Data and Machine Learning
title Feature Papers for Land Innovations – Data and Machine Learning
title_full Feature Papers for Land Innovations – Data and Machine Learning
title_fullStr Feature Papers for Land Innovations – Data and Machine Learning
title_full_unstemmed Feature Papers for Land Innovations – Data and Machine Learning
title_short Feature Papers for Land Innovations – Data and Machine Learning
title_sort feature papers for land innovations data and machine learning
topic spatial big data
land use and land cover
geospatial artificial intelligence
machine learning
deep learning
big data processing
big data analysis
big data visualization
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
topic_facet spatial big data
land use and land cover
geospatial artificial intelligence
machine learning
deep learning
big data processing
big data analysis
big data visualization
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
url ONIX_20240514_9783725800834_107