Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS
This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing a...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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001 | doab_20_500_12854_76634 | ||
005 | 20220111 | ||
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020 | |a 9783036516035 | ||
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024 | 7 | |a 10.3390/books978-3-0365-1603-5 |c doi | |
041 | 0 | |a eng | |
042 | |a dc | ||
072 | 7 | |a TQ |2 bicssc | |
100 | 1 | |a Lee, Chang-Wook |4 edt | |
700 | 1 | |a Han, Hyangsun |4 edt | |
700 | 1 | |a Lee, Hoonyol |4 edt | |
700 | 1 | |a Park, Yu-Chul |4 edt | |
700 | 1 | |a Lee, Chang-Wook |4 oth | |
700 | 1 | |a Han, Hyangsun |4 oth | |
700 | 1 | |a Lee, Hoonyol |4 oth | |
700 | 1 | |a Park, Yu-Chul |4 oth | |
245 | 1 | 0 | |a Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
300 | |a 1 electronic resource (165 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Environmental science, engineering & technology |2 bicssc | |
653 | |a groundwater potential | ||
653 | |a specific capacity | ||
653 | |a machine learning | ||
653 | |a boosted tree | ||
653 | |a ensemble models | ||
653 | |a prototype selection | ||
653 | |a river pollution | ||
653 | |a supervised classification | ||
653 | |a WSN | ||
653 | |a probabilistic method | ||
653 | |a Monte Carlo simulation | ||
653 | |a physical slope model | ||
653 | |a Mt. Umyeon landslides | ||
653 | |a Seoul | ||
653 | |a synthetic aperture radar | ||
653 | |a land subsidence | ||
653 | |a GIS | ||
653 | |a time-series | ||
653 | |a Jakarta | ||
653 | |a land subsidence susceptibility mapping | ||
653 | |a time-series InSAR | ||
653 | |a StaMPS processing | ||
653 | |a seismic vulnerability map | ||
653 | |a DPM method | ||
653 | |a Sentinel-1 | ||
653 | |a seismic literacy | ||
653 | |a neural networks | ||
653 | |a urban vegetation | ||
653 | |a urban open spaces | ||
653 | |a Monterrey Metropolitan Area | ||
653 | |a sustainable development | ||
653 | |a deep learning | ||
653 | |a transfer learning | ||
653 | |a artificial intelligence | ||
653 | |a remote sensing | ||
653 | |a earth observation | ||
653 | |a DInSAR | ||
653 | |a change detection | ||
653 | |a space data science | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4081 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76634 |7 0 |z DOAB: description of the publication |