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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.

Environmental science, engineering & technology --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Environmental science, engineering & technology --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science