His scientific interests lie mostly in Remote sensing, Land cover, Cartography, Scale and Random forest. His Remote sensing research includes elements of Crop, Scale-invariant feature transform, Pixel, Artificial intelligence and Decision tree learning. Le Yu has researched Land cover in several fields, including Enhanced vegetation index, Sample and Climate change.
His work carried out in the field of Cartography brings together such families of science as Spectral bands and Thematic Mapper. His Scale research integrates issues from Earth science, Global Earth Observation System of Systems, Variety, Information management and Visualization. In his work, Digital elevation model, Radiometer and Principal component analysis is strongly intertwined with Support vector machine, which is a subfield of Random forest.
The scientist’s investigation covers issues in Remote sensing, Land cover, Land use, Remote sensing and Artificial intelligence. His study focuses on the intersection of Remote sensing and fields such as Moderate-resolution imaging spectroradiometer with connections in the field of Change detection. Le Yu has researched Land cover in several fields, including Cartography, Scale, Thematic Mapper, Random forest and Sample.
His Cartography research incorporates elements of Impervious surface, Spectral bands and Satellite imagery. His Land use research is multidisciplinary, incorporating elements of Deforestation, Urbanization, Climate change and Physical geography. His work on Deep learning as part of general Artificial intelligence study is frequently linked to Palm oil, bridging the gap between disciplines.
Le Yu focuses on Physical geography, Remote sensing, Land cover, Palm oil and Land use. His research in Physical geography intersects with topics in Elevation, Climate change, Urbanization, Vegetation and Spatial ecology. His works in Remote sensing and Thematic Mapper are all subjects of inquiry into Remote sensing.
The various areas that Le Yu examines in his Land cover study include Random forest and Support vector machine. In his research on the topic of Land use, Change detection and Moderate-resolution imaging spectroradiometer is strongly related with Deforestation. His biological study spans a wide range of topics, including Pattern recognition and Scale.
Le Yu spends much of his time researching Remote sensing, Land cover, Change detection, Deforestation and Moderate-resolution imaging spectroradiometer. Le Yu has included themes like Deep learning, Artificial intelligence and Land use in his Remote sensing study. His Deep learning study combines topics in areas such as Support vector machine, Thematic Mapper, Impervious surface, Random forest and Convolutional neural network.
His research integrates issues of Remote sensing and Land-use planning in his study of Artificial intelligence. His Change detection research is multidisciplinary, incorporating perspectives in Downstream, Synthetic aperture radar, Global change, Land management and Normalized Difference Vegetation Index. His studies deal with areas such as Biofuel, Forestry and Greenhouse gas as well as Deforestation.
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Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Peng Gong;Jie Wang;Le Yu;Yongchao Zhao.
Journal of remote sensing (2013)
Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives
Le Yu;Peng Gong.
Journal of remote sensing (2012)
China’s urban expansion from 1990 to 2010 determined with satellite remote sensing
Lei Wang;Lei Wang;CongCong Li;Qing Ying;Xiao Cheng;Xiao Cheng.
Chinese Science Bulletin (2012)
Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images
Weijia Li;Haohuan Fu;Le Yu;Arthur P. Cracknell.
Remote Sensing (2016)
Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017
Peng Gong;Han Liu;Meinan Zhang;Congcong Li.
Chinese Science Bulletin (2019)
Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II
Kai Cao;Michael Batty;Bo Huang;Yan Liu.
International Journal of Geographical Information Science (2011)
Managing nitrogen to restore water quality in China
ChaoQing Yu;Xiao Huang;Han Chen;H. Charles J. Godfray.
A fast and fully automatic registration approach based on point features for multi-source remote-sensing images
Le Yu;Dengrong Zhang;Eun-Jung Holden.
Computers & Geosciences (2008)
Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach
Le Yu;Jie Wang;Peng Gong.
Journal of remote sensing (2013)
Towards automatic lithological classification from remote sensing data using support vector machines
Le Yu;Alok Porwal;Eun-Jung Holden;Michael C. Dentith.
Computers & Geosciences (2012)
Profile was last updated on December 6th, 2021.
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