What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Remote sensing
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.
His most cited work include:
- Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data (790 citations)
- China’s urban expansion from 1990 to 2010 determined with satellite remote sensing (218 citations)
- Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives (196 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Remote sensing (40.61%)
- Land cover (31.52%)
- Land use (16.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Physical geography (12.12%)
- Remote sensing (40.61%)
- Land cover (31.52%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping (15 citations)
- Mapping global urban boundaries from the global artificial impervious area (GAIA) data (9 citations)
- Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm (9 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
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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