What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Landslide, Remote sensing, Cartography, Terrain and Oblique case.
Norman Kerle has researched Landslide in several fields, including Random forest, Image segmentation and Tectonics.
His Remote sensing research is multidisciplinary, incorporating perspectives in Image processing, Orientation, Image resolution and Scale.
The concepts of his Cartography study are interwoven with issues in Risk assessment and Natural hazard.
His Terrain research is multidisciplinary, relying on both Segmentation and Multispectral image.
His Pattern recognition research includes elements of Digital elevation model, Plateau and Artificial intelligence.
His most cited work include:
- Object-oriented mapping of landslides using Random Forests (388 citations)
- Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods (272 citations)
- Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information (196 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Remote sensing, Landslide, Artificial intelligence, Computer vision and Cartography.
His biological study spans a wide range of topics, including Image resolution, Pixel and Land cover.
His study in Landslide is interdisciplinary in nature, drawing from both Lidar, Segmentation and Terrain.
Terrain is closely attributed to Multispectral image in his work.
His research in Artificial intelligence focuses on subjects like Pattern recognition, which are connected to Contextual image classification.
His Image, Object based and Image segmentation investigations are all subjects of Computer vision research.
He most often published in these fields:
- Remote sensing (31.25%)
- Landslide (22.40%)
- Artificial intelligence (22.40%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (22.40%)
- Deep learning (6.77%)
- Remote sensing (31.25%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Deep learning, Remote sensing, Convolutional neural network and Point cloud.
His Artificial intelligence study incorporates themes from Remote sensing, Machine learning, Earth observation and Computer vision.
Norman Kerle works mostly in the field of Deep learning, limiting it down to topics relating to Data mining and, in certain cases, Robustness, Conditional random field, Satellite data and Degradation.
As part of the same scientific family, Norman Kerle usually focuses on Remote sensing, concentrating on Natural disaster and intersecting with Survey data collection.
Norman Kerle interconnects Binary classification and Contextual image classification in the investigation of issues within Convolutional neural network.
His studies deal with areas such as Image processing, Photogrammetry, Systems engineering and Flexibility as well as Point cloud.
Between 2016 and 2021, his most popular works were:
- Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning (106 citations)
- Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms (62 citations)
- Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks (38 citations)
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