What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Ecology
- Statistics
His main research concerns Remote sensing, Point cloud, Artificial intelligence, Structure from motion and Computer vision.
His study in Remote sensing is interdisciplinary in nature, drawing from both Image resolution, Terrain and Spectroradiometer.
His Point cloud research is multidisciplinary, incorporating perspectives in Vegetation, Photogrammetry, Ranging and Lidar.
Arko Lucieer has included themes like Stereopsis and Digital elevation model in his Photogrammetry study.
The Artificial intelligence study combines topics in areas such as Multivariate statistics and Pattern recognition.
Arko Lucieer combines Computer vision and Georeference in his research.
His most cited work include:
- An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds (433 citations)
- Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery (401 citations)
- Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography: (391 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Remote sensing, Artificial intelligence, Computer vision, Vegetation and Lidar.
His study brings together the fields of Point cloud and Remote sensing.
His study looks at the relationship between Artificial intelligence and fields such as Pattern recognition, as well as how they intersect with chemical problems.
His work on Texture is typically connected to High resolution as part of general Computer vision study, connecting several disciplines of science.
His Vegetation study integrates concerns from other disciplines, such as Image resolution, Satellite imagery, Physical geography, Random forest and Field.
His biological study spans a wide range of topics, including Change detection, Forest management, Canopy, Ranging and Inertial measurement unit.
He most often published in these fields:
- Remote sensing (54.44%)
- Artificial intelligence (20.00%)
- Computer vision (14.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Remote sensing (54.44%)
- Hyperspectral imaging (12.78%)
- Vegetation (14.44%)
In recent papers he was focusing on the following fields of study:
Remote sensing, Hyperspectral imaging, Vegetation, Random forest and Lidar are his primary areas of study.
His Remote sensing research is multidisciplinary, relying on both Image resolution, Point cloud and Spectroradiometer.
The concepts of his Point cloud study are interwoven with issues in Photogrammetry and GNSS applications.
His studies deal with areas such as Remote sensing, Mean squared error, Structural complexity, Physical geography and Adaptive management as well as Vegetation.
His Random forest study deals with the bigger picture of Artificial intelligence.
His research integrates issues of Sample and Hyperspectral image classification in his study of Artificial intelligence.
Between 2018 and 2021, his most popular works were:
- Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery (16 citations)
- Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches (13 citations)
- A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Ecology
- Statistics
His primary scientific interests are in Remote sensing, Random forest, Hyperspectral imaging, Vegetation and Image resolution.
His Remote sensing study combines topics from a wide range of disciplines, such as Adaptive management, Point cloud and Spectroradiometer.
His study looks at the intersection of Random forest and topics like Transect with Multispectral image, Downscaling, Digital surface, Grassland and Themeda triandra.
His Hyperspectral imaging study is concerned with Artificial intelligence in general.
His Artificial intelligence research incorporates themes from Mean squared error and Computer vision.
His study in Vegetation is interdisciplinary in nature, drawing from both Remote sensing, Restoration ecology and Structural complexity.
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