What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Artificial intelligence, Computer vision, Point cloud, Remote sensing and Lidar are his primary areas of study.
His research on Artificial intelligence often connects related areas such as Pattern recognition.
As part of his studies on Computer vision, Jonathan Li often connects relevant subjects like Completeness.
His work in Point cloud addresses issues such as Object detection, which are connected to fields such as Information extraction.
His study in Remote sensing is interdisciplinary in nature, drawing from both Line, Mobile mapping and Hydrological modelling.
The study incorporates disciplines such as Dashboard, Line segment, Scale and Standard deviation in addition to Lidar.
His most cited work include:
- Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework (279 citations)
- Using mobile laser scanning data for automated extraction of road markings (159 citations)
- Using mobile laser scanning data for automated extraction of road markings (159 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Point cloud, Computer vision, Pattern recognition and Remote sensing.
His study looks at the relationship between Artificial intelligence and topics such as Lidar, which overlap with Ranging.
His studies in Point cloud integrate themes in fields like Object detection, Point, Robustness and Algorithm.
His research in Computer vision tackles topics such as Cluster analysis which are related to areas like Euclidean distance.
His research in Pattern recognition intersects with topics in Contextual image classification, Pixel and Object.
His Remote sensing study frequently draws connections between related disciplines such as Land cover.
He most often published in these fields:
- Artificial intelligence (71.40%)
- Point cloud (47.92%)
- Computer vision (41.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (71.40%)
- Point cloud (47.92%)
- Pattern recognition (27.08%)
In recent papers he was focusing on the following fields of study:
Jonathan Li mainly focuses on Artificial intelligence, Point cloud, Pattern recognition, Deep learning and Lidar.
His Computer vision research extends to Artificial intelligence, which is thematically connected.
His Augmented reality study, which is part of a larger body of work in Computer vision, is frequently linked to Road surface, bridging the gap between disciplines.
In his research on the topic of Point cloud, Data mining is strongly related with Point.
His Deep learning research incorporates elements of Object detection and Remote sensing.
His Lidar research includes elements of Ranging and Multispectral image.
Between 2019 and 2021, his most popular works were:
- A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery (29 citations)
- Squeeze-and-Attention Networks for Semantic Segmentation (25 citations)
- Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Jonathan Li mainly investigates Artificial intelligence, Point cloud, Deep learning, Pattern recognition and Lidar.
His Artificial intelligence study frequently involves adjacent topics like Point.
His Point cloud study is concerned with the larger field of Computer vision.
While the research belongs to areas of Computer vision, Jonathan Li spends his time largely on the problem of Simultaneous localization and mapping, intersecting his research to questions surrounding Parking lot, GNSS applications, Word error rate and Global Positioning System.
His Deep learning research includes themes of Object, Intelligent transportation system and Discriminative model.
The various areas that Jonathan Li examines in his Lidar study include Calibration, Perspective, Ranging and Camera resectioning.
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