2023 - IEEE Fellow For contributions to point cloud analytics in LiDAR remote sensing
2023 - Research.com Computer Science in Canada Leader Award
2022 - Fellow of the Engineering Institute of Canada
2022 - Fellow of the Canadian Academy of Engineering
2022 - Fellow of the Asia-Pacific Artificial Intelligence Association
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
Zilong Zhong;Jonathan Li;Zhiming Luo;Michael Chapman.
IEEE Transactions on Geoscience and Remote Sensing (2018)
A study on DEM-derived primary topographic attributes for hydrologic applications: Sensitivity to elevation data resolution
Simon Wu;Jonathan Li;G.H. Huang.
Using mobile laser scanning data for automated extraction of road markings
Haiyan Guan;Jonathan Li;Jonathan Li;Yongtao Yu;Cheng Wang.
Isprs Journal of Photogrammetry and Remote Sensing (2014)
Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds
Bisheng Yang;Lina Fang;Jonathan Li.
Isprs Journal of Photogrammetry and Remote Sensing (2013)
Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data
Xianfeng Zhang;Chunhua Liao;Jonathan Li;Quan Sun.
International Journal of Applied Earth Observation and Geoinformation (2013)
Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds
Yongtao Yu;Jonathan Li;Haiyan Guan;Cheng Wang.
IEEE Transactions on Geoscience and Remote Sensing (2015)
Use of mobile LiDAR in road information inventory: a review
Haiyan Guan;Jonathan Li;Shuang Cao;Yongtao Yu.
International Journal of Image and Data Fusion (2016)
Automated Extraction of Road Markings from Mobile Lidar Point Clouds
Bisheng Yang;Lina Fang;Qingquan Li;Jonathan Li.
Photogrammetric Engineering and Remote Sensing (2012)
Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds
Yongtao Yu;Jonathan Li;Haiyan Guan;Fukai Jia.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2015)
A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery
Linlin Xu;Jonathan Li;Jonathan Li;Alexander Brenning.
Remote Sensing of Environment (2014)
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