Linlin (Lincoln) Xu, PhD
Dr. Linlin (Lincoln) Xu obtained dual BSc degrees in Geomatics Engineering and Computer Science from China University of Geosciences, where he also completed his MSc in Geodesy. He went to to obtain his PhD in Geography from the University of Waterloo in 2014. Before joining UCalgary, Dr. Xu served as an Associate Professor at China University of Geosciences and later as a Research Assistant Professor in the Department of Systems Design Engineering at the University of Waterloo.As an interdisciplinary researcher, Dr. Xu’s research is at the cutting edge of integrating machine learning, AI, and remote sensing technologies—including SAR, hyperspectral, multispectral, and LiDAR—to tackle pressing environmental challenges. In his key research areas, Dr. Xu has demonstrated excellence, leadership and recognition. He has secured many competitive grants, including NSERC DG, NSERC Alliance Option 2, MITACS, OCI and CSA ROSS. He published one book chapter, 75 journal papers and 45 conference articles on high-impact journals and conferences. He has been involved in many invited talks and presentations to different audiences, and he frequently serve as reviewers, guest editors, and associate editors for high-impact remote sensing journals and conferences. He has substantial teaching experiences and successful highly qualified personnel (HQP) training records. He has supervised and co-supervised 15 PhD students, 10 MASc students and 5 undergraduate students.
Areas of Research
AI and Machine Learning, Earth Observation, Remote Sensing, Geospatial Data Science, Environmental Monitoring
With the explosion of Geospatial and Remote Sensing (GRS) data, fast market growth is bottlenecked by the lack of intelligent analytics capable of automatically transferring large-volume, ever-increasing, noisy, heterogeneous raw GRS data into compact, real-time, value-added information product that is essential for critical environmental and climate-related applications. I and my HQPs tailor-design cutting-edge AI solutions to enable automatic generation of scalable geospatial information products in a high-precision, fast and cost-effective manner to better support various key applications in environmental monitoring, resource exploration and climate change studies. The current research topics include: (1) Big geospatial data science and AI analytics (2) Sensor integration (e.g., Hyperspectral, Multispectral, LiDAR and SAR, and passive microwave imaging) and low-cost DIY geospatial sensing systems (3) Integrated Pan-Arctic ocean environment (e.g., sea/lake ice, marine oil spills, Arctic species) mapping and monitoring using deep learning models (4) Integrated Northern land environment (e.g., burned area, wildfire, vegetation, soil, land cover/use, biophysical, biochemical and geochemical parameters) mapping using deep learning models (5) 3D urban environment modeling and digital twins via neural radiative field (NeRF) and Gaussian splatting We are looking for passionate new graduate and undergraduate students to join the team!
With the explosion of Geospatial and Remote Sensing (GRS) data, fast market growth is bottlenecked by the lack of intelligent analytics capable of automatically transferring large-volume, ever-increasing, noisy, heterogeneous raw GRS data into compact, real-time, value-added information product that is essential for critical environmental and climate-related applications. I and my HQPs tailor-design cutting-edge AI solutions to enable automatic generation of scalable geospatial information products in a high-precision, fast and cost-effective manner to better support various key applications in environmental monitoring, resource exploration and climate change studies. The current research topics include: (1) Big geospatial data science and AI analytics (2) Sensor integration (e.g., Hyperspectral, Multispectral, LiDAR and SAR, and passive microwave imaging) and low-cost DIY geospatial sensing systems (3) Integrated Pan-Arctic ocean environment (e.g., sea/lake ice, marine oil spills, Arctic species) mapping and monitoring using deep learning models (4) Integrated Northern land environment (e.g., burned area, wildfire, vegetation, soil, land cover/use, biophysical, biochemical and geochemical parameters) mapping using deep learning models (5) 3D urban environment modeling and digital twins via neural radiative field (NeRF) and Gaussian splatting We are looking for passionate new graduate and undergraduate students to join the team!
Supervising degrees
Geomatics Engineering - Doctoral: Seeking Students
Geomatics Engineering - Masters: Seeking Students
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