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Computer Science

Professor Chunbo Luo

Associate Professor
Computer Science

We have a PhD studentship on AI for ocean health indicators. Click this for more details: https://www.exeter.ac.uk/study/funding/award/?id=5387.

 

Please visit my personal page for more details of the research, code and datasets.

 

Prof Luo's research focuses on machine learning and signal processing methods for collecting and processing Earth observations and remote sensing data (particularly high resolution data). Current projects include the development of novel visual machine learning models for ultra-high resolution datasets of natural disasters, river plumes, hyperspectral land cover datasets, more classical ML methods for POC fluxes estimation and landslide detection, as well as emerging foundation ML models for modelling Earth systems and controlling of UAVs for acquiring remote sensing data. These scientific investigations have high impact applications in areas such as natural disaster management and mitigation (see our novel datasets and machine learning models), and environment protection (see the relevant industry projects and publications). Specific examples include the detection of algal blooms captured in high resolution remote sensing data, tracking of marine carbon flux, and detection of marine oil spills etc. My work greatly benefits from collaborative research with leading research institutes, including the Plymouth Marine Laboratory, the Met Office, and the National Oceanographic Centre. Prof. Luo is a recipient of the ESI Highly Cited Papers recognition.

 

Prof Luo has organized and/or chaired (with other leading researchers) 11 conferences/workshops, including The 17th IEEE International Conference on Big Data Science and Engineering; The 20th International Conference on Computer and Information Technology; The 10th IEEE International Conference on Big Data and Cloud Computing; The 4th IEEE International Conference on Data Science and Systems; The 14th International Symposium on Pervasive Systems, Algorithms, and Networks; International Symposium on Advances in Data Science and Technologies; International Symposium on Advanced Topics in Pervasive Computing and Networking Technologies; The Machine Learning for Earth Observations in Exeter(2023, 2024); The BMVC workshop on Machine Vision for Earth Observation (2023, 2024).

 

I am looking for self-funded PhD applicants who have a strong motivation on the topic of your choice and good coding and/or math background.

 

Admin & Community Services

  • Deputy Director of Research and Impact (CS)
  • EU Horizon 2020 research management: Intelligent and Sustainable Aerial-Terrestrial IoT Networks
  • Theme lead: Remote Sensing, Institute for Data Science and Artificial Intelligence (IDSAI)
  • Steering commitee panel member of NERC NEODAAS

 

News 

  • PhD Studentship: Exeter University is offering 50 CSC funded projects. 留学基金委(CSC)和Exeter共提供50个博士全奖项目,欢迎同学们联系和申请。我直接指导申请流程,包括材料准备,优秀的样本,未来的科研选题等。我们课题组今年建议题目是AI/大模型、多模态、diffusion model等方向 Click here to apply!
  • Our exciting paper on foundation models in segmenting vehicular data can be found on IEEE Trans. Intelligent Vehicles (DOI: 10.1109/TIV.2024.3461651) 
  • Try my supervised project completed by Haoyang on classifiying land covers using deep learning models, and compare the changes happend in history. The system demonstration is here.
  • Machine learning for Earth Observation Workshop 2024 will be held in Exeter on 24/25 June. Click here to register the workshop!
  • We have 2 papers focusing on machine learning for remote sensing image processing accepted in IEEE International Symposium on Geoscience and Remote Sensing 2024 (IGARSS 2024).
  • We have 2 papers on the smart sensor networks for land movement detection and machine learning methods for environment data processing accepted in EGU 2024! Congratulations to all!
  • Our paper published in IEEE Network Magazine is avaialbe: Ubiquitous and Robust UxV Networks: Overviews, Solutions, Challenges, and Opportunities
  • Our remote sensing data processing and machine learning pipeline with code tools will be available soon.

 

News archive - 2023

 

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