![Qian Liu](https://cafnr.missouri.edu/wp-content/uploads/2024/07/qian-liu-web-300x300.png)
Ph.D.
Assistant Professor
School of Natural Resources
Research at a glance
Area(s) of Expertise
Research Summary
Liu's research focuses on four aspects: 1. Climate and atmospheric factor detection, segmentation, and retrieval using artificial intelligence (AI) technologies; 2. Spatiotemporal theories and analysis in big earth data; 3. Remote sensing and climate/environmental data record processing and generation; 4. The ability of using the above to solve pressing issues in extreme weather events, climate change and natural disasters.
Climate factors and extreme weathers require accurate and effective detection, segmentation, and estimation, as well as the in-depth understanding of physical and atmospheric process. The increasing availability of remote sensing images, ground-based observations, and model simulations along with the rapid progress of computing technologies have provided us the unprecedented opportunity to better satisfy the above-mentioned requirements. In my research, beyond the traditional methodology for environmental factors and natural disasters analytics, I pursue high-efficient and automatic approaches by developing a series of AI and spatiotemporal methodologies and frameworks to detect, retrieve, classify and analyze climate factors such as cloud, air pollution, precipitation using various remote sensing data. With the intuitive analytical results, the research directly benefits the scientists, decision-makers, and the public for natural disaster and public health issue research and mitigation purposes.
I also lead the NOAA project: JPSS Life-Cycle Data Reprocessing, to reprocess Environmental Data Records (EDR) derived from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. This project aims to reprocess VIIRS EDRs using unified and consistent algorithms across the whole reprocessing period to minimize or remove the inconsistency due to different versions of retrieval algorithms and input sensor data records (SDR) adopted by the operational EDRs.
Educational background
- Ph.D., George Mason University, 2022