Trustworthy AI
Robust, interpretable and aligned systems that earn confidence in high-stakes settings.
RESEARCH GROUP · EST. 2026
We study intelligence
at the edge of possibility.
Frontier Intelligence Lab is an interdisciplinary research group building reliable, human-centered AI for science and society.
01 / EXPLORE
UNCERTAINTY → INSIGHT
WHO WE ARE
Curiosity-driven research.
Consequential outcomes.
We bring together researchers across machine learning, data science, and domain sciences. Our work asks fundamental questions while creating tools that make a difference beyond the lab.
Meet the group ↘WHAT WE EXPLORE
Research scope
We work across methods and domains to develop intelligence that is capable, dependable, and useful.
03
ONE GROUP, MANY LENSES
“The questions worth asking
rarely fit inside one discipline.”
OUR COMMUNITY
People
LC
PRINCIPAL INVESTIGATOR
Dr. Chong Liu
Remote sensing · Land cover change · Human–environment interaction
liuc@mail.sysu.edu.cn ↗PHD STUDENT
Member Name
Reliable ML
PHD STUDENT
Member Name
Scientific AI
NEXT COULD BE YOU
We are growing.
See opportunities ↗RESEARCH OUTPUTS
Projects & publications
From fundamental methods to open scientific evidence for understanding land-surface change.
SELECTED PROJECTS
2024–2027
Subpixel extraction and dynamic monitoring of three-dimensional urban impervious-surface expansion
National Natural Science Foundation of China · General Program2022–2024
Spatiotemporal change in polar vegetation photosynthetic phenology and its drivers
Natural Science Foundation of Guangdong ProvinceSELECTED PUBLICATIONS
2023 · Earth System Science Data
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
Liu, C. et al. ↗2022 · Science Bulletin
Arctic’s man-made impervious surfaces expanded by over two-thirds in the 21st century
Liu, C. et al. ↗View all publications ↗OPPORTUNITIES
Bring your questions.
Build what matters.
We welcome curious and kind people from diverse backgrounds. If you are excited by hard problems and thoughtful collaboration, we would love to hear from you.
WAYS TO JOIN
Graduate researchers
PhD and master's applicants with strong foundations and a taste for open-ended questions.
Undergraduate researchers
Motivated students seeking a mentored, hands-on research experience.
Collaborators
Scientists, practitioners, and institutions pursuing meaningful shared work.
FROM THE LAB
Latest news
Milestones, field notes, and opportunities from our group.
OPEN DATA & PRODUCTS
Flagship resources
Three representative remote-sensing resources created or contributed to by our group.
01 / LAND COVER
CALC-2020
Circumpolar Arctic land cover at 10 m resolution.Explore dataset ↗02 / AGRICULTURE
GCI30
The first global 30 m cropping-intensity product, derived from multi-source satellite time series.Open in GEE ↗03 / DATA ARCHIVE
Zenodo
Open research data and reproducible resources from the group.Visit archive ↗PRINCIPAL INVESTIGATOR
Academic profile
刘冲副教授
Associate Professor Chong Liu
刘冲，中山大学遥感科学与技术学院副教授、硕士生导师。从事地表覆盖遥感信息提取与应用研究，主要包括人造地表遥感、全球/极区地表覆盖制图与变化监测，以及人类活动与地表环境相互作用。
工作经历 / Experience
- 2024–至今 · 中山大学遥感科学与技术学院 · 副教授
- 2019–2023 · 中山大学遥感科学与技术学院 · 专职副研究员
- 2015–2019 · 江西师范大学地理与环境学院 · 讲师
教育背景 / Education
- 2010–2015 · 武汉大学 · 测绘遥感信息工程国家重点实验室 · 硕博连读博士
- 2013–2014 · UNC at Chapel Hill · 地理系 · 联合培养博士
- 2006–2010 · 武汉大学 · 遥感信息工程学院 · 学士
讲授课程 / Teaching
- 《城市遥感》
- 《数字图像处理》
- 《3S前沿讲座》
- 《高光谱遥感》
- 《3S综合实习》
学术服务 / Service
- 中国测绘学会对地观测工作委员会委员
- 国际数字地球学会数字极地专业委员会委员
科研项目 / Research projects
- 国家重点研发计划项目“北极冻土区温室气体和污染物监测与环境风险评估合作研究”子专题（2026–2028）
- 国家自然科学基金面上项目“城市不透水地物立体扩张信息亚像素遥感提取与动态监测”（2024–2027）
- 广东省自然科学基金面上项目“极地植被光合物候时空变化及其驱动要素研究”（2022–2024）
- 广州市科技计划项目“基于多源遥感数据的城市物候信息提取研究”（2021–2023）
- 国家重点研发计划“三极国际大科学计划战略研究”子课题（2020–2022）
- Microsoft AI for Earth Azure Computing Grant（2020–2021）
- 国家自然科学基金青年项目“基于多源遥感影像时空融合的快速城市化地区长时序不透水面信息提取研究”（2017–2019）
- 江西省自然科学基金青年项目“环鄱阳湖地区不透水面时空变化及其生态环境效应研究”（2016–2018）
代表性论著 / Selected publications
- 程晓，黄华兵，叶玉芳、刘冲等. 全球生态环境遥感监测2022年度报告—北极地区冰雪与植被变化.
- Liu, C., Zhang, Q., Huang, H., Xu, H. and Cheng, X.*, 2025. Identifying where and when urban renewal occurs: a continuous change detection-based framework using two decades’ worth of Landsat data. International Journal of Digital Earth, 18(1), 2510573.
- Liu, C., Huang, H., Zhang, Q., Feng, X., Hou, X., Liu, C., Xu, H. and Cheng, X.*, 2024. Fine-resolution mapping and assessment of artificial surfaces in the northern hemisphere permafrost environments. International Journal of Digital Earth, 17(1), 2302579.
- Liu, C., Xu, X., Feng, X., Cheng, X., Liu, C.* and Huang, H.*, 2023. CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic. Earth System Science Data, 15(1), 133–153.
- Xu, H., Zhou, Y., Wei, Y., Liu, C.*, Li, X. and Chen, W., 2023. A Relative Radiometric Normalization Method for Enhancing Radiometric Consistency of Landsat Time-Series Imageries. IEEE JSTARS, 16, 5797–5812.
- Liu, C., Huang, H., Zhang, Q., Chen, X., Xu, X., Xu, H. and Cheng, X., 2022. Arctic’s man-made impervious surfaces expanded by over two-thirds in the 21st century. Science Bulletin, 67, 1425–1429.
- Xu, H., Qi, S., Li, X., Gao, C., Wei, Y. and Liu, C.*, 2021. Monitoring three-decade dynamics of citrus planting in Southeastern China using dense Landsat records. International Journal of Applied Earth Observation and Geoinformation, 103, 102518.
- Zhang, M., Wu, B., Zeng, H., He, G., Liu, C.*, et al., 2021. GCI30: A global dataset of 30-m cropping intensity using multisource remote sensing imagery. Earth System Science Data.
- Liu, C., Huang, H., Hui, F., Zhang, Z. and Cheng, X.*, 2021. Fine-resolution mapping of pan-arctic lake ice-off phenology based on dense Sentinel-2 time series data. Remote Sensing, 13(14), 2742.
- Liu, C., Zhang, Q., Tao, S., Qi, J., Ding, M., Guan, Q., Wu, B.*, et al., 2020. A new framework to map fine resolution cropping intensity across the globe. Remote Sensing of Environment, 251, 112095.
- Liu, C., Zhang, Q., Luo, H., Qi, S., Tao, S., Xu, H. and Yao, Y.*, 2019. An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks. Remote Sensing of Environment, 229, 114–132.
- Xu, H., Qi, S., Gong, P., Liu, C.* and Wang, J., 2018. Long-term monitoring of citrus orchard dynamics using time-series Landsat data. International Journal of Remote Sensing, 39(22), 8271–8292.