Lecturer at Henan Normal University | China
Lipeng Jiao is a dedicated researcher and academic specializing in deep learning-based remote sensing, with a strong focus on vegetation time-series modeling and disturbance detection. Currently serving as a lecturer at the School of Tourism, Henan Normal University, China, he has developed expertise in integrating advanced computational methods with environmental monitoring and ecological analysis. His career reflects a balance of theoretical knowledge and practical applications, demonstrated by his active role in large-scale national research projects and collaborations with international institutions. With publications in highly regarded journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and GIScience & Remote Sensing, he has established himself as a promising scholar in his field. His research contributions address global environmental challenges, particularly in sustainable land use and ecological monitoring. Through his work, he continues to contribute to both academic advancement and societal well-being.
Professional Profiles
Scopus Profile | ORCID Profile
Education
Lipeng Jiao has pursued a strong educational foundation in surveying, mapping, and geographic information systems, building a career rooted in both technical depth and interdisciplinary applications. He earned his bachelor’s degree in surveying and mapping engineering from Shangqiu Normal University, which provided him with the fundamental skills for spatial data analysis and geoscience research. He further advanced his expertise with a master’s degree in surveying and mapping engineering from the China University of Mining and Technology in Beijing, where he specialized in advanced mapping technologies and environmental data interpretation. He then completed his doctoral studies in cartography and geographic information systems at Beijing Normal University, focusing on remote sensing and ecological monitoring. In addition to his domestic education, he broadened his academic perspective through an international visiting scholar program at Virginia Tech in the United States, where he collaborated on advanced research in vegetation dynamics and remote sensing applications.
Professional Experience
Lipeng Jiao is currently serving as a lecturer at the School of Tourism, Henan Normal University, where he is actively engaged in teaching, research, and mentoring students in areas related to remote sensing and environmental studies. His professional journey is marked by extensive involvement in major research initiatives, including participation in national key research and development programs in China. He has contributed to projects that focus on global remote sensing monitoring, land use change, and ecological simulations, establishing himself as an integral member of multidisciplinary research teams. His international exposure as a visiting scholar at Virginia Tech in the United States allowed him to collaborate with leading experts and enhance his research perspective. In addition to his teaching and research responsibilities, he actively contributes to the dissemination of knowledge through publications in recognized journals. His professional experience reflects a commitment to combining scientific innovation with practical applications in environmental sustainability.
Research Interest
Lipeng Jiao’s research interests are centered on the application of deep learning techniques in remote sensing, with a particular emphasis on vegetation time-series modeling and the detection of ecological disturbances. He is passionate about developing advanced computational methods that can improve the monitoring and interpretation of environmental changes across diverse ecosystems. His studies focus on vegetation disturbance detection, attribution of change agents, and mapping of ecological processes, which are critical for understanding the impacts of climate change and human activities on natural resources. He is also interested in synergizing multi-source satellite data to achieve near real-time monitoring of phenomena such as burned areas and vegetation degradation. By integrating cutting-edge artificial intelligence methods with remote sensing data, his research contributes to the improvement of global ecological monitoring systems. His interests extend toward practical applications, aiming to support sustainable resource management and policy-making for environmental conservation.
Research Skill
Lipeng Jiao possesses a diverse set of research skills that enable him to address complex challenges in remote sensing and environmental monitoring. He is proficient in applying deep learning algorithms to process and analyze large-scale vegetation time-series data, allowing for the detection and attribution of ecological disturbances with high accuracy. His expertise extends to multi-source satellite data integration, enhancing the capability to conduct near real-time environmental assessments. He is skilled in geographic information systems, cartography, and advanced data analysis methods that support spatial and temporal modeling. His contributions to national research projects highlight his ability to work within interdisciplinary teams, manage data-intensive tasks, and produce impactful outcomes. Additionally, his international research exposure has strengthened his adaptability to diverse scientific approaches and collaborative environments. These skills position him as a researcher capable of advancing both theoretical innovations and practical applications in ecological monitoring and sustainability science.
Publications Top Notes
Title: Robust Identification of Vegetation Change Using Shapelet-Based Temporal Segmentation of Landsat Time-Series Stacks: A Case Study in the Qilian Mountains
Authors: Lipeng Jiao; Randolph H. Wynne
Year: 2025
Title: Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data
Authors: Lipeng Jiao; Yanchen Bo
Year: 2022
Conclusion
Lipeng Jiao is a deserving candidate for the Best Researcher Award due to his significant contributions in applying deep learning to vegetation remote sensing, advancing the understanding of ecological changes and land use impacts. His work on vegetation disturbance detection, participation in major research projects, and high-quality publications demonstrate both scientific excellence and societal relevance. With his strong research foundation, international experience, and potential for leadership in collaborative and innovative projects, he is well-positioned to continue making impactful contributions to his field and the broader research community.