PhD Student, University of Electronic Science and Technology of China
Pakezhamu Nuradili is a PhD student from China currently working in the field of remote sensing, deep learning, and UAV-based image processing. Of Kazakh ethnicity, she splits her time between Chengdu, China, and Trento, Italy, through a joint PhD program. She is passionate about applying advanced technologies to environmental monitoring and image segmentation. With proficiency in Python, MATLAB, ENVI, and other technical software, Pakezhamu’s research focuses on improving UAV image processing, especially for low-light and multispectral scenarios. Outside of academia, she enjoys painting, photography, dancing, hiking, and traveling. Pakezhamu is known for her meticulous attention to detail, strong communication skills in multiple languages, and organizational ability, which have made her a valuable contributor to both academic research and teaching roles.
Profile
Education
Pakezhamu Nuradili completed her high school education at Jiangpu Senior High School, Jiangsu Province, China, in 2013. She obtained her Bachelor’s Degree in Electronics and Information Engineering from Hebei University of Science and Technology in 2017. In 2020, she graduated with a Master’s degree in Radio Physics from Yili Normal University, where her research focused on face recognition algorithms. Currently, Pakezhamu is pursuing a PhD in Information and Communication Engineering at the University of Electronic Science and Technology of China, with a joint program at the University of Trento, Italy. Her doctoral research is centered on UAV-based remote sensing, particularly the use of deep learning for image semantic segmentation and environmental monitoring. Her academic journey has been marked by a strong commitment to research excellence and interdisciplinary collaboration.
Experience
Pakezhamu has accumulated valuable teaching and research experience throughout her academic career. From 2017 to 2021, she worked as a substitute teacher at Silk Road College of Ili, Yili Normal University, and Ili Vocational and Technical College, teaching courses on basic computer applications, advanced mathematics, and basic computer studies. During her time at the University of Electronic Science and Technology (UESTC), she served as a graduate teaching assistant for the Principles of Remote Sensing course. Her research experience is extensive, particularly in UAV-based remote sensing, semantic image segmentation, and the integration of deep learning with multispectral and thermal infrared data. She has co-authored multiple publications in prestigious journals and presented her work at international conferences like IGARSS. Pakezhamu has also volunteered at various events, including the Trento Marathon, further showcasing her commitment to community engagement and leadership.
Awards and Honors
Pakezhamu has received numerous academic accolades throughout her educational journey. She was awarded the Hebei Provincial Inspiration Scholarship in 2016 for her outstanding academic performance. In 2017, she received the Outstanding Graduation Design Award from Hebei University of Science and Technology. During her Master’s degree, she earned the Graduate Student Scholarship from Ili Normal University in 2018 and the Xinjiang Autonomous Region Postgraduate Scholarship in 2019. At the University of Electronic Science and Technology of China, she has been honored with several Academic Scholarships (2022, 2023, 2024) in recognition of her exemplary research and academic achievements. She was also named Outstanding Teaching Assistant for the Principles of Remote Sensing course in 2022. These awards reflect her dedication to both academic excellence and teaching, solidifying her reputation as an emerging leader in her field.
Research Focus
Pakezhamu’s research focuses on advanced image processing techniques for remote sensing, particularly through UAV-based data. Her work integrates deep learning methods to improve semantic segmentation of UAV imagery, with an emphasis on applications in environmental monitoring, such as wetland and fire detection. Her research explores the use of multispectral and thermal infrared images, focusing on low-light conditions and improving image quality for accurate segmentation. Key areas of interest include UAV image processing, semantic segmentation, deep learning, and multispectral/thermal image fusion. She is actively contributing to the development of novel algorithms that enhance image interpretation, especially in complex environmental scenarios. Her work has broad applications in precision agriculture, environmental monitoring, and disaster management, offering solutions to real-world challenges by combining state-of-the-art technology with practical needs. As a PhD candidate, she continues to push the boundaries of remote sensing technologies and their applications in sustainable development.
Publications
- UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features – IEEE Journal on Miniaturization for Air and Space Systems (2023) ✨
- Semantic Segmentation for UAV Low-Light Scenes Based on Deep Learning and Thermal Infrared Image Features – International Journal of Remote Sensing (2024) 🌙
- Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer – IGARSS 2024, IEEE International Geoscience and Remote Sensing Symposium 📷
- Deep Learning Method for Wetland Segmentation in UAV Multispectral Imagery – Remote Sensing (2024) 🌍
- Fire Detection Based on Deep Learning Segmentation Methods – Journal (2024) 🔥
- Removing Temperature Drift and Temporal Variation in Thermal Infrared Images of a UAV Uncooled Thermal Infrared Imager – ISPRS Journal of Photogrammetry and Remote Sensing (2023) 🌡️