Master's Candidate | Henan Polytechnic University | China
Mr. Yuyao Wang, a Master’s candidate in Cartography and Geographic Information Systems at Henan Polytechnic University, has made significant contributions to remote sensing and artificial intelligence integration for agricultural and environmental monitoring. His research primarily focuses on remote sensing image processing, land use classification, and artificial intelligence-driven multi-source data fusion. With strong expertise in applying artificial intelligence to satellite data interpretation, Mr. Wang has developed novel frameworks for rice field identification in mountainous regions and cloud removal techniques using SAR and optical imagery fusion. His innovative use of artificial intelligence in remote sensing enhances precision agriculture and sustainable land management practices. Mr. Wang’s professional journey reflects his commitment to applying artificial intelligence and geospatial analytics to complex real-world challenges. His works published in prestigious journals like Remote Sensing and PLOS ONE demonstrate robust analytical skills, methodological innovation, and interdisciplinary thinking powered by artificial intelligence. He continues to explore advanced artificial intelligence techniques for improving data accuracy and environmental insights, bridging gaps between technology and agricultural sciences. Mr. Wang’s achievements highlight excellence in artificial intelligence research, demonstrating potential for transformative advancements in geoinformatics and environmental sustainability. His dedication, scientific curiosity, and mastery of artificial intelligence tools reinforce his reputation as a promising researcher in remote sensing and GIS. 12 Citations, 3 Documents, 2 h-index.
Profiles: Scopus | ORCID
Featured Publications
1. Wang, Y., Cheng, J., Yuan, Z., & Zang, W. (2025). Research on rice field identification methods in mountainous regions. Remote Sensing, 17(19), 3356.
2. Wang, Y. (2025, March 19). A novel cloud removal method by fusing features from SAR and neighboring optical remote sensing images.