Rui Hou, SEX: Male, NATIONALITY: China, PRESENT POSITION: Assistant Researcher, DATE OF BIRTH: 8th March 1999
CONTRACT DETAILS
Address: Aerospace Information Research Institute, Chinese Academy of Sciences
No.9 Dengzhuang South Road, Haidian District, Beijing 100094, P.R.China.
Email: hourui21@mails.ucas.ac.cn
Mobile: 13910769496
ACADEMIC AND PROFESSIONAL INTERESTS
Rui Hou, Ph.D. Assistant Researcher, Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) Dr. Rui Hou primarily conducts research in the field of forestry remote sensing. He has participated in eight research projects, including those funded by the National Natural Science Foundation of China (NSFC), the National Key Research and Development Program of China, and the Major Emergency Science and Technology Projects of the National Forestry and Grassland Administration. To date, he has published four academic papers in peer-reviewed SCI/EI journals as the first author and holds one authorized national invention patent.
ACADEMIC QUALIFICATIONS
Sept. 2021 – June 2026
Ph.D. in Cartography and Geographic Information System
University of Chinese Academy of Sciences (UCAS), Beijing, China
Sept. 2017 – June 2021
B.S. in Geographic Information Science
Beijing Forestry University, Beijing, China
Research and Academic Experience
July 2026 – Present
Assistant Researcher
Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing, China
Host or participate in scientific research projects
[1] Task of the National Key Research and Development Program of China: Research on Intelligent and Precise Monitoring, Early Warning Technology, and Management Platform Construction for Pine Wilt Disease (2021YFD1400902), 2021.12-2024.11, 5 million RMB. Completed, Participant
[2] Sub-task of the National Key Research and Development Program of China: Construction of Terrestrial Ecosystem Risk Assessment Grading System and Safety Early Warning System (2024YFE0198603), 2024.12-2028.11, 200,000 RMB. Ongoing, Participant
[3] Youth Program of the National Natural Science Foundation of China: Satellite Remote Sensing Identification of Pine Wilt Disease Infected Trees in Complex Observation Scenarios (42201355), 2023.01-2025.12, 300,000 RMB. Completed, Participant
[4] Major Emergency Science and Technology Project of the National Forestry and Grassland Administration: Big Data-Based Early Warning and Management System for Pine Wilt Disease Disasters (2021-KJC-Y-0401), 2021.01-2022.12, 400,000 RMB. Completed, Participant
[5] Central Government-funded Forestry and Grassland Technology Promotion and Demonstration Project: Promotion and Demonstration of a Fine-grained Regulation Platform for Pine Wilt Disease Outbreaks Based on Forest and Grassland Ecological Perception, 2024.10-2027.10, 250,000 RMB. Ongoing, Participant
[6] Special Project of the National Forestry and Grassland Administration: Remote Sensing Monitoring Data Processing and Sample Library Construction for Pine Wilt Disease, 2022.11-2023.12, 150,000 RMB. Completed, Participant
[7] Beijing Municipal Project for Integrated Prevention and Control of Forest Pests and Diseases: Intelligent Forecasting of Potential Forest Pest Outbreaks Based on Satellite-UAV Remote Sensing (BJLHZB-FW-20240025), 2024.04-2024.12, 435,000 RMB. Completed, Participant
[8] Zhejiang Provincial Key R&D Project for Emergency Management: Research on Early Warning and Dynamic Simulation Technologies of Forest Fires, 2025.03-2026.02, 980,000 RMB. Completed, Participant
PUBLICATIONS
[1] Hou R, Zhang B, Fang G, Yang S, Guo L, Huang W, Yao J, Jiao Q, Sun H, Yan J. Early Detection of Pine Wilt Disease by Combining Pigment and Moisture Content Indices Using UAV-Based Hyperspectral Imagery[J]. Remote Sensing, 2025, 17(11): 1833.
[2] Hou R, Zhou Y, Wang Y, Huang Z, Yao J, Jiao Q, Huang W, Zhang B. MBA-Former: A Boundary-Aware Transformer for Synergistic Multi-Modal Representation in Pine Wilt Disease Detection from High-Resolution Satellite Imagery[J]. Forests, 2026, 17, 517.
[3] Hou R, Fang G, Wang Y, Guo L, Yan J, Jiao Q, Yao J, Chen F, Li M, Zhang W, Huang W, Zhang B. Identification of optimal detection timing for early pine wilt disease monitoring using hyperspectral time-series analysis[J]. Ecological Informatics, 2026, 96: 103802.
[4] Hou R, Zhang B, Xu S, Dong Y. Deep learning-based precise identification of Areca catechu palms infected with yellowing disease using UAV remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2026, 42(9), 250–258 (in Chinese with English abstract).