Muhammad Ateeq | Remote Sensing | Excellence in Research Award

Published on by MTE Awards

Mr. Muhammad Ateeq | Remote Sensing | Excellence in Research Award

Aerospace Information Research Institute Chinese Academy of Sciences | China

Mr. Muhammad Ateeq’s research integrates computer science, machine learning, and remote sensing to deliver scalable, data-driven solutions for Earth observation and energy planning. His work advances multi-sensor data fusion, image segmentation, and environmental change detection, with strong emphasis on computational rigor and real-world applicability. A notable contribution is his scenario-based spatial assessment framework for hybrid solar–wind energy systems, combining geospatial analytics with techno-economic modeling to support resilient renewable infrastructure planning. His research also extends to deep learning–based plant disease detection, demonstrating high-accuracy classification using transfer learning, and to network security analysis in next-generation communication systems. Collectively, these works highlight methodological versatility and cross-domain relevance. As reflected in his ORCID profile, he has 2 indexed journal publications, an emerging h-index, and a growing citation record, underscoring increasing scholarly visibility and impact.

Citation Metrics

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50

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5

h-index
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Yue Zhang | Remote Sensing Technology | Research Excellence Award

Published on by MTE Awards

Dr. Yue Zhang | Remote Sensing Technology | Research Excellence Award

Postdoctoral Fellow at Regional Centre for Space Science and Technology Education in Asia and the Pacific(China) | China

Dr. Yue Zhang is a rapidly emerging researcher whose work spans machine learning, remote sensing, hydrological forecasting, and environmental monitoring systems, producing a research portfolio with 168 citations, an h-index of 7, and 6 i10-index publications that reflect consistent and meaningful scientific impact. His research focuses on developing advanced hybrid deep-learning architectures-including LSTM, GRU, ConvLSTM, CNN-LSTM, STA-GRU, and physics-informed transformer networks-to improve the reliability and interpretability of streamflow, flood, water-level, and dissolved-oxygen forecasting, using multistation real-time datasets and temporal–spatial data linkages for enhanced predictive accuracy. He has significantly contributed to remote sensing applications by integrating GNSS-R signals, spatiotemporal attention models, and soft physical constraints to advance marine foreign-object monitoring, wind-speed retrieval, and seawater-intrusion early-warning systems. His work further includes innovations in GIS-enabled environmental intelligence platforms, real-time disturbance-response modelling, and image-level early-warning mechanisms for complex marine scenarios. His publication record spans reputable journals such as Water, Intelligence and Robotics, and Remote Sensing, covering topics including intelligent flood forecasting, lake water-quality management, deep-learning approaches for environmental and agricultural monitoring, and hybrid modelling methods for large-scale hydrological systems. Through interdisciplinary collaboration, contributions to international research initiatives, and development of system-integrated monitoring frameworks, Yue Zhang continuously advances the state of the art in environmental data science, demonstrating clear leadership potential and strong alignment with the goals of high-impact research recognition.

Profile : Google Scholar

Featured Publications

Deng, Y., Zhang, Y., Pan, D., Yang, S. X., & Gharabaghi, B. (2024). Review of recent advances in remote sensing and machine learning methods for lake water quality management. Remote Sensing, 16(22), 4196. Cited by: 53

Zhang, Y., Zhou, Z., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2023). Flood forecasting using hybrid LSTM and GRU models with lag time preprocessing. Water, 15(22), 3982. Cited by: 36

Zhang, Y., Gu, Z., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2022). The discharge forecasting of multiple monitoring stations for Humber River by hybrid LSTM models. Water, 14(11), 1794. Cited by: 36

Zhou, Z., Zhang, Y., Gu, Z., & Yang, S. X. (2023). Deep learning approaches for object recognition in plant diseases: A review. Intelligence and Robotics, 3(4), 514–537. Cited by: 12

Zhang, Y., Pan, D., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2023). Intelligent flood forecasting and warning: A survey. Intelligence and Robotics, 3(2), 190–212. Cited by: 12