Dr. Hong Yang | Remote Sensing Archaeology | Best Researcher Award

Dr. Hong Yang, wuhan university, China.

Dr. Hong Yang 🎓 is a dedicated Ph.D. researcher at Wuhan University 🏛️ specializing in remote sensing archaeology and deep learning 🤖. With over ten academic publications and contributions to national key R&D programs, he merges AI and heritage science in innovative ways. His work on LiDAR-based semantic segmentation enhances archaeological site analysis 🏺🌲. Dr. Yang has also engaged in industry projects and patent development, reflecting his commitment to impactful and interdisciplinary research. 🌐📊

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🎓 Early Academic Pursuits

Dr. Hong Yang is currently pursuing a Ph.D. at Wuhan University, one of China’s premier institutions. His early academic path has been marked by dedication to research and a clear focus on technological advancement. With a strong foundation in geospatial sciences and computer science, Dr. Yang quickly demonstrated a keen interest in the integration of remote sensing and artificial intelligence, setting the stage for his impactful academic journey. 📚🛰️

💼 Professional Endeavors

As a researcher at Wuhan University, Dr. Yang has actively contributed to numerous national-level key R&D programs. Despite being in the early stages of his career, he has already engaged in four consultancy and industry-sponsored projects, showcasing a strong link between academia and practical applications. His growing research portfolio includes one patent under process, and participation in one major research project, reflecting both innovation and hands-on experience. 🔬🏢

🔬 Contributions and Research Focus On Remote Sensing Archaeology

Dr. Yang’s primary research interests lie at the intersection of remote sensing archaeology and deep learning. His recent study, which applies a semantic segmentation model integrating LiDAR-derived data with convolutional neural networks, has advanced the ability to detect archaeological features in forested areas. Notably, he introduced multi-channel LiDAR data processing and incorporated a channel attention mechanism to enhance the performance of both Unet and TransUNet models. This novel approach has contributed significantly to machine-learning-driven archaeology. 🧠🌍📡

🌍 Impact and Influence

Dr. Yang’s research has begun to resonate in the scientific community, with six peer-reviewed journal publications in esteemed SCI and Scopus-indexed journals. His citation index of 22 reflects early recognition of his work’s value, especially in the niche area of remote sensing in archaeology. Through national collaborations and interdisciplinary integration, Dr. Yang is fostering technological progress with cultural preservation. 📈🌐

🧠 Research Skills

Dr. Yang is proficient in:

• Deep learning model development (e.g., Unet, TransUNet)

• Semantic segmentation using LiDAR

• Data augmentation and multi-channel synthesis

• Integration of channel attention mechanisms

• Remote sensing applications in cultural heritage

His multidisciplinary expertise enables him to explore complex datasets and deliver impactful insights. 💻🗺️

🏅 Awards and Honors

Dr. Yang has been nominated for the Best Researcher Award, recognizing his significant contributions to research and innovation in the emerging fields of AI and archaeology. This nomination highlights both the originality and relevance of his work, which has bridged historical study with cutting-edge technology. 🏅🔍

🏛️ Legacy and Future Contributions

Looking forward, Dr. Yang aims to further refine machine learning tools for archaeological detection and preservation. He envisions expanding his research to global heritage sites, contributing to digital archaeology, and fostering international collaborations. His legacy is taking shape as a pioneer in combining AI-driven methods with cultural preservation, ensuring that the past is protected with the tools of the future. 🌐🏛️🤖

Publications Top Notes

1. 📡 Segmenting ancient cemeteries under forests using synthesized LiDAR-derived data and deep convolutional neural network
   🗞️ npj Heritage Science — 2025

2. 🌍 Predicting ancient city sites using GEE coupled with geographic element features and temporal spectral features: a case study of the Neolithic and Bronze Age of the Jianghan region, China
   🗞️ npj Heritage Science — 2025

3. 🏯 Moated site object detection using time series satellite imagery and an improved deep learning model in northeast Thailand
   🗞️ Journal of Archaeological Science — 2024

4. 🌊 Fitting profile water depth to improve the accuracy of lake depth inversion without bathymetric data based on ICESat-2 and Sentinel-2 data
   🗞️ International Journal of Applied Earth Observation and Geoinformation — 2023

5. 💧 Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning
   🗞️ International Journal of Digital Earth — 2022

6. 🛰️ Bathymetric Inversion and Mapping of Two Shallow Lakes Using Sentinel-2 Imagery and Bathymetry Data in the Central Tibetan Plateau
   🗞️ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing — 2022

7. 🏞️ Multi-resolution satellite images bathymetry inversion of Bangda Co in the western Tibetan Plateau
   🗞️ International Journal of Remote Sensing — 2021