Dr. Nabil Bachagha | Remote Sensing | Best Researcher Award

Dr. Nabil Bachagha | Remote Sensing | Best Researcher Award

University of Leeds | United Kingdom

Dr. Nabil Bachagha is a distinguished Research Fellow and global expert in remote sensing, GIS, and deep learning, with significant contributions to digital heritage preservation and archaeological landscape documentation. His interdisciplinary research integrates advanced geospatial technologies, including UAV photogrammetry, terrestrial 3D laser scanning, and machine learning models, to enhance the detection, classification, and conservation of archaeological and cultural heritage sites. A UK Global Talent Visa holder under the Exceptional Talent Route, Dr. Bachagha’s work bridges technology and heritage, focusing on data-driven approaches to protect endangered sites and reconstruct ancient civilizations through digital innovation. His expertise spans ENVI, ArcGIS, QGIS, and Earth Engine applications, combined with proficiency in Python, R, MATLAB, and JavaScript for geospatial analytics and automated system development. With over 430 citations from 374 documents in Scopus (h-index: 6) and 675 citations in Google Scholar (h-index: 8, i10-index: 7), Dr. Bachagha’s research demonstrates strong academic influence and global recognition. His projects, such as the “One Belt, One Road Heritage Protection” and “Endangered Wooden Architecture Programme,” exemplify his commitment to integrating AI, remote sensing, and geospatial intelligence in cultural heritage management.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Bachagha, N., Wang, X., Lasaponara, R., Luo, L., & Khatteli, H. (2020). Remote sensing and GIS techniques for reconstructing the military fort system of Roman boundary (Tunisia section) and identifying archaeological sites. Remote Sensing of Environment.

Bachagha, N., Luo, L., Wang, X., Masini, N., Tababi, M., Khatteli, H., & Lasaponara, R. (2020). Mapping the Roman water supply system of the Wadi el Melah Valley in Gafsa, Tunisia, using remote sensing. Sustainability.

Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., & Bachagha, N. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment.

Bachagha, N., Xu, W., Luo, X., Brahmi, M., Wang, X., Souei, F., & Lasaponara, R. (2022). On the discovery of a Roman fortified site in Gafsa, southern Tunisia, based on high-resolution X-band satellite radar data. Remote Sensing.

Bachagha, N., Tababi, M., Selim, G., Shao, W., Xue, Y., Li, W., Bennour, A., Luo, L., Lasaponara, R., & Lao, Y. (2025). Facilitating archaeological discoveries through deep learning and space-based observations: A case study in southern Tunisia. Nature Communications.

Yexin Wang | Planetary remote sensing | Best Researcher Award

Assoc. Prof. Dr. Yexin Wang | Planetary remote sensing | Best Researcher Award

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, China
📚 Dr. Yexin Wang is an Associate Professor at the State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS). With a Ph.D. in Measuring and Testing Technologies and Instruments from Beihang University, she specializes in planetary remote sensing, computer vision, artificial intelligence, and image processing. Dr. Wang has significantly contributed to China’s planetary exploration missions, including Chang’e-4, Chang’e-5, Chang’e-6, and Tianwen-1, through her expertise in environment perception, navigation, and localization.

Publication Profile

Education

🎓 Dr. Yexin Wang earned her Ph.D. in Measuring and Testing Technologies and Instruments from Beihang University, Beijing, in 2014. Her strong academic foundation underpins her advanced research in planetary exploration and image processing.

Experience

🌌 As a key researcher at AIRCAS, Dr. Wang has been actively involved in groundbreaking planetary exploration missions. Her projects span planetary remote sensing, pattern recognition, and environment perception for rovers used in China’s Chang’e and Tianwen-1 missions. She has also collaborated with international institutions like the National Institute for Nuclear Physics (INFN-LNF), contributing to global space exploration advancements.

Awards and Honors

🏆 Dr. Yexin Wang’s outstanding contributions have been widely recognized, with notable achievements in scientific research and innovation. Her leadership in high-profile projects and patents reflects her impactful role in planetary science and technology.

Research Focus

🔍 Dr. Wang’s research revolves around planetary remote sensing, computer vision, artificial intelligence, and image processing. She is particularly focused on environment perception, feature extraction, navigation, and localization for deep-space exploration rovers. Her work ensures precise mapping and analysis of extraterrestrial surfaces.

Conclusion

✨ Dr. Yexin Wang is a distinguished scientist and a driving force behind advancements in planetary exploration. Her dedication to innovation and collaboration has cemented her position as a leading expert in remote sensing and space technology.

Publications

YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area (2025) – Sensors. Cited by [10 articles]. DOI: 10.3390/s25010243

Geological context of the Chang’e-6 landing area and implications for sample analysis (2024) – The Innovation. Cited by [15 articles]. DOI: 10.1016/j.xinn.2024.100663

High-Precision Visual Localization of the Chang’e-6 Lander (2024) – National Remote Sensing Bulletin. Cited by [8 articles]. DOI: 10.11834/jrs.20244229

A Catalogue of Impact Craters and Surface Age Analysis in the Chang’e-6 Landing Area (2024) – Remote Sensing. Cited by [20 articles]. DOI: 10.3390/rs16112014

Topographic Mapping Capability Analysis of Moderate Resolution Imaging Camera (MoRIC) Imagery of Tianwen-1 Mars Mission (2023) – Journal of Remote Sensing. Cited by [12 articles]. DOI: 10.34133/remotesensing.0040

A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM (2022) – Remote Sensing. Cited by [18 articles]. DOI: 10.3390/rs14215420

Updated lunar cratering chronology model with the radiometric age of Chang’e-5 samples (2022) – Nature Astronomy. Cited by [45 articles]. DOI: 10.1038/s41550-022-01604-3

Progresses and prospects of environment perception and navigation for deep space exploration rovers (2021) – Acta Geodaetica et Cartographica Sinica. Cited by [22 articles]. DOI: 10.11947/j.AGCS.2021.20210290

Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images (2021) – Remote Sensing. Cited by [30 articles]. DOI: 10.3390/rs13173439

Enhanced Lunar Topographic Mapping Using Multiple Stereo Images Taken by Yutu-2 Rover (2021) – Photogrammetric Engineering and Remote Sensing. Cited by [25 articles]. DOI: 10.14358/PERS.87.8.567