Mrs. Andsera Adugna Mekonen | Remote Sensing | Young Scientist Award

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Mrs. Andsera Adugna Mekonen | Remote Sensing | Young Scientist Award

University of Naples | Italy

Andsera Adugna Mekonen is an emerging Earth and Environmental Scientist specializing in remote sensing, geoinformatics, and precision agroforestry systems. His research focuses on leveraging drone and satellite imagery for above-ground biomass estimation and sustainable agroforestry ecosystem monitoring. He integrates advanced remote sensing technologies, GIS applications, photogrammetry, and machine learning to improve environmental assessment and agricultural productivity. His expertise extends to UAS-based data acquisition, multispectral and RGB imagery analysis, and the application of artificial intelligence and data science in Earth observation. He has presented his work at leading international conferences, including IEEE MetroAerospace, and contributed to advancements in sustainable land management and ecosystem monitoring. His innovative approach combines Earth observation with AI-driven analytical frameworks to enhance accuracy in biomass modeling and environmental risk assessment. He has authored impactful research in peer-reviewed journals, with a Scopus record of 2 documents and an h-index of 1, and a Google Scholar profile reflecting 59 citations, an h-index of 2, and an i10-index of 1. His contributions demonstrate a growing influence in geospatial and agro-environmental research, emphasizing interdisciplinary integration of technology and sustainability science.

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Featured Publications

Mekonen, A. A., Raghuvanshi, T. K., Suryabhagavan, K. V., & Kassawmar, T. (2022). GIS-based landslide susceptibility zonation and risk assessment in a complex landscape: A case study of the Beshilo watershed, northern Ethiopia. Environmental Challenges, 8, 100586.

Mekonen, A. A., Accardo, D., & Renga, A. (2024). Above-ground biomass estimation in an agroforestry environment by UAS and RGB imagery. In IEEE International Workshop on Metrology for Aerospace, 272–277.

Mekonen, A. A., Accardo, D., & Renga, A. (2025). Above-Ground Biomass Prediction in Agroforestry Areas Using Machine Learning and Multispectral Drone Imagery. In IEEE International Workshop on Metrology for Aerospace, 63–68.

Mekonen, A. A., Accardo, D., & Claudia, C. (2025). An effective process to use drones for above-ground biomass estimation in agroforestry landscapes. Aerospace, 12(11), 26.

Sisay, S. B., Melkamu, M. B., Birhan, B. A., & Mekonen, A. A. (2019). Inoculation and phosphorus fertilizer improve food-feed traits of grain legumes in mixed crop-livestock systems of Ethiopia.

Dr. Nabil Bachagha | Remote Sensing | Best Researcher Award

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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.