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.

Profile

Scopus | ORCID | Google Scholar

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.

Shumin Wang | Remote Sensing | Best Researcher Award

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Dr. Shumin Wang | Remote Sensing | Best Researcher Award

Dr. Shumin Wang , lecturer , Hubei University of Education , China.

Shumin Wang is a dedicated academic and researcher in the field of remote sensing and environmental change, with a robust background in computer science and quantitative image analysis. Currently serving as a Lecturer at Hubei University of Education, he combines technical expertise with a deep interest in land surface temperature (LST) downscaling techniques. His academic journey spans prestigious Chinese institutions, and his research has led to impactful publications in internationally recognized journals. Shumin’s work focuses on developing and refining spatial modeling methods to improve satellite-based environmental monitoring, contributing significantly to the geoscience and remote sensing community through innovation and scientific rigor.

Publication Profile

Scopus

ORCID

🎓 Education Background

Shumin Wang completed his Ph.D. in Global Environmental Change (Quantitative Remote Sensing) at Beijing Normal University (2020–2023), where he focused on advanced satellite data analysis techniques. Prior to this, he earned an M.Sc. in Computer Science and Technology with a specialization in Remote Sensing Image Processing from Chongqing University of Posts and Telecommunications (2017–2020). His academic journey began with a B.Sc. in Computer Science and Technology from Jining Medical University (2013–2017). His interdisciplinary training has equipped him with a strong foundation in computational techniques, algorithm development, and environmental modeling, shaping him into a promising scholar in the remote sensing domain.

🏢 Professional Experience

Shumin Wang began his professional academic career in December 2023 as a Lecturer at Hubei University of Education. In this role, he engages in both teaching and research, mentoring undergraduate and postgraduate students in the field of quantitative remote sensing and environmental informatics. His teaching emphasizes real-world applications of spatial downscaling and data fusion in climate and ecological systems. As an early-career faculty member, he is also actively expanding his research collaborations and participating in national and international academic activities. His experience reflects a growing contribution to academia, particularly in leveraging satellite imagery for solving global environmental problems.

🏅 Awards and Honors

Though specific awards and honors for Shumin Wang have not been detailed, his inclusion in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing and Remote Sensing signals high recognition within the scientific community. His research contributions—especially in land surface temperature downscaling—have been widely cited, suggesting strong academic impact and recognition from peers. As a young researcher, his potential for future accolades in remote sensing and environmental modeling is high, particularly as he continues to contribute to data-driven solutions for global environmental challenges. Continued excellence in research and publication positions him for future scientific honors.

🔬 Research Focus

Shumin Wang’s research is primarily focused on remote sensing image processing and spatial downscaling of land surface temperature (LST). He has extensively explored geographically weighted regression models, including Taylor expansion and autoregressive techniques, to enhance the spatial resolution of satellite-derived LST data. His work addresses critical challenges in urban climate monitoring, environmental modeling, and sustainable land management by enabling more precise thermal observations. Wang’s innovative methodologies aim to bridge the gap between low-resolution satellite data and high-resolution environmental needs, contributing to smarter urban planning, ecological protection, and climate change research. His approach is methodologically robust and environmentally relevant.

📌 Conclusion

In summary, Shumin Wang is a promising early-career researcher and educator in the field of quantitative remote sensing and computer-based environmental modeling. His educational background and research output position him as a strong candidate for future leadership in the geoscience and remote sensing community. With a focus on developing advanced algorithms for LST downscaling, he is committed to enhancing the accuracy and utility of satellite observations. His academic trajectory, from undergraduate to Ph.D. and into lecturing, illustrates a steady commitment to scientific advancement and education. Wang’s continued contributions will likely influence future technologies in global environmental monitoring.

📄 Publication Top Notes

  1. A Taylor expansion algorithm for spatial downscaling of MODIS land surface temperature
    IEEE Transactions on Geoscience and Remote Sensing, 2022
    Cited by: 51 articles (as of latest data)

  2. Downscaling land surface temperature based on non-linear geographically weighted regressive model over urban areas
    Remote Sensing, 2021
    Cited by: 63 articles

  3. Spatial downscaling of MODIS land surface temperature based on geographically weighted autoregressive model
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Cited by: 70 articles

  4. Research on land surface temperature downscaling algorithm based on local nonlinear geographically weighted regression model
    Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020
    Cited by: 12 articles

 

Dr. Fang Li | Remote sensing | Best Researcher Award

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Dr. Fang Li | Remote sensing | Best Researcher Award

lecturer, Dalian Minzu University, China

Fang Li 🎓 is a dedicated lecturer at Dalian Minzu University, China, specializing in computer science and technology. She earned her Ph.D. in 2023 from Dalian Maritime University, focusing on signal and remote sensing image processing. With a strong passion for innovation and academic excellence, she has developed a reputation for her cutting-edge work in hyperspectral image processing, anomaly detection, and real-time target detection. As an active IEEE member, Fang Li contributes significantly to the global scientific community through her impactful research and publications in top-tier journals.

Publication Profile

ORCID

🎓Education Background

Fang Li received her Ph.D. in Computer Science and Technology in 2023 from Dalian Maritime University, China 🏫. Her academic foundation is rooted in advanced image processing and hyperspectral remote sensing technologies, setting the stage for her impressive research contributions.

💼Professional Experience

Currently serving as a lecturer at Dalian Minzu University 👩‍🏫, Fang Li has been actively engaged in teaching and research activities. Her experience spans several years of dedicated work in signal processing and remote sensing, with a strong emphasis on hyperspectral imaging applications. She also played a leading role in the Excellent Doctoral Dissertation Cultivation Program at her university, showcasing her leadership in mentoring and academic development.

🏅Awards and Honors

Fang Li has received institutional recognition for her academic excellence, including being a lead figure in the Excellent Doctoral Dissertation Cultivation Program 🏆 at Dalian Maritime University. While formal international awards are pending, her scholarly work in top IEEE journals reflects her growing global impact in the research field.

🔬Research Focus

Fang Li’s research focuses on signal and remote sensing image processing, particularly hyperspectral image analysis 🌌. Her interests include anomaly detection, target detection, band fusion, and real-time data processing. With over 15 journal publications and 6 patents under process, her work contributes significantly to the advancement of remote sensing and machine learning technologies.

🧩Conclusion

Fang Li exemplifies dedication, innovation, and scholarly excellence 📚. As a rising academic in hyperspectral remote sensing, she has consistently demonstrated the potential to lead and influence cutting-edge research. Her commitment to scientific development, paired with her IEEE membership and impactful publications, positions her as a deserving candidate for the Best Researcher Award.

📘Top Publications 

Abundance Estimation Based on Band Fusion and Prioritization Mechanism
Journal: IEEE Transactions on Geoscience and Remote Sensing (2022)
Cited by: 32 articles (as per Google Scholar)

Bi-Endmember Semi-NMF Based on Low-Rank and Sparse Matrix Decomposition
Journal: IEEE Transactions on Geoscience and Remote Sensing (2022)
Cited by: 27 articles

Progressive Band Subset Fusion for Hyperspectral Anomaly Detection
Journal: IEEE Transactions on Geoscience and Remote Sensing (2022)Cited by: 25 articles

Sequential Band Fusion for Hyperspectral Anomaly Detection
Journal: IEEE Transactions on Geoscience and Remote Sensing (2022)
Cited by: 44 articles

Sequential Band Fusion for Hyperspectral Target Detection
Journal: IEEE Transactions on Geoscience and Remote Sensing (2022)
Cited by: 36 articles

Assoc. Prof. Dr. Qiansheng Zhao | Geographical information | Excellence in Research Award

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Assoc. Prof. Dr. Qiansheng Zhao | Geographical information | Excellence in Research Award

Associate Professor, Wuhan University, China

Dr. Qiansheng Zhao is a dedicated Associate Professor at the School of Geodesy and Geomatics, Wuhan University . With a solid foundation in both Computer Science and Surveying Engineering, he has contributed significantly to the fields of Geographical Information Science and 3D GIS. Dr. Zhao has played a key role in advancing digital twin technologies, marine disaster scenario modeling, and intelligent geospatial systems. He brings international research exposure, having served as a visiting scholar at the University of Tennessee, Knoxville 🇺🇸, and continues to influence the field through innovative research, publications, and technological development.

Publication Profile

🎓 Education Background:

Dr. Zhao earned dual Bachelor’s degrees in Computer Science and Surveying Engineering from Wuhan University in 2004 🎓. He completed his Ph.D. at the same institution in 2009, specializing in geospatial technologies. His multidisciplinary academic background uniquely positions him at the intersection of geoinformatics, artificial intelligence, and spatial data processing.

💼 Professional Experience:

Since 2010, Dr. Zhao has served as an Associate Professor at Wuhan University, where he leads research in 3D GIS, GeoAI, and web-based spatial systems. He was a visiting scholar at the University of Tennessee, Knoxville during 2013–2014, enriching his global perspective and collaborative engagements. His work includes national-level projects supported by the Ministry of Science and Technology of China, as well as collaborations with the Marine Security Technical Committee of the China Society of Public Security Science and Technology 🌏.

🏆 Awards and Honors:

Dr. Zhao has made impactful contributions recognized through his involvement in national R&D programs and professional committees. While specific award titles are not mentioned, his leadership roles, successful project acquisition, and consistent research output highlight his excellence in both innovation and academic research 🏅. His authored book on marine disaster scenario deduction further emphasizes his applied expertise.

🔬 Research Focus:

Dr. Zhao focuses on 3D GIS, GeoAI, and digital twin technologies. His key contributions include developing systems for dynamic web-based management and visualization of large-scale 3D models, and creating collaborative geospatial platforms using Conflict-free Replicated Data Types (CRDTs) 🌐. His recent research involves smart highway digital twins and AI-driven marine security simulations, reflecting his commitment to solving real-world problems through cutting-edge geospatial science 🤖🌊.

🧩 Conclusion:

Dr. Qiansheng Zhao is a forward-thinking geospatial scientist whose work bridges advanced computing with practical applications in geographic information systems. With a strong academic foundation, international exposure, and a prolific publication record, he continues to contribute to the global advancement of GIS technologies, smart environments, and marine spatial intelligence. His work stands as a testament to innovation and excellence in geospatial research 🚀.

📚 Top Publications :

A cloud-based framework for collaborative 3D GIS using CRDTs. ISPRS Journal of Photogrammetry and Remote Sensing. 
Cited by: 58 articles

Semantic mapping integration for smart marine disaster management. Computers, Environment and Urban Systems. 
Cited by: 44 articles

Efficient web-based visualization of massive 3D city models using edge computing. Sensors. 
Cited by: 63 articles

Digital twin-driven real-time GIS for intelligent transportation systems. International Journal of Digital Earth. 
Cited by: 36 articles

Prof. Theodore Tsiligiridis | Remote Sensing | Best Researcher Award

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Prof. Theodore Tsiligiridis | Remote Sensing | Best Researcher Award

Professor, Agricultural University of Athens, Greece

Professor Theodore A. Tsiligiridis is a distinguished academic and researcher in telecommunications, computer science, and agricultural informatics. With a strong background in mathematics, probability, and statistics, he has contributed extensively to mobile cellular systems, performance evaluation of networks, and the integration of digital technologies in rural development. His work in European projects like RACE, DELTA, ORA, and GoDigital has significantly influenced ICT applications in agriculture and food security. His contributions to sensor network technologies and intelligent systems for pest management further highlight his interdisciplinary expertise.

Publication Profile

🎓 Education

Professor Tsiligiridis holds a B.Sc. in Mathematics from the University of Athens, Greece. He later pursued an M.Sc. in Probability and Statistics at Manchester University, UK, followed by a Ph.D. in Telecommunications from the Department of Electronic and Electrical Engineering at the University of Strathclyde, Scotland, UK. His educational background provided a strong foundation for his pioneering research in digital communications and agricultural data analytics.

💼 Experience

Following his academic journey, Professor Tsiligiridis joined the Computer Science, Mathematics, and Statistical Division at the Agricultural University of Athens (AUA). Throughout his career, he has taken on various academic and public sector roles, coordinating multiple European research projects. His involvement in projects such as RACE I/II (Advanced Telecommunications), DELTA (Distance Learning), and EUROFARM (Farm Structure Survey) reflects his commitment to bridging ICT and agriculture. Additionally, his leadership in the GoDigital/EU project facilitated internet services and e-commerce practices in thousands of SMEs in rural Greece.

🏆 Awards and Honors

Professor Tsiligiridis’ research contributions have been widely recognized in academia and industry. He has played a pivotal role in multiple EU-funded initiatives, earning commendations for his efforts in advancing telecommunications, rural ICT integration, and agricultural informatics. His pioneering work in wireless sensor networks, artificial intelligence in pest control, and food security has been cited extensively, showcasing his impact in these fields.

🔬 Research Focus

His research spans mobile telecommunications, sensor networks, smart agriculture, and artificial intelligence applications in environmental monitoring. He has extensively worked on electronic trapping systems for pest management, the integration of statistical and geospatial data in small farming systems, and the development of AI-driven solutions for food security. His interdisciplinary approach has led to practical solutions that enhance agricultural sustainability and efficiency.

📝 Conclusion

Professor Theodore A. Tsiligiridis is a visionary academic whose contributions have significantly shaped the intersection of ICT, telecommunications, and agricultural data science. His extensive research, leadership in EU-funded projects, and innovative applications in environmental informatics make him a key figure in advancing digital transformations in rural and agricultural sectors. His impactful work continues to inspire future generations in computer science, engineering, and agritech innovation.

📚 Publications

Supporting the Role of Small Farms in the European Regional Food Systems: What Role for the Science-Policy Interface? (2021) – Global Food Security
🔗 Read Here | Cited by 12

Typology and Distribution of Small Farms in Europe: Towards a Better Picture (2018) – Land Use Policy
🔗 Read Here | Cited by 123

Electronic Traps for Detection and Population Monitoring of Adult Fruit Flies (Diptera: Tephritidae) (2018) – Journal of Applied Entomology
🔗 Read Here | Cited by 71

A Sentiment Lexicon-Based Analysis for Food and Beverage Industry Reviews: The Greek Language Paradigm (2020) – International Journal on Natural Language Computing
🔗 Read Here | Cited by 14

A Location-Aware System for Integrated Management of Rhynchophorus Ferrugineus in Urban Systems (2015) – Computers, Environment and Urban Systems
🔗 Read Here | Cited by 50

Pest Management Control of Olive Fruit Fly (Bactrocera Oleae) Based on a Location-Aware Agro-Environmental System (2012) – Computers and Electronics in Agriculture
🔗 Read Here | Cited by 53

Location-Aware System for Olive Fruit Fly Spray Control (2010) – Computers and Electronics in Agriculture
🔗 Read Here | Cited by 43

Plant Virus Identification Based on Neural Networks with Evolutionary Preprocessing (2010) – Computers and Electronics in Agriculture
🔗 Read Here | Cited by 37

A Memetic Algorithm for Optimal Dynamic Design of Wireless Sensor Networks (2010) – Computer Communications
🔗 Read Here | Cited by 30

Feature Extraction for Time-Series Data: An Artificial Neural Network Evolutionary Training Model for the Management of Mountainous Watersheds (2010) – Computers and Electronics in Agriculture
🔗 Read Here