Dr. Adlin Dancheva | Remote Sensing | Best Researcher Award
Dr. Adlin Dancheva | Remote Sensing | Space Research and Technology Institute-BAS | Bulgaria
Dr. Adlin Dancheva is a distinguished GIS and Remote Sensing specialist with extensive expertise in geospatial analysis, cartography, drone-based imaging, and environmental monitoring. She is currently pursuing her Ph.D. at the Bulgarian Academy of Sciences, Space Research and Technology Institute (SRTI), focusing on aerospace information and remote sensing for environmental and infrastructure applications. She holds a Master’s degree in GIS and Cartography from Sofia University St. Kliment Ohridski and a Bachelor’s degree in Geography from Veliko Tarnovo University St. Cyril and St. Methodius. Dr. Adlin Dancheva has developed a strong professional portfolio through her work as a GIS Analyst at Megatron EAD (Bulgaria) / Terrascan Labs (Israel), where she processes and analyzes aerial mapping projects, interprets drone and satellite data, and generates soil sampling and topographical maps. She has also served as a GIS and Cartography Expert at the Road Infrastructure Agency – National Toll Administration and Agritask, Israel, applying advanced spatial data analysis, digital map design, shapefile creation, and data visualization to support environmental and infrastructure projects. Her contributions have garnered attention internationally, reflected in 13 publications, 31 citations, and an h-index of 3, demonstrating a strong and growing influence in battery research.
Selected Publications
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Dancheva, A., & colleagues. (2025). Citric acid as electrolyte additive in aqueous magnesium-air battery used in Antarctic climate. Electrochimica Acta. (8 citations)



















Satellite imagery for rapid detection of liquefaction surface manifestations: Türkiye–Syria 2023 Earthquakes – Remote Sensing, 2023, Cited by: 32
Automated 3D jointed rock mass structural analysis using LiDAR for rockfall susceptibility – Geotechnical and Geological Engineering, 2020, Cited by: 29
Evaluation of machine learning algorithms for object-based mapping of landslide zones using UAV data – Geosciences, 2021, Cited by: 26
3D hazard analysis and object-based characterization of landslide motion using UAV imagery – International Archives of Photogrammetry and Remote Sensing, 2019, Cited by: 20