Zaide Duran
Papers
2
Total Citations
69
H-Index
2
About
Zaide Duran is a leading researcher in 3D point cloud analysis and its applications in photogrammetry, remote sensing, and robotics. Her work focuses on advancing machine learning and deep learning techniques for semantic segmentation and classification of complex 3D scenes. Duran’s most influential contribution, “Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features” (2021), has garnered 66 citations, establishing a foundational framework for extracting multiscale geometric features to improve point cloud classification accuracy. This work is widely recognized for bridging traditional geometric analysis with modern machine learning approaches. More recently, her 2024 study, “Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms,” explores the integration of advanced AI to enhance reliability in aerial targeting, with applications spanning defense, autonomous systems, and space exploration. Duran’s research directly addresses real-world challenges in autonomous navigation and environmental monitoring, demonstrating the critical role of robust 3D perception in next-generation AI systems. Her contributions continue to shape how machines interpret and interact with three-dimensional environments, making her a key figure in the evolution of intelligent spatial computing.
Research Focus
Key Achievements
Top Papers
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