Can Haktan Karadal
Papers
1
Total Citations
60
H-Index
1
About
Can Haktan Karadal is a researcher whose work lies at the intersection of deep learning and remote sensing, with a particular focus on automated image classification. His most cited paper, "Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques" (2021), has garnered 60 citations, showcasing his contribution to efficient, lightweight neural network architectures for analyzing satellite and aerial imagery. By integrating MobileNetV2 with discrete wavelet transform (DWT), Karadal advanced the field’s ability to process high-resolution data with reduced computational cost—a critical step for real-time environmental monitoring and urban planning. His research demonstrates a talent for bridging practical engineering constraints with cutting-edge AI, making his methods accessible for resource-limited applications. Karadal’s work is particularly notable for its emphasis on multiscale feature extraction, which improves classification accuracy across diverse terrains and land-use categories. As a researcher, he continues to explore how optimized deep learning models can transform geospatial analysis, offering scalable solutions for agriculture, disaster response, and climate change studies. His growing citation record reflects a steady influence on both computer vision and remote sensing communities.
Research Focus
Key Achievements
Top Papers
- 1