Ruiqi Ma
Papers
1
Total Citations
32
H-Index
1
About
Ruiqi Ma is a leading researcher in remote sensing and power infrastructure monitoring, with a focus on integrating deep learning and LiDAR technologies for real-time utility inspection. Their most cited work, "DCPLD-Net: A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data" (2022, 32 citations), introduces a novel neural network architecture that couples diffusion processes with convolutional layers to enable rapid, accurate extraction of power lines from aerial LiDAR point clouds. This contribution directly addresses the critical need for automated, reliable inspection of electrical grids, which underpins social and industrial stability. By leveraging UAV-based remote sensing, Ma’s research advances the practical deployment of AI in energy infrastructure monitoring, offering a scalable solution for real-time detection that reduces human risk and operational costs. Their work bridges computer vision, geospatial analysis, and civil engineering, demonstrating how deep learning can transform traditional survey methods. With growing citation impact, Ma’s innovations are shaping the future of smart grid maintenance and autonomous drone-based inspection systems.
Research Focus
Key Achievements
Top Papers
- 1