Evgenii Karasev

About

Evgenii Karasev is a researcher whose work sits at the intersection of computer vision, robotics, and geospatial analysis, with a particular focus on automated terrain mapping and path planning for autonomous vehicles. His most-cited paper, "Automated terrain mapping based on mask R-CNN neural network" (2020, 5 citations), introduces a novel approach that combines deep learning with classical computer vision techniques. Specifically, Karasev leverages ORB descriptors to stitch aerial images from unmanned aerial vehicles into orthomosaics, then applies Mask R-CNN for semantic segmentation of terrain features. This work directly addresses the challenge of enabling robotic vehicles to navigate complex, unstructured environments by generating high-resolution, labeled maps in real time. While his citation count is modest, the practical significance of his methodology—bridging neural network-based object detection with traditional image stitching—marks a meaningful contribution to autonomous navigation and remote sensing. Karasev’s research is particularly valuable for students and engineers working at the frontier of field robotics, where robust, automated mapping remains a critical bottleneck.

Research Focus

Key Achievements

Top Papers

1. 1
   Automated terrain mapping based on mask R-CNN neural network

   5 citations · 2020

Key Collaborators

Contact & Links