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About

Teemu Hakala is a leading researcher at the intersection of robotics, remote sensing, and forestry automation. His work focuses on developing autonomous systems for under-canopy forest mapping, integrating mobile laser scanning, hyperspectral LiDAR, and ultra-wideband (UWB) positioning to solve critical challenges in forest inventory and autonomous vehicle perception. Hakala’s most influential paper, “Under-canopy UAV laser scanning for accurate forest field measurements” (2020, 182 citations), pioneered the use of drones for precise, robotic-assisted data collection beneath forest canopies—a breakthrough that reduces reliance on labor-intensive manual surveys. He further advanced the field by integrating mobile laser scanning with forest harvesters for accurate tree stem measurements (2024), and demonstrated the feasibility of hyperspectral single-photon LiDAR for robust autonomous vehicle perception (2022). His recent work on autonomous robotic drone systems for mapping forest interiors (2024) and UWB positioning inside forest canopies (2024) continues to push the boundaries of forestry automation. With over 227 citations across his top papers, Hakala’s research is shaping the future of robotic forest monitoring and autonomous navigation in complex environments.