6 papers
Distant Object Localisation from Noisy Image Segmentation Sequences
Julius Pesonen, Arno Solin, Eija Honkavaara
3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of obje…
Field evaluation and optimization of a lightweight autonomous lidar-based UAV system based on a rigorous experimental setup in boreal forest environments
Aleksi Karhunen, Teemu Hakala, Väinö Karjalainen +1
Interest in utilizing autonomous uncrewed aerial vehicles (UAVs) for under-canopy forest remote sensing has increased in recent years, resulting in the publication of numerous auto…
Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels
Julius Pesonen, Stefan Rua, Josef Taher +3
Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understand and care for our environment…
Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
Väinö Karjalainen, Niko Koivumäki, Teemu Hakala +5
Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations…
Deep Learning-based Robust Autonomous Navigation of Aerial Robots in Dense Forests
Guglielmo Del Col, Väinö Karjalainen, Teemu Hakala +2
Autonomous aerial navigation in dense natural environments remains challenging due to limited visibility, thin and irregular obstacles, GNSS-denied operation, and frequent perceptu…
Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models
Julius Pesonen, Teemu Hakala, Väinö Karjalainen +6
Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can co…