Antero Kukko

Research Professor

Finnish Geospatial Research Institute

Finland

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Antero Kukko is a Research Professor at the Finnish Geospatial Research Institute in Finland. His research focuses on advanced methodologies in remote sensing and geospatial analysis, particularly utilizing multispectral LiDAR and mobile laser scanning systems for forest mapping and real-time localization. He has contributed to the development of datasets for semantic learning in forestry and innovative approaches for tracking and measuring tree characteristics under forest canopies. His recent publications highlight significant advancements in the integration of various sensing technologies for environmental monitoring.

Antero Kukko

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Articles (27)

Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds

Comparative Analysis of Ultra-Wideband and Mobile Laser Scanning Systems for Mapping Forest Trees under A Forest Canopy

A generic multi-lidar data batching strategy on the sensor driver level

Glacial ring forms on Axel Heiberg Island, Nunavut, Canada

Direct integration of ALS and MLS for real-time localization and mapping

Tracking foresters and mapping tree stem locations with decimeter-level accuracy under forest canopies using UWB

Assessing the consistency of low vegetation characteristics estimated using harvester, handheld, and drone light detection and ranging (LiDAR) systems

Ultra-Wideband-Based Method for Measuring Tree Positions With Decimeter-Level Accuracy Under a Forest Canopy

Integration of a Mobile Laser Scanning System with a Forest Harvester for Accurate Localization and Tree Stem Measurements

Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions

Overview and Benchmark on Multi-Modal Lidar Point Cloud Registration for Forest Applications

Performance Comparison of UWB IEEE 802.15.4z and IEEE 802.15.4 in Ranging, Energy Efficiency, and Positioning

Semantic segmentation of raw multispectral laser scanning data from urban environments with deep neural networks

ForestSemantic: a dataset for semantic learning of forest from close-range sensing

Data-Driven Antenna Delay Calibration for UWB Devices for Network Positioning

Performance Analysis of Standalone UWB Positioning Inside Forest Canopy

Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests

Individual tree segmentation and species classification using high-density close-range multispectral laser scanning data

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

REAL-TIME LIDAR-INERTIAL POSITIONING AND MAPPING FOR FORESTRY AUTOMATION

Unlocking Digitalization in Forest Operations with Viewshed Analysis to Improve GNSS Positioning Accuracy

Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys?—A Case Study Surveying Roadside City Trees

High Arctic channel incision modulated by climate change and the emergence of polygonal ground

POINT CLOUD REGISTRATION USING LASER DATA FROM AN ORANGE ORCHARD

Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data

Close-Range Remote Sensing of Forests

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Collaborators (12)

Eric Hyyppä

Shawn Chartrand

Shannon Hibbard

Tuomas A Yrttimaa

Mikko Vastaranta

Sander Oude Elberink

Samuli Junttila

Xiaowei Yu

Ninni Saarinen

George Vosselman

Ville Luoma

Harri Kaartinen

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