Lukas Winiwarter

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Professor of Geodesy

University of Innsbruck

Austria

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Lukas Winiwarter is a Professor of Geodesy at Universität Innsbruck, Austria. His research areas encompass 3D reconstruction techniques, particularly in the context of underwater scenes and photogrammetric applications. He has contributed to advancements in tree species classification and low-cost 3D mapping solutions using innovative methodologies such as deep learning and laser scanning data analysis.

Recent Grants

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UncertainTree

Open Date: 2022-06-01

Close Date: 2023-10-31

Articles (17)

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2025

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Collaborators (10)

José Carlos Cabaleiro

Professor

Universidade de Santiago de Compostela

SPAIN

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Samuli Junttila

Associate Professor

University of Helsinki

FINLAND

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Ninni Saarinen

University of Helsinki

FINLAND

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Jana Mullerova

Ass. professor

Jan Evangelista Purkyně University in Ústí nad Labem

CZECH REPUBLIC

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Martin Rutzinger

University of Innsbruck

AUSTRIA

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Emily Lines

Associate Professor

University of Cambridge

UNITED KINGDOM

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Fabian Fassnacht

Professor

Freie Universität Berlin

GERMANY

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Katharina Anders

Assistant Professor

Technical University of Munich

GERMANY

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Daniel Czerwonka-Schröder

Professor for Applied Geodesy

Bochum University of Applied Sciences

GERMANY

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Zoe Schindler

University of Freiburg

GERMANY

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Lukas Winiwarter

Research Interests

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About

Recent Grants

Articles (17)

Analysis of refraction aware Neural Radiance Fields for 3D reconstruction of through the water scenes

Exploring the Potential of Refractive NeRFs for Photogrammetric Bathymetry - First Application to UAV-based Data from the Pielach River

Simulation and validation of underwater scenes for two-media optical 3D reconstruction

Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR‐species20K dataset

A systematic literature review of low-cost 3D mapping solutions

Deep learning with simulated laser scanning data for 3D point cloud classification

Evaluating Tree Species Mapping: Probability Sampling Validation of Pure and Mixed Species Classes Using Convolutional Neural Networks and Sentinel-2 Time Series

Extraction of Forest Road Information from CubeSat Imagery Using Convolutional Neural Networks

Estimating tree species composition from airborne laser scanning data using point-based deep learning models

Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models

CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography

Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering

Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

EVALUATION OF UAV-BORNE PHOTOGRAMMETRY AND LASER SCANNING FOR 3D TOPOGRAPHIC CHANGE ANALYSIS AT AN ACTIVE ROCK GLACIER

INTEGRATION OF KALMAN FILTERING OF NEAR-CONTINUOUS SURFACE CHANGE TIME SERIES INTO THE EXTRACTION OF 4D OBJECTS-BY-CHANGE

VIRTUAL LASER SCANNING OF DYNAMIC SCENES CREATED FROM REAL 4D TOPOGRAPHIC POINT CLOUD DATA

Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests

Collaborators (10)

José Carlos Cabaleiro

Samuli Junttila

Ninni Saarinen

Jana Mullerova

Martin Rutzinger

Emily Lines

Fabian Fassnacht

Katharina Anders

Daniel Czerwonka-Schröder

Zoe Schindler

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