Peter Krzystek

Professor

Munich University of Applied Sciences

Germany

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About

Peter Krzystek is a Professor at Munich University of Applied Sciences in Germany. His research areas include remote sensing, tree crown delineation, and biodiversity variables, with recent publications focusing on the use of aerial multispectral imagery and lidar data for forestry applications. He is known for his contributions to deep learning techniques for tree species classification and the analysis of forest ecosystems.

Articles (11)

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2024

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2024

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2023

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2022

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2022

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2021

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2021

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2021

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2020

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2020

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2020

Collaborators (7)

Andreas Schmitt

Professor of Applied Geodesy

Munich University of Applied Sciences

GERMANY

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Lucie Homolová

Global Change Research Institute CAS

CZECH REPUBLIC

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Alfred Schöttl

Professor

Munich University of Applied Sciences

GERMANY

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Melanie Brandmeier

Professor

Technical University of Applied Sciences Würzburg-Schweinfurt

GERMANY

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Sebastian Briechle

University of Twente

NETHERLANDS

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Marco Heurich

Professor

Inland Norway University of Applied Sciences

NORWAY

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Thomas Nauss

philipps-universität marburg chemistry department 35032 marburg germany

GERMANY

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Peter Krzystek

Research Interests

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Articles (11)

Semi-supervised multi-class tree crown delineation using aerial multispectral imagery and lidar data

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

Towards complete tree crown delineation by instance segmentation with mask R–CNN and DETR using UAV-based multispectral imagery and lidar data

Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net

NOVEL SINGLE TREE DETECTION BY TRANSFORMERS USING UAV-BASED MULTISPECTRAL IMAGERY

A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’

Combining graph-cut clustering with object-based stem detection for tree segmentation in highly dense airborne lidar point clouds

Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data

CLASSIFICATION OF TREE SPECIES AND STANDING DEAD TREES BY FUSING UAV-BASED LIDAR DATA AND MULTISPECTRAL IMAGERY IN THE 3D DEEP NEURAL NETWORK POINTNET++

Heterogeneity–diversity relationships differ between and within trophic levels in temperate forests

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Collaborators (7)

Andreas Schmitt

Lucie Homolová

Alfred Schöttl

Melanie Brandmeier

Sebastian Briechle

Marco Heurich

Thomas Nauss

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