Martin Štroner

Head of the Department

České Vysoké Učení Technické v Praze Fakulta Jaderná a Fyzikálně inženýrská

Czech Republic

Research Interests

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Martin Štroner is the Head of the Department at České vysoké učení technické v Praze, Czech Republic. His research areas include advanced methodologies in geospatial technology, particularly focusing on accuracy assessments of various scanning techniques, deep learning applications in point cloud classification, and automated optimization algorithms for geodetic surveying. Recent publications include studies on SLAM versus static scanners, terrain filtering using neural networks, and photogrammetric monitoring of geological events.

Articles (13)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Collaborators (3)

Tomas Suk

Czech Technical University in Prague

CZECH REPUBLIC

View details

Jaroslav Braun

Czech Technical University in Prague

CZECH REPUBLIC

View details

Peter Blišťan

SLOVAKIA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Martin Štroner

Research Interests

Contact this professor

About

Articles (13)

A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners

Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network

Scanning the underground: Comparison of the accuracies of SLAM and static laser scanners in a mine tunnel

A New Automated Algorithm for Optimization of Measurements for Achieving the Required Accuracy of a Geodetic Network

Determination of Accuracy and Usability of a SLAM Scanner GeoSLAM Zeb Horizon: A Bridge Structure Case Study

UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1)

Methodology and Results of Staged UAS Photogrammetric Rockslide Monitoring in the Alpine Terrain in High Tatras, Slovakia, after the Hydrological Event in 2022

Color-Based Point Cloud Classification Using a Novel Gaussian Mixed Modeling-Based Approach versus a Deep Neural Network

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

Progressive Dilution of Point Clouds Considering the Local Relief for Creation and Storage of Digital Twins of Cultural Heritage

Multidirectional Shift Rasterization (MDSR) Algorithm for Effective Identification of Ground in Dense Point Clouds

A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Collaborators (3)

Tomas Suk

Jaroslav Braun

Peter Blišťan

Make your next move.