Label errors in point cloud in training data for classification using machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959367" target="_blank" >RIV/49777513:23520/20:43959367 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.31490/9788024843988-21" target="_blank" >http://dx.doi.org/10.31490/9788024843988-21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31490/9788024843988-21" target="_blank" >10.31490/9788024843988-21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Label errors in point cloud in training data for classification using machine learning

Popis výsledku v původním jazyce

One of the applications of ULS (UAV-borne laser scanning) lies in power line inspection. However, with LiDAR data (i.e. point clouds) comes the need for reliable automatic classification, also called semantic segmentation, of data which allows further analysis of gathered data. Vast number of possible methods for automatic classification of point clouds have been proposed and implemented, many of which depend on machine learning. Motivation for this research is the need for pre-classified data for training of machine learning models, specifically the impact of label accuracy/error in the pre-classified data used for machine learning classification. To find out what is the impact of error levels of labels on machine learning classification of power line point clouds we have used the method of Classification and regression trees (CART) using Opals software. During this research several tests were conducted with various levels and types of error in class labelling of training data and the results were compared with correctly labelled data to calculate confusion matrices and thus evaluate the impact of different error levels.

Název v anglickém jazyce

Label errors in point cloud in training data for classification using machine learning

Popis výsledku anglicky

One of the applications of ULS (UAV-borne laser scanning) lies in power line inspection. However, with LiDAR data (i.e. point clouds) comes the need for reliable automatic classification, also called semantic segmentation, of data which allows further analysis of gathered data. Vast number of possible methods for automatic classification of point clouds have been proposed and implemented, many of which depend on machine learning. Motivation for this research is the need for pre-classified data for training of machine learning models, specifically the impact of label accuracy/error in the pre-classified data used for machine learning classification. To find out what is the impact of error levels of labels on machine learning classification of power line point clouds we have used the method of Classification and regression trees (CART) using Opals software. During this research several tests were conducted with various levels and types of error in class labelling of training data and the results were compared with correctly labelled data to calculate confusion matrices and thus evaluate the impact of different error levels.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Symposium GIS Ostrava 2020 Prostorová data pro Smart City a Smart Region

ISBN

978-80-248-4398-8

ISSN

1213-239X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

VYSOKÁ ŠKOLA BÁŇSKÁ-TECHNICKÁ UNIVERZITA OSTRAVA

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

18. 3. 2020

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

—