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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

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

ISBN

978-80-248-4398-8

ISSN

1213-239X

e-ISSN

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Number of pages

5

Pages from-to

1-5

Publisher name

VYSOKÁ ŠKOLA BÁŇSKÁ-TECHNICKÁ UNIVERZITA OSTRAVA

Place of publication

Ostrava

Event location

Ostrava

Event date

Mar 18, 2020

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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