Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F24%3A00560360" target="_blank" >RIV/60162694:G43__/24:00560360 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.hindawi.com/journals/amete/2023/6285569/" target="_blank" >https://www.hindawi.com/journals/amete/2023/6285569/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2023/6285569" target="_blank" >10.1155/2023/6285569</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Popis výsledku v původním jazyce

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.

Název v anglickém jazyce

Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Popis výsledku anglicky

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

ADVANCES IN METEOROLOGY

ISSN

1687-9309

e-ISSN

1687-9317

Svazek periodika

2023

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

6285569

Kód UT WoS článku

001057512100001

EID výsledku v databázi Scopus

2-s2.0-85170565349